EAI Endorsed Transactions on Energy Web

Energetic and exergetic study of a flat plate collector based solar water heater - investigation of the absorber size

Kumar Aditya Chandra — 2024-02-20

Small sized absorber in a flat plate solar collector is beneficial in terms of cost and minimum heat losses. However, its detailed thermal performance compared to standard size collector is still not fully understood. There is a paucity of research to appreciate thermal performance of solar water heating collector with consideration of a small absorber size (below 1m²) and a standard absorber size (2 m²). The present study attempts to investigate the energy and exergy efficiencies of flat plate solar water heating collector with two absorber plate areas (2 m² and 0.74 m²) to enumerate size of the absorber required for improved first and second law thermal efficiencies of the collector. The efficiencies of these two collector designs are experimentally compared with the help of a test facility available in the site for given operating temperatures and rate of flow. The combined experimental uncertainty due to the measuring instruments and the measured parameters is also ascertained. The obtained results highlight the significance of the larger absorber size (2m²) for higher thermal efficiency, and lower absorber size (0.74m²) for higher exergetic efficiency. The highest thermal efficiency obtained is 77.38% for larger absorber size, and the highest exergy efficiency of 13.21% is obtained for lower absorber size collector. It is demonstrated that larger and lower absorber size of the collector have higher thermal efficiency and higher exergy efficiency, respectively, than some of the published works.

Photovoltaic power generation prediction and optimization configuration model based on GPR and improved PSO algorithm

Zhennan Zhang — 2024-02-20

As the growing demand for energy as well as the strengthening of environmental awareness, photovoltaic power generation, as a clean and renewable energy source, has gradually attracted people's attention and attention. To facilitate the dispatching and planning of power system, this study uses historical data and meteorological data to build a photovoltaic power generation prediction and configuration optimization model on the ground of Gaussian process regression and improved particle swarm optimization algorithm. The simulation results show that the regression prediction curve of the Gaussian process regression prediction model is the closest to the real curve, and the prediction curve is stable and not easily disturbed by noise data. The Root-mean-square deviation and the average absolute proportional error of the model are small, and the disparity in the predicted value and the true value of the model is small; The integration of multi factor data has improved the accuracy of prediction data, and the regression prediction effect is good. The improved Particle swarm optimization algorithm could continuously enhance in the search for the optimal solution, and the Rate of convergence is fast. The Pareto solution can provide different solutions suitable for photovoltaic power generation optimization. Reasonable optimization configuration can effectively reduce active power line loss and voltage deviation, with the maximum reduction values reaching 132kW and 0.028, respectively. The research and design of predictive models and optimized configuration models can promote the formation of smart grids.

Input-Output Performance Evaluation of Science and Technology Enterprises Based on DEA Model

Guoliang Sun — 2024-02-20

INTRODUCTION: For enterprises, the results of performance evaluation will be affected by the selected input and output factors, so the indicators of the evaluation system should be representative and characteristic, and the evaluation method should be reasonable.

OBJECTIVES: According to the characteristics of science and technology industry, this paper establishes the performance evaluation index system, and the evaluation model of the input-output performance for the science and technology enterprises.

METHODS: With the evaluation model, this paper empirically analyses the evaluation index data of 30 science and technology enterprises in recent 3 years, and verifies the effectiveness of the selection of management performance evaluation index of science and technology enterprises.

RESULTS: Finally, according to the empirical analysis results of the evaluation model.

CONCLUSION: This paper puts forward some countermeasures to improve the business performance of science and technology enterprises.

Robust Control System for DFIG-Based WECS and Energy Storage in reel Wind Conditions

Chojaa Hamid — 2024-01-16

This research work focuses on addressing the challenges of controlling a wind energy conversion system (WECS) connected to the grid, particularly when faced with variable wind speed profiles. The system consists of a Doubly-Fed Induction Generator (DFIG) connected to the grid through an AC/DC/AC converter, along with a Li-ion battery storage system connected to the Back-to-Back converter DC link via a DC/DC converter. The non-linearity and internal parametric variation of the wind turbine can negatively impact energy production, battery charging performance, and battery lifespan. To overcome these issues, the study proposes a robust control approach called Integral action Sliding Mode Control (ISMC) to enhance the dynamic performance of the WECS based on DFIG. Additionally, the battery charging and discharging controllers play a crucial role in efficiently distributing power to the grid and storage unit based on the battery's state of charge, extracted energy, and power injected into the grid. Two current regulation modes, buck charging and boost discharging, are employed to ensure proper energy distribution. Furthermore, a storage system energy management algorithm is implemented to ensure battery safety during one of the charging modes. The effectiveness and robustness of the proposed control method were validated through simulations of a 1.5 MW wind power conversion system using Matlab/Simulink. The results confirmed the method's efficiency and efficacy.

Real-Time Co-Simulation for the Analysis of Cyber Attacks Impact on Distance Relay Backup Protection

Nadia Boumkheld — 2024-01-16

Smart Grid is a cyber-physical system that incorporates Information and Communication Technologies (ICT) into the physical power system, which introduces vulnerabilities to the grid and opens the door to cyber attacks. Wide area protection is one of the most important smart grid applications that aims at protecting the power system against faults and disturbances, which makes it an attractive target to cyber attacks, aiming at compromising the reliability of the power system. Understanding the interaction between the cyber and physical components of the smart grid and analyzing the damage that cyber-attacks can do to wide area protection is very important as it helps in developing effective mitigation approaches. This paper evaluates the impact of cyber attacks on a wide area distance relay backup protection scheme in real-time, through the development of a co-simulation platform based on Real Time Digital Simulator (RTDS) and network simulator 3 (NS3) and using the IEEE-14 bus power system model.

Optimization Design of Surface-mounted Permanent Magnet Synchronous Motors Using Genetic Algorithms

Trinh Truong Cong — 2024-01-16

The permanent magnet synchronous motor (PMSM) has gained widespread popularity in various industrial applications due to its simple structure, reliable performance, compact size, high efficiency, and adaptability to different shapes and sizes. Its exceptional characteristics have made it a focal point in industrial settings. The PMSM can be categorized into two primary types based on the arrangement of the permanent magnets (PM): interior permanent magnet (IPM) and surface-mounted permanent magnet (SPM). In the IPM, the magnets are embedded into the rotor, while in SPM, they are mounted on the rotor's surface. The utilization of PMs eliminates the need for excitation currents due to their high flux density and significant coercive force. This absence of excitation losses contributes to a notable increase in efficiency. In this study, a multi-objective optimal design approach is introduced for a surface mounted PMSM, aiming to achieve maximum efficiency while minimizing material costs. The optimization task is accomplished using a genetic algorithm. Furthermore, the motor designs are simulated using the finite element method (FEM) to assess and compare designs before and after the optimization process.

Comparison between LightGBM and other ML algorithms in PV fault classification

Paulo Monteiro — 2024-01-16

In this paper, the performance analysis of Machine Learning (ML) algorithms for fault analysis in photovoltaic (PV) plants, is given for different algorithms. To make the comparison more relevant, this study is made based on a real dataset. The goal was to use electric and environmental data from a PV system to provide a framework for analysing, comparing, and discussing five ML algorithms, such as: Multilayer Perceptron (MLP), Decision Tree (DT), K-Nearest Neighbors (KNN), Support Vector Machine (SVM) and Light Gradient Boosting Machine (LightGBM). The research findings suggest that an algorithm from the Gradient Boosting family called LightGBM can offer comparable or better performance in fault diagnosis for PV system.

A Snowmelt Optimization Algorithm Applied to Green Low Carbon Logistics Pathways Optimization Problems

Chunxia Zhai — 2024-01-18

INTRODUCTION: Efficient and accurate optimization of green and low-carbon logistics paths, as one of the key technologies of green and low-carbon logistics, can not only promote the high-quality development of the economy, but also reduce the negative impacts of logistics on the environment and increase the cost of logistics delivery.

OBJECTIVES: To address the problems of slow convergence and easy to fall into local optimization in the current performance prediction research on talent team building.

METHODS: This paper proposes a snowmelt heuristic optimization algorithm to solve the green low-carbon logistics path optimization problem. Firstly, the objective function of green low-carbon logistics path optimization is designed by analyzing the optimization cost and conditional constraints of the green low-carbon logistics path optimization problem; then, a method based on intelligent optimization algorithm is proposed by designing the position-order array coding and fitness function, combined with the snow-melting optimization algorithm; finally, the validity and superiority of the proposed method are verified by simulation experiments.

RESULTS: The results show that the proposed method not only improves the convergence speed but also increases the optimization fitness value.

Conclusion: The problem of slow convergence and easy to fall into local optimum in the solution of green low-carbon logistics path optimization problem is solved.

Using STAR-CCM+ Software in Aerodynamic Performance of Bogies under Crosswind Conditions

Yongrong Jin — 2024-01-18

INTRODUCTION: STAR-CCM+ is a CFD software that uses continuum mechanics numerical techniques and is a tool for thermodynamic and fluid dynamics analysis. STAR-CCM+has expanded the functions of surface treatment, such as surface wrapper, surface remesh, and volume mesh generation.With the increase of train speed, the aerodynamic phenomena become more prominent, and the aerodynamic phenomena of high-speed trains in crosswind environment become more complicated.

OBJECTIVES: Bogie is an important part of high-speed train. It is of great significance to study the aerodynamic performance Three groups of trains operating in a strong wind environment are modeled, and the surface pressure distribution characteristics of the car body and bogie, as well as the aerodynamic and aerodynamic torque distribution characteristics of each car and bogie, are analyzed when the train operates at 350km/h under a Class 12 crosswind condition.

RESULTS: The results of the study show the variation rules of surface pressure, aerodynamic force and aerodynamic moment of the car body and bogie with wind speed.

CONCLUSION: The windward surface pressure of the vehicle body increases linearly with the increase of wind speed, and the surface pressure of the roof and leeward side decreases linearly with the increase of wind speed.

The residual life prediction of power grid transformers based on GA-ELM computational model and digital twin data

Xiangshang Wang — 2024-04-04

As one of the core equipment of the power grid, the operation status of transformers directly affects the stability and reliability of the power system. To accurately evaluate the remaining life of power grid transformers, a genetic algorithm is applied to optimize the Extreme Learning Machine based on digital twin technology. Then, considering changes in load rate, a residual life prediction model for power grid transformers is constructed. From the results, the error of the research method was within 2℃, with a maximum error of only 1.76℃. The research model converged with a fitness value of 0.04 at 150 iterations. It showed good predictive performance for hot spot temperatures under different load rates, with an average accuracy of 99.97%. Compared with backpropagation models and extreme learning machine models, the research method improved accuracy by 2.85% and 1.01%, respectively, with small and stable prediction errors. It verified the superiority of the research model, indicating that the research method can improve the accuracy of predicting the remaining life for power grid transformers. By monitoring the operation status of transformers in real-time, potential faults can be detected in a timely manner. The maintenance and replacement can be carried out in advance to avoid power outages caused by equipment damage. In addition, the research can provide reference for the planning and design of power systems, and support the stability and reliability of power systems.

A novel concept of solar photovoltaic partial shading and thermal hybrid system for performance improvement

Usha S — 2024-01-26

Large values from external causes, such as partial shade, can greatly influence output power of PV. The applications of partial shading are frequently utilized in simulation software. However, in this research work, partial shading and the integration of the photovoltaic Thermal (PV/T) Hybrid Solar Panel is implemented, and analysis is done to see how it affects the output power of solar panels under genuine climatic circumstances. Many research investigations have been conducted and researchers continue to look at PV/T systems to enhance their performance. The application is designed to provide information on solar panel output power under normal and partial shading situations. The maximum amount of power that solar panels can generate is 298.50 W. Under typical circumstances, partial shading in a solar panel can result in a maximum power value of 141.13 W, and this partial shading leads the power to increase.

Investigation on ANFIS-GA controller for speed control of a BLDC fed hybrid source electric vehicle

P. Jagadish Babu — 2024-01-29

The BLDC (Brushless DC Motor) is utilized in electric vehicles, space missions, and mechanical applications. Neural Network Inference System reduces torque ripple for hybrid electric vehicle (PV-Battery) along with BLDC drive to achieve efficient speed performance and stability. A hybrid input source methodology is thus put forwarded to drive the stator currents giving exactly the expressed electromagnetic torque and counter-EMF harmonics. The torque and speed control technique are directed to neural network interference system, and H6 Voltage Source Inverter (H6 VSI) drives BLDC with a gate pulse signal. We examine how an ANFIS-GA torque controller may eliminate BLDC torque ripples under uninterrupted hybrid power supply in this work. MATLAB (Simulink) results show that Genetic Algorithm (GA) improves training of ANFIS better with varying set speed conditions. The ANFIS-GA controller outperforms challenging controllers under various BLDC motor driving load conditions, proving its efficiency.

Cold Chain Low-carbon Logistics Path Optimisation Method Based on Improved Hummingbird Optimisation Algorithm

Xuan Long — 2024-01-31

INTRODUCTION:The research of scientific and reasonable logistics and distribution programme time the pursuit of each logistics enterprise, not only can improve customer satisfaction and corporate image, but also help to reduce distribution costs.

OBJECTIVES: For the current cold chain low-carbon logistics distribution path optimisation methods there are problems such as easy to fall into the local optimum, optimisation time-consuming.

METHODS: This paper proposes a cold chain low-carbon logistics distribution path optimisation method based on the improved Hummingbird optimisation algorithm. Firstly, by analyzing the characteristics of the cold chain low-carbon logistics distribution path optimization problem, designing the cold chain low-carbon logistics path optimization objective function and constraints, and constructing a cold chain low-carbon logistics distribution path optimization model based on a soft time window; then, the hummingbird optimization algorithm is improved by using the initialization strategy of the set of good points and the cardinality leap strategy, to overcome the defects of the hummingbird optimization algorithm; secondly, a method based on intelligent optimization algorithm is proposed by designing the double-layer array coding and the adaptive function, combined with the improved hummingbird optimization algorithm. A cold chain low-carbon logistics path optimization method based on intelligent optimization algorithm is proposed; finally, the superiority and robustness of the proposed method are verified by simulation experimental analysis.

RESULTS: The results show that the proposed method not only improves the optimisation time, but also increases the optimisation fitness value.

CONCLUSION: This paper solves the problem that the optimisation of the green low-carbon logistics path optimisation problem is time-consuming and prone to falling into local optimum.

Deep Cryogenic Temperature CMOS Circuit and System Design for Quantum Computing Applications

Jency Rubia J — 2024-02-01

Quantum computing is a fascinating and rapidly evolving field of technology that promises to revolutionize many areas of science, engineering, and society. The fundamental unit of quantum computing is the quantum bit that can exist in two or more states concurrently, as opposed to a classical bit that can only be either 0 or 1. Any subatomic element, including atoms, electrons, and photons, can be used to implement qubits. The chosen sub-atomic elements should have quantum mechanical properties. Most commonly, photons have been used to implement qubits. Qubits can be manipulated and read by applying external fields or pulses, such as lasers, magnets, or microwaves. Quantum computers are currently suffering from various complications such as size, operating temperature, coherence problems, entanglement, etc. The realization of quantum computing, a novel paradigm that uses quantum mechanical phenomena to do computations that are not possible with classical computers, is made possible, most crucially, by the need for a quantum processor and a quantum SOC. As a result, Cryo-CMOS technology can make it possible to integrate a Quantum system on a chip. Cryo-CMOS devices are electronic circuits that operate at cryogenic temperatures, usually below 77 K (−196 °C).

Design and analysis of battery management system in electric vehicle

M Parameswari — 2024-02-01

The usage of electric vehicles is gaining momentum in recent time’s thus providing support to the growth in sales of electric vehicles. The Battery management system is the most important aspect to ensure the smooth functioning of an electric vehicle. This research highlights some key statements on the background of electric vehicles. The increase in the overall growing importance of electric vehicles has also been explained in this work. Battery management system has an importance in the functioning of electric vehicles, thus presenting the key highlights of this article. The finding presents the importance of batteries and their type used in EVs. The simulation results of the Lithium battery cell – 1 RC, 2 RC equivalent circuit parameters such as charging current, terminal voltage, state of charge, and battery current have been simulated and analysed in Matlab. The future scope of BMS and its development has been discussed.

Design and Comparison of SEU Tolerant 10T Memory Cell for Radiation Environment Applications

P Mangayarkarasi — 2024-02-01

Single event upsets (SEUs), which are caused by radiation particles, have emerged as a significant concern in aircraft applications. Soft mistakes, which manifest as corruption of data in memory chips and circuit faults, are mostly produced by SEUs. The utilization of SEUs can have both advantageous and detrimental effects in some critical memory applications. Nevertheless, in adherence to design principles, Radiation-Hardening-By-Design (RHBD) methodologies have been employed to mitigate the impact of soft mistakes in memory. This study presents a novel memory cell design, referred to as a Robust 10T memory cell, which aims to improve dependability in the context of aerospace radiation environments. The proposed design has several advantages, including reduced area, low power consumption, good stability, and a decreased number of transistors. Simulations were conducted using the TSMC 65nm CMO technology, employing the Tanner tool. The parameters of the RHB 10T cell were measured and afterwards compared to those of the 12T memory cell. The findings obtained from the simulation demonstrate that the performance of the 10T memory cell surpasses that of the 12T memory cell.

Energy Management System of Luminosity Controlled Smart City Using IoT

R Sathesh Raaj — 2024-02-05

INTRODUCTION: With the escalating rates of urbanization, there is a pressing need for enhanced urban services. The concept of smart cities, leveraging digital technologies, offers a promising solution to elevate urban living. The integration of Internet-of-Things (IoT) in urban infrastructure, particularly on highways, opens avenues for novel services and cross-domain applications through Information and Communication Technologies. However, the efficient functioning of an IoT-enabled smart city necessitates careful energy resource management.

OBJECTIVES: Propose a Highway Lighting System (HWLS) integrating IoT technologies to enhance urban services, focusing on significant energy savings and real-time environmental parameter monitoring.

METHODS: To achieve the objective of enhancing urban services through the proposed Highway Lighting System (HWLS), the system was designed and implemented by integrating cutting-edge sensors, communication links, and the Blynk IoT app. The deployment involved incorporating IoT technologies for real-time monitoring of air quality, air moisture, and soil moisture, alongside a fault identification system using GSM and GPS modules.

RESULTS: The proposed HWLS demonstrates significant energy savings, consuming only 37.6% of the original power consumption. The incorporation of IoT technologies facilitates real-time monitoring of environmental parameters, enabling informed decision-making for urban service optimization. The fault-finding system, utilizing GSM and GPS modules, enhances the reliability of the lighting system.

CONCLUSION: In conclusion, the Highway Lighting System (HWLS) represents a novel approach to smart city infrastructure, particularly in the context of urban lighting. The integration of IoT technologies not only contributes to energy savings but also enhances the overall efficiency of urban services. The proposed system's ability to monitor environmental parameters and identify faults demonstrates its potential for sustainable urban development and improved quality of life.

Modelling and Simulation of Grid Connected Wind Turbine Induction Generator for Windfarm

A Rathinavel — 2024-02-06

As the power generation sector moving towards the sustainability to achieve clean and renewable energy source, the wind power generation plays a vital role due to its abundance in nature. A big chunk of the decrease in carbon emission is a major attribute to the growth of the wind energy sector. Wind turbine production, structural development, logistics, maintenance and R&D are just some of the areas that could benefit from the growth of the wind energy industry. This brought out the attention of researchers of the electrical engineering to focus on wind power generation. It can be more efficient and cost-effective to operate wind turbines as a wind farm rather than individually. This has led to a surge in the construction of wind farms, both onshore and offshore wind farms. Therefore, in this paper, the study and analyse of single Induction generator with wind turbines and 33MW windfarm performance is presented. The simulation result demonstrates the efficiency of DFIG in producing energy at a constant wind speed, as well as its ability to regulate both active and reactive power at steady-state.

Optimization of Core Loss for Power Transformer Using Taguchi Method

Geetha A — 2024-02-06

This article focuses on the optimization of process parameters such as core area, core material and voltage for the design of power transformer. It employs Taguchi orthogonal array technique for designing the experiments and its analysis. Utility transformers are usually specified with the losses associated at design stage. The area of the core cross-section applied voltage, as well as the core material all has impact core loss deterioration. The impact of such variables influencing core loss is investigated by executing the model. A small proportion of core as well as the coil assembly experiments is simulated using the Taguchi approach with the orthogonal array. In this study, the core as well as the coil assembly of an 8MVA, 33/11KV, 3 Phase Transformer is modelled in ANSYS MAXWELL software. MINITAB software is used to assess the program's anticipated core loss in order to discover the optimal arrangement for three control variables.

Design and Application of Global Energy Trade Cross Border E-commerce Optimization Model

Jianing Shan — 2024-02-21

INTRODUCTION: With the continuous advancement of global economic integration, cross-border energy trade activities internationally are becoming increasingly frequent.

OBJECTIVES: To improve the efficiency of energy trade, this study is devoted to exploring the optimization model of cross-border e-commerce in global energy trade. Introducing advanced information technology and e-commerce platforms aims to facilitate the digital transformation of energy trade, improve transaction efficiency, reduce costs, and promote sustainable energy development.

METHODS: This study adopts a comprehensive methodology, including a literature review, case analysis, and model construction. First, relevant literature on global energy trade and cross-border e-commerce was thoroughly studied to understand the current development status and existing problems. Second, the successful experiences and challenges of cross-border e-commerce in enhancing the efficiency of energy trade are summarized through the analysis of several international cases. Finally, a set of optimization models that comprehensively consider market demand, technical conditions, and policy environment are constructed to guide the development of cross-border e-commerce in global energy trade. RESULTS: The empirical analysis of the optimization model reveals that cross-border e-commerce has significant potential in international energy trade. The model can effectively improve transaction efficiency, reduce the risk of information asymmetry, and promote the balanced development of the global energy market. It is also observed that digital transformation significantly affects the promotion of sustainable energy, providing a more sustainable path for global energy transition.

CONCLUSION: By establishing a cross-border e-commerce optimization model for international energy trade, this study provides substantial theoretical and empirical support for promoting the digital transformation of energy trade. In the future, governments, enterprises, and international organizations can draw on the conclusions of this study to formulate relevant policies and strategies to promote global energy trade jointly toward a more efficient and sustainable development path.

Energy Market Prediction and Risk Assessment Based on China's Rural Collective Economy

Xiaohang Liu — 2024-02-21

INTRODUCTION: Energy, as a core element supporting the functioning of modern society, is vital to the development of the rural collective economy. With the upgrading of the agrarian industrial structure and the improvement of rural electrification levels, the energy demand gradually increases. Therefore, for China's rural collective economy, an in-depth study of the forecasting and risk assessment of the energy market has essential theoretical and practical value for scientific planning of resource allocation and improving energy utilization efficiency.

OBJECTIVES: This study aims to reveal the development trend and key influencing factors through an in-depth analysis of China's rural collective economy's energy market and to make scientific forecasts of the future development of the energy market. At the same time, through risk assessment, it proposes risk prevention and resolution countermeasures of the energy market for the rural collective economy to provide decision support for rural energy security and sustainable development.

METHODS: This study adopts a comprehensive analysis approach, combining historical data, policy literature analysis, and expert interviews. First, a comprehensive analytical framework is established by combing the development history of the rural collective economy energy market over the past few years. Second, quantitative analysis models and numerical simulations are used to analyze the key factors affecting the energy market. Finally, expert interviews are conducted to obtain the views of experts in related fields on the future development and risks of the energy market to improve the research conclusions further.

RESULTS: The results of the study show that the energy market of China's rural collective economy will show a trend of gradual growth, but it also faces multiple risk challenges, including market price fluctuations, policy adjustments, and an imbalance between supply and demand. In the future, with the deepening of green energy policies, rural collective economies will pay more attention to the application of clean and renewable energy.

CONCLUSION: To summarize, this study provides a scientific reference for the energy strategy decision-making of rural collective economies by forecasting and assessing the risk of the energy market based on China's rural collaborative economies. In the future, it is necessary to pay more attention to the improvement of the policy system to promote the development of green energy, as well as the establishment of a sound market regulatory mechanism to reduce the uncertainty of the energy market and provide solid support for the sustainable development of the rural collective economy.

Key players in renewable energy and artificial intelligence research

Rolando Eslava-Zapata — 2024-02-22

INTRODUCTION: As countries work on the transition towards renewable energies that comply with the 2030 Agenda and the sustainable development goals, Artificial Intelligence is presented as a tool that is being adopted to promote the generation of renewable energies such as solar or wind power. , given the support it offers to automation, assisted decisions, and production efficiency.

OBJECTIVES: To analyze the key players in renewable energy and artificial intelligence research.

METHODS: The Scopus database is used to obtain the scientific articles for the period 2013-2023, and the Visualization of Similarities program (VOSviewer 1.6.18) is used for data processing and analysis.

RESULTS: An analysis of 822 articles shows that the countries with the highest scientific production are China (148), India (136) and the United States (81). In this regard, it is clear that there is significant collaboration between countries. With regard to the analysis of Co-occurrence - Author Keywords, three clusters are generated. The first cluster, identified with the color red, is related to artificial intelligence management; the second cluster, identified with the color green, is related to artificial intelligence innovation; and the third cluster, identified with the color blue, is related to energy models.

CONCLUSION: Researchers are facing new challenges every day to respond to the irruption of the use of new algorithms in the generation of renewable energies, given the range of available tools such as deep learning or neural networks. Research results have revealed that in recent years, scientific production has understood that AI is not a trend but rather a challenge facing society, industry, countries, or education in order to achieve sustainable development.

Real-time investigation of dust collection effects on solar PV panel efficiency

Gowtham Vedulla — 2024-02-23

The amount of the light distraction on the PV is made by the accumulation of particles of dust which in turn decreases efficient performance as well as leads to a reduction of money flow for the investors. More studies and tests were carried out inside the laboratories that cannot find a proper solution to mitigate the same. This study can enable the proper cleaning schedules of the PV panels as this work is being carried out on a real-time basis on the rooftops. The measurement of required parameters like irradiation, output power from the panels, and the amount of dust particles accumulated was done on an hourly, monthly, and yearly basis. It is found that nearly 8% of the performance could be dropped annually. For making a sustained operation of the PV panels it is required to have a cleaning process for 45 days intervals, especially for small-scale systems.

Performance Investigation of HFR Full-bridge Inverter in Resonant Inductive Coupled Power Transfer System for an Electric Vehicle

P Geetha — 2024-02-28

Inductive WPT of the resonant category is generally employed for medium and high-power transmission applications like electric vehicle charging due to it contributes excellent efficiency. The high-frequency resonant full-bridge inverter using series-series resonant topology is proposed. The design of the high-frequency resonant inverter is simulated and verified by MATLAB/SIMULINK software. The charging scheme which is available in the AC-DC as well as in the DC-DC converter should operate with the two steps to achieve a duty cycle-based voltage control and hysteresis current control. The resonant frequency of the proposed system has a frequency range of 65 kHz with a DC voltage of 12V. The simulation has been carried out successfully and transmitted a 5KW power load of constant current and voltage control. The Performance chart of with the existing method is carried out in terms of parameters and the efficiency can be achieved by 95%.

OPC UA Application Study in Oil and Gas Pipeline Network Monitoring Data Forwarding

Bingqiang Mao — 2024-03-19

INTRODUCTION： With the continuous development of oil and gas pipeline network monitoring and control technology, the need for data transmission and communication is becoming more and more prominent. In this context, OPC UA has attracted wide attention. This study aims to explore the application of OPC UA in data forwarding for oil and gas pipeline network monitoring in order to improve the efficiency, reliability and security of data transmission.

PURPOSE: The purpose of this study is to evaluate the applicability of OPC UA in oil and gas pipeline network monitoring and to verify its performance in data forwarding through empirical studies. By gaining an in-depth understanding of the characteristics of OPC UA, it aims to provide a more advanced and efficient monitoring data transfer solution for the oil and gas industry.

METHOD: The study adopts a combination of field monitoring and laboratory simulation. First, the essential characteristics and requirements of monitoring data in oil and gas pipeline networks were collected. Subsequently, a monitoring system with OPC UA as the communication protocol was established and field tested. In the laboratory environment, data transmission scenarios under different working conditions were simulated, and the performance of OPC UA under different conditions was analyzed.

RESULT: The field monitoring results show that the data transmission efficiency is significantly improved by using OPC UA as the communication protocol for data forwarding in oil and gas pipeline network monitoring. Meanwhile, the system performs well in different environments with high reliability and security. The laboratory simulation results further verify the stability and adaptability of OPC UA under complex working conditions.

CONCLUSION: OPC UA is an effective communication protocol that can meet the data transmission requirements for oil and gas pipeline network monitoring. Its efficient, reliable, and secure characteristics make it an ideal choice for improving the communication performance of monitoring systems in the oil and gas industry. The empirical results of this study provide reliable technical support for the oil and gas industry in the field of data transmission and a vital reference for the optimization and upgrading of monitoring systems in the future.

New Approach to SCADA System Screen Configuration Based on the Model of Oil and Gas Pipeline Network

He Huang — 2024-03-19

INTRODUCTION： With the continuous progress of science and technology, the monitoring and control of oil and gas pipeline networks have become more and more critical; SCADA systems, as a kind of technology widely used in industrial control, play a key role. The screen configuration of the SCADA system is the core part of its user interface, which is directly related to the operator's mastery of the status of the pipeline network. In order to improve the monitoring efficiency and reduce the operation risk, this study is devoted to exploring a new method of SCADA system screen configuration based on the oil and gas pipeline network model.

PURPOSE: The purpose of this study is to develop an innovative SCADA system screen configuration method to present the operating status of the oil and gas pipeline network more intuitively and efficiently. The design based on the pipeline network model aims to enhance the operators' understanding of essential information, such as pipeline network topology, fluid flow, etc., so as to make monitoring and control more intelligent.

METHODS: The study adopts a new method of SCADA system screen configuration based on the oil and gas pipeline network model. First, the topology, sensor data, and control nodes of the oil and gas pipeline network are comprehensively modelled. Then, through the design principle of human-computer interaction, the modelling results are integrated into the screen configuration of the SCADA system to realize the intuitive presentation of information. At the same time, advanced visualization technology is introduced so that the operators can understand the real-time changes in the pipe network status more clearly.

RESULTS: After experimental verification, the new method shows significant advantages in oil and gas pipeline network monitoring. The operators can recognize the abnormalities of the pipeline network more quickly and accurately through the SCADA system screen configuration, which improves the efficiency of troubleshooting and treatment. The visualized interface design makes the operation more intuitive and reduces the possibility of operating errors, thus improving the safety and reliability of the pipeline network.

CONCLUSION: The new method of SCADA system screen configuration based on the oil and gas pipeline network model has achieved significant results in improving monitoring efficiency and reducing operational risks. Through a more intuitive and intelligent interface design, operators can have a more comprehensive understanding of the operating status of the pipeline network, which provides practical support for rapid response and decision-making. This approach introduces new ideas to the field of oil and gas pipeline network monitoring, which is of positive significance for improving the overall performance of the system. Future work can be carried out to optimize the interface design further and expand the applicable scenarios.

Based on 3D Virtual Reconstruction of Modern City Landscape Sculpture Planning Design

Xin Xu — 2024-03-15

INTRODUCTION: With the continuous advancement of urbanization, urban landscape sculpture plays an increasingly important role in modern urban planning. Traditional planning and design methods make it challenging to demonstrate the three-dimensional sense and artistry of sculpture fully; therefore, this study explores a new method of planning and designing modern urban landscape sculpture based on three-dimensional virtual reconstruction.

OBJECTIVES: This study aims to enhance the three-dimensional sense and artistry of urban landscape sculpture planning and design through three-dimensional virtual reconstruction technology to meet the needs of modern urban development better. By using advanced technical means, the planning and design can be made more intuitive and specific and provide urban residents with a more artistic public space.

METHODS: The study adopts advanced three-dimensional virtual reconstruction technology, combined with urban planning and design theory, to plan and design modern urban landscape sculpture. Firstly, relevant literature on urban planning and sculpture design is collected to understand the existing design concepts and technical means. Secondly, a detailed virtual reconstruction of the sculpture is carried out by using three-dimensional modeling software to show the three-dimensional effect of the sculpture. Finally, the design scheme is optimized and improved through fieldwork and expert review.

RESULTS: Through three-dimensional virtual reconstruction technology, this study successfully shows the whole picture of modern urban landscape sculpture. The design scheme not only has a three-dimensional sense but it has also been improved in artistry. The results of fieldwork and expert evaluation show that the new design scheme is more in line with the needs of urban development and adds a unique artistic atmosphere to the urban space.

CONCLUSION: This study has achieved positive results in the field of modern urban landscape sculpture planning and design through 3D virtual reconstruction technology. The new design method not only provides a more specific tool for urban planners but also creates a more creative and artistic public space for urban residents. In the future, the application of this method in different urban contexts can be further explored and expanded to inject more innovation and vitality into urban planning and sculpture design.

Analysis of Energy International E-commerce Innovation Strategy Based on Global Value Chain

Qing Bai — 2024-03-18

INTRODUCTION: With the drive of globalization and digitalization, the global energy industry is undergoing a brand new transformation. Energy international e-commerce is an emerging paradigm that continues to grow within the global value chain framework, bringing significant changes and opportunities to the energy sector.

OBJECTIVES: This research examines the role of international e-commerce in advancing the energy industry's growth, maximizing the distribution of resources worldwide, and boosting market competitiveness. It does this by analyzing the innovation strategy of the sector based on the global value chain.

METHODS: The basic concepts and characteristics of global value chain theory and energy international e-commerce are analyzed, and then the innovation strategies in technological innovation, international cooperation, supply chain optimization, and data-driven are explored in depth, and empirical analyses of these strategies are conducted through case studies.

RESULTS: It is found that technological innovation not only promotes the development of international energy e-commerce but also gives rise to new business models; international cooperation and supply chain optimization effectively optimize the global resource allocation and market layout; and data-driven market expansion strategy improves the market competitiveness of enterprises. The case study results further validate the effectiveness and practicality of these strategies. CONCLUSION: Energy international e-commerce innovation strategies based on GVCs play an essential role in promoting the transformation and upgrading of the energy industry, optimizing resource allocation efficiency, and enhancing enterprises' market competitiveness.

Short-term Electricity Load Forecasting Based on Improved Seagull Algorithm Optimized Gated Recurrent Unit Neural Network

Mengfan Xu — 2024-04-15

INTRODUCTION: The complexity of the power network, changes in weather conditions, diverse geographical locations, and holiday activities comprehensively affect the normal operation of power loads. Power load changes have characteristics such as non stationarity, randomness, seasonality, and high volatility. Therefore, how to construct accurate short-term power load forecasting models has become the key to the normal operation and maintenance of power.

OBJECTIVES: Accurate short-term power load forecasting helps to arrange power consumption planning, optimize power usage and largely reduce power system losses and operating costs.

METHODS: A hybrid decomposition-optimization-integration load forecasting method is proposed to address the problems of low accuracy of current short-term power load forecasting methods.

RESULTS: The original power load time series is decomposed using the complete ensemble empirical modal decomposition method, while the correlation of power load influencing factors is analysed using Pearson correlation coefficients. The seagull optimisation algorithm is overcome to fall into local optimality by using the random adaptive non-linear adjustment strategy of manipulated variables and the differential variational Levy flight strategy, which improves the search efficiency of the algorithm. Then, the The gated cyclic unit hidden layer parameters are optimised by the improved seagull optimisation algorithm to construct a short-term electricity load forecasting model.The effectiveness of the proposed method is verified by simulation experimental analysis. The results show that the proposed method has improved the accuracy of the forecasting model.

CONCLUSION: The CEEMD method is used to decompose the original load time series, which improves the accuracy of the measurement model. The GRU prediction model based on improved SOA optimization not only has better prediction accuracy than other prediction models, but also consumes the least amount of time compared to other prediction models.

Optimization and privacy protection of microgrid power trading system based on attribute encryption technology

Kangqian Huang — 2024-03-15

This paper presents an effective technique and approach to deal with the dual challenges of performance optimization and privacy protection in microgrid power trading systems (MPTS) by utilizing attribute encryption technology. By embedding advanced cryptographic techniques into the operational substrate of microgrids, we introduce a novel approach to dramatically enhance the efficiency of energy distribution, while guaranteeing the privacy protection and integrity of participant data. The core objective of this technique is the application of attribute-based encryption (ABE), a method that offers fine-grained access control, ensuring sensitive information is made available only to eligible users based on their attributes, rather than their identities. In doing so, it meets the important requirement of securing data, without impairing the overall productivity of a power trading system. This paper presents a novel technique of ABE in the domain of MPTS, but also quantifies, through extensive theoretical analysis and simulations, how this integration leads to superior energy resource allocation and lower operational costs.

Research on Improvement Calculation Method of Grid Power Losses Based on New Energy Access Model

Jun Zhang — 2024-03-20

This research presents an improved calculation method for grid power losses, particularly focusing on the challenges posed by new energy access models. With the integration of electric vehicles and the rise of data centers, the demand for electrical energy has surged, leading to increased strain on grid stations and subsequent power losses. The proposed model aimed at reducing these power losses, while also examining existing systems to mitigate and analyze such issues. A significant contribution of this work is the application of the Random Forest machine learning algorithm, which enables efficient and accurate power flow calculations essential for optimizing grid performance. The proposed method is expected to enhance the grid’s ability to handle future energy demands and contribute to the sustainable development of electrical energy systems.

Research on Distributed Renewable Energy Power Measurement and Operation Control Based on Cloud-Edge Collaboration

Jingming Zhao — 2024-03-22

This paper examines how we can combine two big trends in solar energy: the spread of solar panels and wind turbines to renew the power grid, and cloud and edge computing technology to improve the way the grid works. Our study introduces a new strategy that is based on a means to exploit the power of cloud computing’s big data handling ability, together with the capacity of edge computing to provide real-time data processing and decision making. The method is designed to address major challenges in renewables systems making the system bigger and more reliable, and cutting the time delays in deciding how the system should respond. These are the kinds of changes that will be necessary so that we can blend solar and wind power into our current power grid, whether we are ready to say goodbye to coal or natural gas power. Our paper presents a way in which we believe that renewables systems can work more smoothly and effectively. This includes making it easier to measure how much power is being generated, to control these systems so that they function much like traditional power plants, and hence, to allow renewable energy to be part of a reliable and efficient part of our electricity supply. These are all crucial steps in using technology to make more of the green power from the sun – which we must do for our energy usage to be more earth friendly.

Research on Power Load Data Acquisition and Integrated Transmission Systems in Electric Energy Calculation and Detection

Sanlei Dang — 2024-03-22

This paper presents the crucial area of power load data acquisition with an integrated transmission system for precise calculation and detection of electric energy. With the advances in technology, management and optimization of energy has become critical for sustainability and economic reasons. Thus, we have targeted the cutting-edge methods for data gathering of power load along with its efficient transmission previously reviewed. We scrutinized the current methods and technologies used in power load data acquisition and identified their limitations along with areas of improvements. We followed advanced sensors and measuring devices for data collection employed an integrated transmission system with up-to-the-minute communication protocols and data processing algorithms. These were experimentally verified to improve the accuracy and reliability of the electric energy calculations. The real-world case studies were included for its practical implementations to provide an insight into its impacts. The results of this study provide a maturing outlook along with valuable analysis for electric energy calculation and detection. The system due to its potential for enhancing the energy management and efficiency can have a real-life and profound significance in sustainable and economic handling of the increasing load of energy.

Construction and Research on Cloud-edge Collaborative Power Measurement and Security Model

Jiajia Huang — 2024-03-22

Accurate power consumption assessment is of critical importance in the fast-evolving world of cloud and edge computing. These technologies enable rapid data processing and storage but they also require huge amounts of energy. This energy requirement directly impacts operational costs, as well as environmental responsibility. We are conducting research to develop a specialized cloud-edge power measurement and security model. This model delivers reliable power usage data from these systems while maintaining security for the data they process and store. A combination of simulation-based analysis and real-world experimentation helped us to deliver these results. Monte Carlo based simulations produced power usage predictions under various conditions and Load Testing validated their real-world performance. A Threat Modeling-based security study identified potential vulnerabilities and suggested protection protocols. A collaborative approach enhances power measurements accuracy and encourages secure operation of the combined cloud-edge systems. By fusing these metrics, a more efficient and secure operation of computing resources becomes possible. This research underscores the critical importance of developing advanced techniques for power metering and security in cloud-edge computing systems. Future research may focus on both expanding the model’s use to an array of larger, more complex networks, as well as the inclusion of AI driven predictive analytics to amplify accuracy of power management.

Prognostication of Weather Patterns using Meteorological Data and ML Techniques

Saksham Mathur — 2024-04-05

In the field of modern weather prediction, the accurate classification is essential, impacting critical sectors such as agriculture, aviation, and water resource management. This research presents a weather forecasting model employing two influential classifiers random forest and technique based on gradient boosting, both implemented using the Scikit-learn library. Evaluation is based on key metrics including F1 score, accuracy, recall, and precision, with Gradient Boosting emerging as the superior choice for precipitation prediction. The study examines the performance of Random Forest Regression, Gradient Boosting Regression, and Radial Basis Function Neural Network in forecasting precipitation, drawing on prior research that demonstrated the superiority of the Random Forest algorithm in terms of accuracy and speed. Ensemble methods, particularly the Voting Classifier, a fusion of Random Forest and Gradient Boosting, outperform individual models, offering a promising avenue for advancing weather classification.

Dynamic Simulation and Tradeoffs and Synergies of Ecosystem Service Value in Metropolitan Suburbs Using the PLUS Model

Chaoyu Zhang — 2024-04-05

INTRODUCTION: Due to rapid economic development and continuous human activities, land use changes in the suburbs of metropolitan areas are drastic, which in turn affects the balance of ecosystem functions. Analyzing and predicting the ecological service value characteristics and trade-offs in rapidly urbanizing regions is of great significance for promoting high-quality regional development.

AIM: This study attempts to reveal the trade-off and synergistic characteristics between the internal values of ecosystem services in suburban metropolitan areas under the influence of rapid urbanization.

METHODS: Based on the patch-generating land use simulation (PLUS) model, simulated the land use changes in Xinzheng City under multiple scenarios in 2030, combined with methods such as equivalent factor method and spatial autocorrelation analysis，estimating, and predicting the ecosystem service value and its trade-off synergy relationship in Xinzheng City from 1980 to 2030.

RESULTS: The value of ecosystem services in Xinzheng City continues to decline, hydrological regulation and soil conservation are the most important ecosystem service functions, under the scenario of farmland protection, ESV shows a stable growth trend. The synergistic relationship between various functions of ESV is significant, the Shizu Mountain National Forest Park, Shuangji River and other high agglomeration areas, as well as the Airport Economic Zone and Nanlonghu Town and other low agglomeration areas, all show a synergistic relationship, with only a portion of the southern side of the main urban area of Xinzheng showing a balancing relationship.

CONCLUSIONS: Our findings can scientifically identify the environmental advantages of ecological sustainable development in Xinzheng City, and transform them into development advantages, providing provide strong technical support for the spatial ecological restoration and ecological security pattern construction of metropolitan suburbs.

Path Planning of Self-driving Vehicles Combining Ant Colony and DWA Algorithms in Complex Dense Obstacles

Jing Niu — 2024-04-05

INTRODUCTION: To solve the problems of low quality and weak global optimization of the DWA algorithm, especially the problems of unreasonable path planning and the inability to give consideration to speed and driving safety in the process of vehicles passing through dense obstacles, this paper proposed an improved DWA algorithm based on ant colony algorithm.

OBJECTIVES: The traffic capacity and computing efficiency of Self-driving Vehicles in complex dense obstacles can be greatly improved.

METHODS: Through the obstacle density and distance information obtained by high-precision sensors on the vehicle, the speed objective function is updating in real time by using ant colony algorithm. And the maneuverability and safety performance of vehicles passing through are considering by the way.

RESULTS: The experimental results show that this method can obviously improve the vehicle's traveling ability and uneven path planning in the case of dense obstacles, and the number of iterations of the algorithm is reduced by more than 16%.

CONCLUSION: The improved DWA algorithm integrated with the ant colony algorithm can effectively improve the operating efficiency of the algorithm, reduce the distance the car must go around outside the obstacles, and improve Car driving safety. The effectiveness and universality of the improved DWA algorithm were verified through experiments.

Fault diagnosis of gearboxin wind turbine based on EMD-DCGAN

Guangyi Meng — 2024-04-05

INTRODUCTION: Wind turbine gearbox fault diagnosis is of great significance for the safe and stable operation of wind turbines. The accuracy of wind turbine gearbox fault diagnosis can be effectively improved by using complete wind turbine gearbox fault data and efficient fault diagnosis algorithms.A wind turbine gearbox fault diagnosis method based on EMD-DCGAN method is proposed in this paper.

OBJECTIVES: It can solve the problem when the sensor fails or the data transmission fails, it will lead to errors in the wind turbine gearbox fault data, which in turn will lead to a decrease in the wind turbine gearbox fault diagnosis accuracy.

METHODS: Firstly, the outliers in the sample data need to be detected and removed. In this paper, the EMD method is used to eliminate outliers in the wind turbine gearbox fault data samples with the aim of enhancing the true continuity of the samples; secondly, in order to make up for the lack of missing samples, a data enhancement algorithm based on a GAN network is proposed in the paper, which is able to effectively perfect the missing items of the sample data; lastly, in order to improve the accuracy of wind turbine gearbox faults, a DCGAN neural network-based fault diagnosis method is proposed, which effectively combines the data dimensionality reduction feature of deep learning method and the data enhancement feature of generative adversarial network, and can improve the accuracy and speed of fault diagnosis.

RESULTS and CONCLUSIONS: The experimental results show that the proposed method can effectively identify wind turbine gearbox fault conditions, and verify the effectiveness of the algorithm under different sample data conditions.

Optimization design of heliostat field based on high-dimensional particle swarm and multiple population genetic algorithms

Yiwen Huang — 2024-04-05

INTRODUCTION: Tower-type heliostat field is a new type of energy conversion, which has the advantages of high energy efficiency, flexibility and sustainability and environmental friendliness.

OBJECTIVES: Through the research and improvement of the tower heliostat field to promote the development of solar energy utilization technology.

METHODS: In this paper, we calculate and optimize the tower heliostat field by using single objective optimization, high-dimensional particle swarm algorithm and multiple group genetic algorithm.

RESULTS: In this case of question setting, average annual optical efficiency is 0.6696; average annual cosine efficiency is 0.7564; annual average shadow occlusion efficiency is 0.9766; average annual truncation efficiency is 0.9975; average annual output thermal power is 35539.1747W; mean annual output thermal power per unit area is 0.5657W.The optimal solution after the initial optimization of the algorithm is that the total number of mirror fields is 6,384 pieces, and the average annual output power per unit area is 530.6W.

CONCLUSION: The model of this paper can reasonably solve the problem and has strong practicability and high efficiency, but high dimensional particle swarm algorithm due to easily get local optimal solution, so can introduce the chaotic mapping to increase the randomness of the search space, improve the global search ability of the algorithm.

Embedded Highway Health Maintenance System Based on Digital Twin Superposition Model

Bijun Lei — 2024-04-05

INTRODUCTION: The highway monitoring data acquisition technology develops quickly. Based on the traditional form of continuous monitoring, intelligent management system focuses on digital and wireless transmission. In the operation of highway maintenance system, each system is independent of each other, lacking of effective connection. Moreover, the level of continuous monitoring is obviously backward, which restricts the development of highway health monitoring. It is necessary to further study the level of integration to achieve the real-time tracking and the monitoring of highway’s healthy development.

OBJECTIVES: This paper presents a highway health maintenance system based on digital twin technology, which intends to provide a solution for efficient, stable and automatic data transmission of the highway operation and maintenance management.

METHODS: The output of the algorithm after the noise reduction effect is compared with the data containing the generated noise. The average number of nodes is set before running the algorithm to determine the actual length of the vertical position of the embedded sensor (calculating the position of two sensor nodes). The vertical length can be referred to the combined noise level formed and the combined test to determine the position. With the help of the overall data, it can be seen that the Kalman low-pass filtering algorithm can well describe the trend of the received signal and retain the key information in the received signal.

RESULTS: It proves that the algorithm in this paper has fast calculation speed and high efficiency, and the basic working principle is simple. Thus, it is a good data denoising solution.

CONCLUSION: The output in the paper ensures the data exchange and the update of the whole life cycle of highway, defines the digital twin entity model, and provides a reference for the establishment of information and data network.

Optimal Planning of User-side Scaled Distributed Generation Based on Stackelberg Game

Xiaoming Zhang — 2024-04-05

BACKGROUND: User-side distributed generation represented by distributed photovoltaic and distributed wind turbine has shown an expansion trend of decentralized construction and disordered access, which is difficult to satisfy the demand for large-scale exploitation and sustainable development of distributed generation under the low-carbon transformation vision of the power system.

OBJECTIVES: To address the interest conflict and operation security problems caused by scaled distributed generation accessing the distribution network, this paper proposes the optimal planning method of user-side scaled distributed generation based on the Stackelberg game.

METHODS: Firstly, a cluster planning and operation mode of distributed generation is established. Then, a prediction method for planning behavior of user-side distributed generation is proposed in order to predict whether users will adopt the self-build mode or the leasing site mode for distributed generation. Finally, in order to reveal the game relationship between the distribution network operator and the users in the allocation of distributed generation resources, a bi-level planning model for scaled distributed generation is established based on the Stackelberg game.

RESULTS: The simulation results show that the revenue of the distribution network operator under the gaming model increases by 10.15% and 16.88% compared to the models of all users self-build distributed generation and all users leasing distributed generation site, respectively, while at the same time, individual users also realize different degrees of revenue increase.

CONCLUSION: The case analysis validates the effectiveness of the proposed method in guiding the rational and efficient planning of user-side distributed generation.

Research on Land-Based Wind/Solar Power Station Site Deformation Monitoring Based on SBAS-InSAR Technology

Junke Guo — 2024-04-05

INTRODUCTION: In recent years, China has been building extensive wind/solar power stations. During the construction and operation of land-based wind/solar power stations, deformation monitoring is an important method to investigate the station stability.

OBJECTIVES: Therefore, this study uses Sentinel-1 data and time-series InSAR technology to monitor the deformation of photovoltaic and wind power stations in Qingyuan County.

METHODS: InSAR technology obtains deformation rate maps in the radar line of sight (LOS) direction for a wide area around the power station sites. Since wind/solar power stations are mainly located in natural environments with relatively dense vegetation coverage, this paper proposes a SBAS-InSAR method integrated with spatiotemporal filtering to accurately extract the time series deformation over a large area. Based on the statistical characteristic difference between the deformation and the atmospheric delay, spatiotemporal filterings are applied to remove the atmospheric delay from the InSAR derived deformation results.

RESULTS: The experimental results show that spatiotemporal filtering is an effective and fast method to remove atmospheric delay.

CONCLUSION: The integration of BSAS-InSAR with spatiotemporal filtering has great potential applications in the deformation monitoring of land-based wind/solar power station sites, which is critical for the construction and operation of land-based wind/solar power stations.

Research on optimal scheduling of microgrid based on improved quantum particle swarm optimization algorithm

Fengyi Liu — 2024-04-09

INTRODUCTION: With the large-scale integration of new energy into the grid, the safety and reliability of the power grid have been severely tested. The optimized configuration of micro power systems is a key element of intelligent power systems, playing a crucial role in reducing energy consumption and environmental pollution.

OBJECTIVES: a power grid optimization scheduling model is proposed that comprehensively considers the issues of power grid operating costs and environmental governance costs

METHODS: Using quantum particle swarm optimization method to optimize the objective function with the lowest system operating cost and the lowest environmental governance cost. In order to improve the search ability of the algorithm and eliminate the problem of easily getting stuck in local optima, the Levy flight strategy is introduced, and the variable weight method is used to update the particle factor to improve the optimization ability of the algorithm.

RESULTS: The simulation results show that the improved quantum particle swarm optimization algorithm has strong optimization ability, and the scheduling model proposed in this paper can achieve good scheduling results in different scheduling tasks.

CONCLUSION: （1）The improved particle swarm algorithm, in comparison to itspredecessor, boasts a greater degree of optimization accuracy, aswifter convergence rate, and the capability to avoid the algorithm'sdescent into the local optimal solution at a later stage of the process. （2）The proposed model can effectively reduce users’ electricity costs and environmental pollution, and promote the optimized operation of microgrids.

Study on the Influence of Land Use Change on Carbon Emissions Using System Modeling under the Framework of Dual Carbon Goals

Pingli Zhang — 2024-04-10

At the crucial period of addressing climate change, especially to the carbonization of land use change, it is vital that relevant actions are taken to enable two ambitious dual-carbon goals, namely, ensuring that carbon emissions peak before 2030 and achieving carbon neutrality before 2060. This research investigates the impacts of land use changes on carbon emissions using a novel approach that integrates Light Detection and Ranging (LiDAR) with Geographic Information System (GIS). This approach is innovative due to its high quality three-dimensional representation to quantified exact carbon stock and forest emissions occurring due to specific land-use change. Therefore, through actual LiDAR, this research helps demarcate the pattern emitting different land-use measures, including deforestation, urban programs, agricultural differences, and forest and land changes, over historical change records and verified carbonization formulas. Similar qualitative levels between LiDAR and GIS analysis help determine the varying degrees of carbonization occurring due to enhanced deforestation, urban additions, and agricultural contributions while reporting the possible procedural carbons acquired during reforestation and other measurements. The results helped clarify that the most distinct level of land utilization shows the least level of carbon sent into the air. Therefore, the implication is that strategic land use measures and better working conditions can curb carbon indications. These signals support land-use policy and preparedness goals in a low carbon level. This study creates valuable records for the land utilization and cartograph, created through the power of LiDAR and GIS analysis.

Investigation of Quantitative Assessment Techniques for Supply-Regulation Capability in Multi-Scenario New-Type Power Systems

Miao Liu — 2024-04-10

This paper offers an in-depth investigation into various quantitative assessment methods used to quantify the supply regulation capacity in new types of power systems under different conditions. As new forms of energy, including renewables, are increasingly becoming the predominant sources of power systems, the traditional systems are undergoing transformative modifications to efficiently address the issue of power generation and consumption fluctuations. In this regard, this paper proposes an original framework that combines advanced statistical methods and machine learning. The primary purpose of the framework is to identify the level of resilience and flexible adaptability of new power systems. The paper presents the results of the simulations and real-world applications of the proposed measurement methods in enhancing power supply reliability and efficiency in all conditions. The implications based on the results will be beneficial to policymakers and other specialists who are making decisions involving designing and optimizing modern power systems. Furthermore, the paper aims to contribute to the existing discussion by providing further insights into the effectiveness of the proposed methods of measurement.

Development of an Energy Planning Model Using Temporal Production Simulation and Enhanced NSGA-III

Xiaojun Li — 2024-04-10

This paper presents an innovative model of Energy Planning Model which allows navigating the complexities of modern energy systems. Our model utilizes a combination of Temporal Production Simulation and an Enhanced Non-Dominated Sorting Genetic Algorithm III to address the challenge associated with fluctuating energy demands and renewable sources integration. The model represents a significant advancement in energy planning due to its capacity to simulate energy production and consumption dynamics over time. The unique feature of the model is based on Temporal Production Simulation, meaning that the model is capable of accounting for hourly, daily, and seasonal fluctuations in energy supply and demand. Such temporal sensitivity is crucial for optimization in systems with high percentages of intermittent renewable sources, as existing planning solutions largely ignore such fluctuations. Another component of the model is the Enhanced NSGA-III algorithm that is uniquely tailored for the nature of multi-objective energy planning where one must balance their cost, environmental performance, and reliability. We have developed improvements to NSGAIII to enhance its efficiency when navigating the complex decision space associated with energy planning to reach faster convergence and to explore more optimal solutions. Methodologically, we use a combination of in-depth problem definition approach, advanced simulation, and algorithmic adjustments. We have validated our model against existing models and testing it in various scenarios to illustrate its superior ability to reach optimal energy plans based on efficiency, sustainability, and reliability under various conditions. Overall, through its unique incorporation of the Temporal Production Simulation and an improved optimization algorithm, the Energy Planning Model provides novel insights and practical decision support for policymakers and energy planners developed to reach the optimal sustainable solutions required for the high penetration of renewables.

Research Progress on Deep Learning Based Defect Detection Technology for Solar Panels

Yuxin Wang — 2024-04-11

INTRODUCTION: Based on machine vision technology to carry out photovoltaic panel defect detection technology research to solve the photovoltaic panel production line automation online defect detection and localization problems.

OBJECTIVES: The goal is to improve the accuracy of defect detection on PV cell production lines, increase the speed of defect detection to meet real-time monitoring needs, and improve production efficiency.

METHODS: In this paper, three detection methods such as image processing based detection, traditional machine learning based detection and deep learning algorithm based detection are discussed and compared and analyzed respectively. Finally, it is concluded that deep learning based detection methods are more effective in comparison. Then, further analysis and simulation experiments are done by several deep learning based detection algorithms.

RESULTS: The experimental results show that the YOLOv8 algorithm has the highest precision rate and maintains good results in terms of recall and mAP values. The detection speed is all less than other algorithms, 10.6ms.

CONCLUSION: The inspection model based on yolov8 algorithm has the highest comprehensive performance and is the most suitable algorithmic model for detecting defects in solar panels in production lines.

Research on Surface Defect Detection Method of Photovoltaic Power Generation Panels——Comparative Analysis of Detecting Model Accuracy

Yunxin Wang — 2024-04-11

INTRODUCTION: Research on intelligent defect detection technology using machine vision was conducted to address the challenging problem of detecting and localizing PV defects in photovoltaic power generation system operation and maintenance.

OBJECTIVES: The aim is to improve the accuracy of PV defect detection and enhance the operation and maintenance efficiency of PV power plants.

METHODS: In this paper, three detection methods such as image processing based detection, traditional machine learning based detection, and deep learning algorithm based detection are discussed and compared, and analyzed respectively. It is finally concluded that the deep learning based detection is more efficient in comparison. Then further analysis and simulation experiments are done through several detection algorithms based on deep learning.

RESULTS: The experiment yields a high accuracy of the detection model based on the Faster-RCNN algorithm. Its mAP value reaches 92.6%. The detection model based on the YOLOv5 algorithm reaches a mAP value of 91.4%. But its speed is as much as 7 times faster than the model based on the Faster-RCNN algorithm.

CONCLUSION: Comprehensive speed and accuracy index. Combining the needs of PV defect detection in the operation and maintenance of PV power generation systems with the results of simulation experiments. It is concluded that the detection model based on the YOLOv5 algorithm can provide better detection capability. Modeling with this algorithm is more suitable for PV defect detection.

Analysis and Design of Wind Turbine Monitoring System Based on Edge Computing

Xiaoju Yin — 2024-04-11

INTRODUCTION: A wind turbine data analysis method based on the combination of Hadoop and edge computing is proposed.

OBJECTIVES: Solve the wind turbine health status monitoring system large data, time extension, energy consumption and other problems.

METHODS: By analysing the technical requirements and business processes of the system, the overall framework of the system was designed and a deep reinforcement learning algorithm based on big data was proposed.

RESULTS: It solves the problem of insufficient computing resources as well as energy consumption and latency problems occurring in the data analysis layer, solves the problems in WTG task offloading, and improves the computational offloading efficiency of the edge nodes to complete the collection, storage, and analysis of WTG data.

CONCLUSION: The data analysis and experimental simulation platform is built through Python, and the results show that the application of Hadoop and the edge computing offloading strategy based on the DDPG algorithm to the system improves the system's quality of service and computational performance, and the method is applicable to the distributed storage and analysis of the device in the massive monitoring data.

Image Recognition of Photovoltaic Cell Occlusion Based on Subpixel Matching

Yuexin Jin — 2024-04-12

INTRODUCTION: During the operation of large photovoltaic power stations, they are often shielded by dust and bird droppings, which greatly reduce the power generation and even cause fires. Analysis of PV cell occlusion image recognition accuracy based on sub-pixel matching.

OBJECTIVES: In order to find the location of the pv cells, we use the method of subpixel image matching. Improve recognition accuracy.

METHODS: When the power plant is running normally, taken the original image for photovoltaic power station as the original sample, and then using the subpixel gradient matching algorithm, to match the original image and find out that the minimum matching values.

RESULTS: If the calculation results is greater than a specified threshold, When the calculated result is greater than the specified threshold, the power station is considered abnormal.

CONCLUSION: The experimental process shows that this method can better judge the operating status of photovoltaic power station, and can find out the location of mismatched photovoltaic cells more accurately, and the calculation accuracy reaches sub-pixel level.

Characterization and Prediction of Wind Turbine Blade Damage Based on Fiber Grating Sensor

Xin Guan — 2024-04-12

INTRODUCTION: As a renewable and clean use of energy, wind power generation has a very important role in the new energy generation industry. For the many parts of various wind turbines, the safety and reliability of wind turbine blades are very important.

OBJECTIVES: The energy spectrum simulation algorithm included in the wavelet analysis method is used to simulate and analyzewind turbine blade damage, to verify the correctness and validity of wind turbine blade damage analysis.

METHODS: Matlab simulation is used to introduce the experiments related to the static and dynamic detection of fiber grating sensors, analyze the signal characteristics of the wind turbine blade when it is damaged by the impact, and provide a basis for the analysis of the external damage of large wind turbine blade.

RESULTS: The main results obtained in this paper are the following. By analyzing the decomposition of wavelet packets, the gradient change of wavelet impact energy spectrum before and after the wavelet damage was obtained and compared with the histogram, and the impact energy spectrum of each three-dimensional wavelet energy packet in the image was compared and analyzed, which can well realize the recognition of wavelet damage gradient for solid composite materials.

CONCLUSION: With the help of Matlab simulation to collect the impact response signal, using the wavelet packet energy spectrum method to analyze the signal, can derive the characteristics of wind turbine blade damage.

Maximum Power Point Tracking Control Method of Photovoltaic Cell under Shadow Influence

Yifeng Meng — 2024-04-12

In view of the poor effect of battery power tracking control in the current solar power generation system, the maximum power point tracking (MPPT) control method of photovoltaic cell under the influence of shadow is proposed. The MPPT control method of photovoltaic cell is optimized by using the influence of shadow, the structural characteristics of photovoltaic cell are optimized, and the voltage rise and fall DC / DC conversion circuit is adopted, The maximum power identification algorithm of photovoltaic cells is set, and the voltage disturbance method is used to realize the MPPT, so that the solar photovoltaic cells always maintain the maximum power output, so as to ensure the control effect. Finally, the experiment shows that the MPPT control method of photovoltaic cells has high practicability and fully meets the research requirements.

Design and Implementation of an SGX Based Electricity Information Collection and Management System

Yao Song — 2024-04-12

With the rapid growth of the number and scale of smart grid users, traditional data encryption transmission methods can no longer meet the performance requirements of data aggregation. In response, a power consumption information collection and management system based on SGX software protection extension is proposed. The system mainly consists of three parts: user electricity data acquisition terminal, SGX data security processing and distributed storage module on the chain, and data monitoring management display platform. The user electricity data collection terminal collects electricity data from various buildings, residences, rooms, and other smart meters, analyzes and uploads it. After calling the trusted function of SGX technology, it enters the security zone provided by SGX for data processing. Finally, the data security processing results and data are uploaded to the blockchain for storage. In order to visually display user electricity usage data, an intelligent monitoring platform for user electricity collection and management has been established. This system can reduce the workload of user electricity data collection, ensure the accuracy of data collection, and provide an efficient and highly reliable system platform for user electricity data management.

Research on Wind Power Prediction Model Based on Random Forest and SVR

Zehui Wang — 2024-04-12

Wind power generation is random and easily affected by external factors. In order to construct an effective prediction model based on wind power generation, a wind power prediction model based on principal component analysis (PCA) noise reduction, feature selection based on random forest model and support vector regression (SVR) algorithm is proposed. First, in the data preprocessing stage, PCA is used for sample data denoising; then the random forest model is used to calculate the importance evaluation value of each feature to optimize the selection of feature parameters; finally, The SVR algorithm is applied for training and prediction. Experiments show that the prediction effect of the model based on random forest and SVR is excellent, the root mean square error(RMSE) is 0.086, the average absolute percentage error(MAPE) is 23.47%, and the coefficient of determination(R2) is 0.991. Compared with the traditional SVR model, the root mean square error of the method proposed in this paper is reduced by 95.9%, and the prediction accuracy and the fit of the prediction curve are significantly improved.

An Ultra-Short-Term Wind Power Prediction Method Based on Quadratic Decomposition and Multi-Objective Optimization

Hayou Chen — 2024-04-15

To augment the accuracy, stability, and qualification rate of wind power prediction, thereby fostering the secure and economical operation of wind farms, a method predicated on quadratic decomposition and multi-objective optimization for ultra-short-term wind power prediction is proposed. Initially, the original wind power signal is decomposed using a quadratic decomposition method constituted by the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Fuzzy Entropy (FE), and Symplectic Geometry Mode Decomposition (SGMD), thereby mitigating the randomness and volatility of the original signal. Subsequently, the decomposed signal components are introduced into the Deep Bidirectional Long Short-Term Memory (DBiLSTM) neural network for time series modeling, and the Sand Cat Swarm Optimization Algorithm (SCSO) is employed to optimize the network hyperparameters, thereby enhancing the network’s predictive performance. Ultimately, a multi-objective optimization loss that accommodates accuracy, stability, and grid compliance is proposed to guide network training. Experimental results reveal that the employed quadratic decomposition method and the proposed multi-objective optimization loss can effectively bolster the model’s predictive performance. Compared to other classical methods, the proposed method achieves optimal results across different seasons, thereby demonstrating robust practicality.

Research on Establishment and Application of Evaluation System of Urban Energy Strategy Development Indicators under the Perspective of Carbon Neutrality

Chenyu Chen — 2024-04-15

A scientific, comprehensive and integrated assessment of urban energy development is of great significance for the establishment of a clean, low-carbon and efficient urban modern energy system. From the perspective of carbon neutrality, this paper sets 25 evaluation indicators in seven dimensions: energy supply, energy consumption, energy efficiency improvement, clean and low-carbon, safety and reliability, low-carbon transport, and scientific and technological innovation, and constructs a secondary indicator system for evaluating the strategic development of urban energy. The system adopts the hierarchical analysis method to determine the weights of the indicators, the double-baseline progression method to standardize the indicator scores, and finally the weighted composite index method to calculate the level of urban energy strategy development. This paper applies the index system to evaluate the current energy development status of Wenzhou city in 2020 and 2022, and to predict the energy strategy development in 2025 and 2030. The scores of Wenzhou city's urban energy strategy development level in the corresponding four periods are 63.56, 70.59, 77.87 and 85.06, indicating that by 2023, Wenzhou city's urban energy development level will go from medium development to high development. Wenzhou City should accelerate the proportion of renewable energy in the future. It is necessary to complement multiple energy sources and improve the integration of heat, electricity, gas and cold. In terms of end consumption, it is necessary to improve the efficiency of energy use, reduce energy intensity, implement electric energy substitution and form an energy consumption pattern centered on electricity.

Multi-temporal Scale Wind Power Forecasting Based on Lasso-CNN-LSTM-LightGBM

Qingzhong Gao — 2024-04-15

Due to the increasingly severe climate problems, wind energy has received widespread attention as the most abundant energy on Earth. However, due to the uncertainty of wind energy, a large amount of wind energy is wasted, so accurate wind power prediction can greatly improve the utilization of wind energy. To increase the forecast for wind energy accuracy across a range of time scales, this paper presents a multi-time scale wind power prediction by constructing an ICEEMDAN-CNN-LSTM-LightGBM model. Initially, feature selection is performed using Lasso regression to identify the most significant variables affecting the forecast for wind energy across distinct time intervals. Subsequently, the ICEEMDAN is utilized to break down the wind power data into various scales to capture its nonlinear and non-stationary characteristics. Following this, a deep learning model based on CNN and LSTM networks is developed, with the CNN responsible for extracting spatial features from the time series data, and the LSTM designed to capture the temporal relationships. Finally, the outputs of the deep learning model are fed into the LightGBM model to leverage its superior learning capabilities for the ultimate prediction of wind power. Simulation experiments demonstrate that the proposed ICEEMDAN-CNN-LSTM-LightGBM model achieves higher accuracy in multi-time scale wind power prediction, providing more reliable decision assistance with the management and operation of wind farms.

Multi-stage Multi-energy Flow Integrated Energy Systems of Electricity, Gas, and Heat Based on Heterogeneous Energy Flow Characteristics

Qinglong Gou — 2024-04-16

INTRODUCTION: The development of integrated energy systems (IES) is of paramount significance in addressing climate change and other challenges. Ensuring the rapid and accurate calculation of energy flow states is crucial for their efficient operation. However, the difference in response time of various heterogeneous energy flows in IES will lead to the inaccuracy of the steady-state model.

OBJECTIVES: This paper proposes a model for multi-stage multi-energy flow IES of electricity, gas, and heat based on heterogeneous energy flow characteristics.

Methods: IES was divided into fast variable networks and slow variable networks, and a multi-energy flow multi-stage model was established. Suitable models were matched for different subnets at different stages to improve the calculation accuracy.

RESULTS: Selected a practical Electrical-Gas-Heat IES as a case study for simulation. Through case studies, the effectiveness and accuracy of the proposed method are demonstrated.

CONCLUSION: The multi-stage model proposed in this paper can improve the accuracy of multi-energy flow in IES.

Study on Dynamic Response Characteristics of Offshore Floating Wind Turbine Pitch System

Xin Guan — 2024-04-16

Due to its special working conditions, offshore wind turbine will bear large direct and indirect loads under the combined action of air flow and wave flow. In this paper, a variable pitch system composed of variablepitch motorand variable pitch bearing is improved, and the characteristics of system's bending moment, torque, vibration and other physical quantities under the action of multiple physical loads are verified, and the mechanical response characteristics of floating wind turbine under the control of unified variable pitch and independent variable pitch are studied under the running conditions at sea. The results show that mechanical structure of uniform pitch is compared with that of independent pitch, the independent variable pitch structure can effectively reduce the mean oscillation value of wind turbine tower in the parallel direction of air flow by optimizing control strategy, and reduce the thrust at the hub of wind turbine and the bending moment at the root of tower, but increase the vibration frequency and fatigue load of offshore wind turbine tower along parallel direction of air flow. Reduce the fatigue life of equipment. The research results can be used as a reference to reduce the variable pitch control and vibration suppression of offshore wind turbines and improve the reliability of wind turbines.

Research on the Technical and Economic Development of Large Megawatt Wind Turbines Based on Medium-Voltage Electrical System

Wei Liu — 2024-04-16

INTRODUCTION: With the advent of the "bidding era" and "parity era" in the wind power market, the competition of the whole machine factory is becoming more and more fierce, and the capacity of the single fan is getting larger and larger, which becomes the key to the design of the fan electrical system of the large-capacity unit (5.XMW or more). At present, the low-voltage wind power system (690V,1140V) is the common solution for wind turbines. However, due to the limitation of the cable section of low-voltage electrical system and the increase of the rated current of the generator, the increase of the capacity of a single machine makes more cables from the generator side to the grid, and the cost also increases.

OBJECTIVES: Aiming at the future large megawatt wind power market, the medium voltage Doubly-Fed electrical system solution is proposed to increase the higher generation and electricity income of wind farms and reduce the manufacturing cost of wind farms.

METHODS: The technology and economy of medium voltage and low voltage electrical system are compared.

RESULTS: With the gradual increase of single capacity, the economy of medium pressure wind power generation system is getting better and better, and the higher the height of the tower, the better the economy. At the same time, the reduction of the rated current of the generator brings about the reduction of line loss and the increase of power generation. The number of cables is greatly reduced, and the construction cost and difficulty of cable laying will be greatly reduced.

CONCLUSION: In response to the technical trend of large-capacity wind turbines in the future, the medium-voltage wind power generation system has a good application prospect, both from the economic and technical point of view.

Study on the Economic and Technical Optimization of Hybrid Rural Microgrids Integrating Wind, Solar, Biogas, and Energy Storage with AC/DC Conversion

Hu Tan — 2024-04-16

Under the guidance of the 'dual carbon' goals and 'rural revitalization' strategy, the development of microgrids primarily based on wind, solar, and biogas energy is rapidly advancing in rural areas. A critical and challenging area of current research is how to optimally configure the capacity of these microgrids of varying sizes, taking into account the availability of resources in the system's environment and specific climatic conditions, to maximize economic benefits. Based on this, the article constructs a model of a hybrid AC/DC microgrid system powered by wind, solar, and biogas energy. It undertakes multi-objective optimization to achieve the highest utilization of renewable energy, the most economical cost, and the minimum carbon emissions while ensuring the reliability of the system's power supply. The study explores the economically and technically optimal configuration of this microgrid energy system under certain climatic conditions. The results indicate that the optimal configuration for a rural microgrid powered by wind, solar, and biogas energy should include a 2.6 kW biogas generator, 30.00 kW solar panels, 5.24 kW wind turbines, a 2.6 kW battery storage system, and a 10.00 kW bidirectional inverter. This configuration results in the lowest total net cost of the system, achieving optimal outcomes in terms of total net cost, cost per kilowatt-hour, and supply reliability.

Study on Reactive Power Optimization Including DSSC for New Energy Access to the Power Grid

Yuan Hu — 2024-04-16

The vigorous development of new energy has effectively reduced carbon emissions, but it has also brought fluctuating impacts on the carrying capacity of the power grid. In order to improve the voltage stability after integrating new energy sources and promote the scientific consumption of more new energy, this paper proposes the use of Distributed Static Synchronous Compensator (DSSC) devices for flexible and controllable voltage regulation in new energy integration. An improved particle swarm optimization algorithm is then developed to optimize the reactive power considering the regulation of DSSC. The paper conducts power flow calculations based on the DSSC power injection model and establishes a reactive power optimization mathematical model with objectives of minimizing active power loss, minimizing node voltage deviation, and maximizing voltage stability margin in the grid with new energy integration. The improved particle swarm optimization algorithm is utilized to achieve the reactive power optimization. Experimental simulations are conducted using the IEEE 33-node system to analyze the voltage improvement before and after adopting the improved particle swarm optimization algorithm considering the DSSC device in the grid with new energy integration. It is found that the proposed method effectively reduces active power loss and stabilizes voltage fluctuations, demonstrating its practical value.

Improvement of Efficiency of Inverters in Hydro Photovoltaic Power Station with Particle Swarm Optimization

Huijie Xue — 2024-04-16

In the sparsely populated areas without electricity, the hydro photovoltaic power station is a feasible solution for electricity supply. The strategy of distributing the power among the inverters is critical to the efficiency of them. The conventional distributing strategies result in low efficiency of the inverters. In order to improve the efficiency, this paper analysed the loss and efficiency characteristics of the inverter and expressed the power distributing problem as an optimal control problem minimizing the total loss for the inverters. The optimal control problem was solved with particle swarm optimization and the efficiency optimum power distribution strategies in three operation scenarios were obtained. The quantitative analysis method was adopted to evaluate the effect of the efficiency optimum power distribution strategies. The total efficiency of the inverters with the optimal strategies and the conventional strategies were calculated respectively. The optimal distribution strategies were compared quantitatively with conventional power distribution strategies on the basis of the efficiency. The results demonstrated the validity of the strategies obtained in this paper in improving the total efficiency of the inverters.

Carbon Emission Forecast Based on Multilayer Perceptron Network and STIRPAT Model

Ning Zhao — 2024-04-16

INTRODUCTION: It is of great research significance to explore whether China can achieve the "two-carbon target" on time. The MLP model combines nonlinear modeling principles with other techniques, possessing powerful adaptive learning capabilities, and providing a viable solution for carbon emission prediction.

OBJECTIVES: This study models and forecasts carbon emissions in Jiangsu Province, one of China's largest industrial provinces, aiming to forecast whether Jiangsu province will achieve the two-carbon target on time plan and provide feasible pathways and theoretical foundations for achieving dual carbon goals.

METHODS: Based on the analysis of the contributions of relevant indicators using the Grey Relational Analysis method, a comprehensive approach integrating the STIRPAT model, Logistic model, and ARIMA model is adopted. Ultimately, an MLP prediction model for carbon emission variations is established. Using this model, simulations are conducted to analyze the carbon emission levels in Jiangsu Province under different scenarios from 2021 to 2060.

RESULTS: The time to reach carbon peak and the likelihood of achieving carbon neutrality vary under three scenarios. Under the natural scenario of no human intervention, achieving carbon neutrality is not feasible. While under human-made intervention scenarios including baseline and intervention scenarios, Jiangsu Province is projected to achieve the carbon neutrality target as scheduled, attaining the peak carbon goal, however, proves challenging to realize by the year 2030.

CONCLUSION: The MLP model exhibits high accuracy in predicting carbon emissions. To expedite the realization of dual carbon goals, proactive government intervention is necessary.

Research on Predictive Control Energy Management Strategy for Composite Electric Ship Based on Power Forecasting

Haotian Chen — 2024-04-03

A proposed solution is presented to address the issue of rising energy loss resulting from inaccurate power prediction in the predictive energy management strategy for composite electric power electric ship. The solution involves the development of a power prediction model that integrates Archimedes' algorithm, optimized variational modal decomposition, and BiLSTM. Within the framework of Model Predictive Control, this predictive model is utilized for power forecasting, transforming the global optimization problem into one of optimizing the power output distribution among power sources within the predictive time domain, then the optimization objective is to minimize the energy loss of the composite electric power system, and a dynamic programming algorithm is employed to solve the optimization problem within the forecast time domain. The simulation findings demonstrate a significant enhancement in the forecast accuracy of the power prediction model introduced in this study, with a 52.61% improvement compared to the AOA-BiLSTM power prediction model. Concurrently, the energy management strategy utilizing the prediction model proposed in this research shows a 1.02% reduction in energy loss compared to the prediction model control strategy based on AOA-BiLSTM, and a 15.8% reduction in energy loss compared to the ruler-based strategy.

Opportunities, challenges and future perspectives of Geothermal Energy in Ethiopia: A Review

Tessafa Abrham Ashagrie — 2024-01-16

Ethiopia is among the East African regions with huge geothermal energy potential due to the presence of geologically active volcanic and hot spring-featured Rift Valley. However, geothermal energy is at its infant stage of utilization not only in Ethiopia but also in the continent of Africa and globally. Regionally, Kenya is the country with advanced extraction of it for electricity generation followed by Ethiopia in East Africa. Generation of electricity from this largely abundant energy resource has an enormous opportunity for societal and regional economic development though it is not an easy process due to the many challenges of the generation process. In this paper, geothermal energy in Ethiopia and the region, the opportunity, barriers with possible solutions, and future perspective are stated.

An Architecture and Review of Intelligence Based Traffic Control System for Smart Cities

Manasa Kommineni — 2024-01-29

City traffic congestion can be reduced with the help of adaptable traffic signal control system. The technique improves the efficiency of traffic operations on urban road networks by quickly adjusting the timing of signal values to account for seasonal variations and brief turns in traffic demand. This study looks into how adaptive signal control systems have evolved over time, their technical features, the state of adaptive control research today, and Control solutions for diverse traffic flows composed of linked and autonomous vehicles. This paper finally came to the conclusion that the ability of smart cities to generate vast volumes of information, Artificial Intelligence (AI) approaches that have recently been developed are of interest because they have the power to transform unstructured data into meaningful information to support decision-making (For instance, using current traffic information to adjust traffic lights based on actual traffic circumstances). It will demand a lot of processing power and is not easy to construct these AI applications. Unique computer hardware/technologies are required since some smart city applications require quick responses. In order to achieve the greatest energy savings and QoS, it focuses on the deployment of virtual machines in software-defined data centers. Review of the accuracy vs. latency trade-off for deep learning-based service decisions regarding offloading while providing the best QoS at the edge using compression techniques. During the past, computationally demanding tasks have been handled by cloud computing infrastructures. A promising computer infrastructure is already available and thanks to the new edge computing advancement, which is capable of meeting the needs of tomorrow's smart cities.

Flying Ad-Hoc Networks (FANETs): A Review

Tarandeep Kaur Bhatia — 2024-03-20

INTRODUCTION: FANETs are a type of wireless communication network consisting of Unmanned Aerial Vehicles (UAVs) or drones that work collaboratively to process data and attain optimal results. These networks have achieved significant attention due to their potential applications in diverse engineering fields. The paper provides a comprehensive analysis of FANET, covering various aspects related to its classification, architecture, communication types, mobility models, challenges, characteristics, and design. It also discusses the importance of routing protocols and topology in FANETs. Furthermore, this paper identifies and presents open issues and challenges in the field of FANETs, urging researchers to focus on exploring and addressing these essential parameters and research areas.

OBJECTIVES: This paper will aims to promote further investigation and advancement in the field of FANETs and similar networks, enabling researchers to explore and overcome the challenges to unleash the full potential of these UAV-based ad-hoc networks shortly.

METHODS: The data used in this paper was gathered from various research papers. A brief comparison among FANETs, MANETs, and VANETs has been shown and highlighted the main points. This paper also elaborates the general architecture, mobility models, routing, routing protocols in FANETs.

RESULTS: It was discovered that the use of both deterministic and probabilistic techniques is suggested to enhance the performance and efficiency of FANETs. By combining these methods, the paper suggests that better results can be achieved in terms of network reliability, adaptability, and overall performance.

CONCLUSION: This paper discusses the importance of routing protocols and topology in FANETs. Furthermore, this paper identifies and presents open issues and challenges in the field of FANETs, urging researchers to focus on exploring and addressing these essential parameters and research areas.

A Novel Comparative Analysis of Solar P&O, ANN-based MPPT Controller under Different Irradiance Condition

Pavithra C — 2024-01-26

The depletion of fossil fuels and rising energy demand have increased the use of renewable energy. Among all Solar PVs, system-based electricity production is increased due to multiple advantages. In this paper a Solar PV system with an Artificial Neural Network (ANN)-based Maximum Power Point Tracking (MPPT) controller is developed. ANN has multiple advantages like stability, improved dynamic response, and fast and precise output. The System is modelled with a DC-DC boost converter with Perturb and Observe (P&O)-based MPPT controller which is operated in MATLAB-based Simulink model. Both the controller output is analyzed and compared, among these two controllers ANN has very fast and more precise output under dynamic conditions.

Comparison of Solar P&O and FLC-based MPPT Controllers & Analysis under Dynamic Conditions

C Pavithra — 2024-01-31

Increase in electricity generation is caused due to population increase, which leads to the depletion of fossil fuels, and increased pollution. This leads to focusing on alternate renewable energy, mainly solar photovoltaic generation, due to the abundant availability. The maximum power generated by a PV module depends on the temperature and irradiance because the P-V and V-I natures are non-linear. Various DC-DC boost converters are used along with the MPPT techniques because the conversion efficiency of the PV system is low [1][2]. In this paper, comparative analysis between Perturb and Observe (P&O) and Fuzzy Logic-based Maximum Power Point Tracking (MPPT) systems along with modified SEPIC are done using MATLAB/ SIMULINK software. Simulations are done at different irradiations to observe its tracking speed towards MPP. From the obtained output (simulation), it is observed that the Fuzzy Logic Converter (FLC)-based MPPT controllers have good dynamic performance, reduced oscillation, high tracking speed, maximum power, etc...[3].

Seidel Laplacian Energy of Fuzzy graphs

K Sivaranjani — 2024-03-04

The energy of a graph is related to its spectrum, which is equal to the total of the latent values of the pertinent adjacency matrix. In this research work, we proposed some of the features and the energy of the Seidel Laplacian of a fuzzy graph. Also, the lower and upper bounds for the energy of the Seidel Laplacian of a fuzzy graph were studied with suitable illustrative examples.

Integrated Q-Learning with Firefly Algorithm for Transportation Problems

K R Pratiba — 2024-02-06

The study addresses the optimization of land transportation in the context of vehicle routing, a critical aspect of transportation logistics. The specific objectives are to employ various meta-heuristic optimization techniques, including Genetic Algorithms (GA), Ant Colony Optimization (ACO), Firefly Algorithm (FA), Particle Swarm Optimization (PSO), and Q-Learning reinforcement algorithm, to find the optimal solutions for vehicle routing problems. The primary aim is to enhance the efficiency and effectiveness of land transportation systems by minimizing factors such as travel distance or time while adhering to constraints. The study evaluates the advantages and limitations of each algorithm and introduces a novel-based approach that integrates Q-learning with the FA. The results demonstrate that these meta-heuristic optimization techniques offer promising solutions for complex vehicle routing challenges. The integrated Q-learning with Firefly Algorithm (iQLFA) emerges as the most successful approach among them, showcasing its potential to significantly improve transportation optimization outcomes.

Highly Efficient Maximum Power Point Tracking Control Technique for PV System Using Different Controller and Converter with Modular Multilevel Inverter

Pavithra C — 2024-02-27

In order to operate photovoltaic (PV) systems using maximum power point tracking (MPPT), three distinct combinations of controllers and converters are proposed in this research and compared. Using MATLAB/Simulink simulation, these strategies are assessed based on the output parameters of time, power, and current. The demand on power production has increased manifold in recent years and on the other hand, the conventional resources utilized for it will be vanished in near future. The requirement of PV based generation is getting increased. The procedure of getting solar energy from a solar panel is common. With MPPT, here the output obtained must be the same quantity of energy even when the source of that energy is partially available. Climate change and other issues could be to blame for this inefficiency. In this project three distinct converters and three distinct controllers have been compared. All three converters are linked to each controller individually, and measurements of current, voltage, and power are analysed. By which the result is obtained. After the comparison of nine outputs, the most powerful and efficient combination is identified. By doing this, the converters and controllers produce high D.C voltage. Direct voltage transmission to the MMI. A.C. voltage is created by converting D.C. voltage. Increase the MMI's output by doing this. The voltage generated by the MMI is sent to the grid for domestic usage. Even when the source is not readily available, the solar panel's voltage can still be used.

Rainfall Prediction using XGB Model with the Australian Dataset

Surendra Reddy Vinta — 2024-03-12

Rainfall prediction is a critical field of study with several practical uses, including agriculture, water management, and disaster preparedness. In this work, we examine the performance of several machine learning models in forecasting rainfall using a dataset of Australian rainfall observations from Kaggle. Six models are compared: random forest (RF), logistic regression (LogReg), Gaussian Naive Bayes (GNB), k-nearest neighbours (kNN), support vector classifier (SVC), and XGBoost (XGB). Missing value imputation and feature selection were used to preprocess the dataset. To analyse the models, we employed cross-validation and performance indicators such as accuracy, precision, recall, and F1-score. According to our findings, the RF and XGB models fared the best, with accuracy ratings of 87% and 85%, respectively.

With accuracy ratings below 70%, the GNB and SVC models performed the poorest. Our findings imply that machine learning algorithms can be useful tools for predicting rainfall, but careful model selection and evaluation are required for reliable results.

Smart Control Strategy for Adaptive Management of Islanded Hybrid Microgrids

S Poonkuzhali — 2024-03-25

This research paper presents a smart power control approach specifically designed for an independent microgrid. The proposed hybrid system consists of various crucial components, including a PV array, super capacitor, DC bus, battery bank, and AC bus working together to generate and store electricity within the microgrid. To address the challenges arising from random fluctuations in ecological parameters and changes in load demand, a supervisory controller is developed to enhance the standalone hybrid microgrid. This allows for optimized power management within the micro grid. The Liebenberg Marquardt algorithm is used to retrieve the trained ANN machine. The two and three hidden layered ANN machines have 96% accuracy on an average, whereas the single-layer ANN machine have poor predictive ability. The proposed model is implemented and analysed using MATLAB/Simulink. The observed results from the simulation experiments validate the effectiveness of integrating available resources in ensuring the resilience and reliability of microgrids.

Machine Learning Applied to Water Distribution Networks Issues: A Bibliometric Review

H Denakpo — 2024-03-27

INTRODUCTION: Water Distribution Networks are critical infrastructures that have garnered increasing interest from researchers.

OBJECTIVES: This article conducts a bibliometric analysis to examine trends, the geographical distribution of researchers, hot topics, and international cooperation in using Machine Learning for Water Distribution Networks over the past decade.

METHODS: Using “water distribution” AND (prediction OR “Machine learning” OR “ML” OR detection OR simulation), as search string, 4859 relevant publications have been retrieved from WoS database. After applying the PRISMA method, we retained 2427 documents for analysis with a Bibliometric library programmed in R.

RESULTS: China and the USA are the most productive on the ground, and only one African country appears in this ranking in 14th place. We also identified two ways for future research works, which are: the assessment of water quality and the design of optimisation models.

CONCLUSION: The application of this research in African countries would be fascinating for a better quality of service and efficient management of this resource, which is inaccessible to many African countries.

Design of Capacitive Power Transfer System with Small Coupling Capacitance for Wireless Power Transfer

Xin Wang — 2024-04-11

Wireless power transfer systems play an important role in the application of modern power supply technology. Wireless charging has been widely used in portable devices such as smartphones, laptops, and even some medical devices. Higher system efficiency can be achieved while reducing costs. This article describes the design of a capacitive power transfer (CPT) system using the Class-E amplifier method. When the capacitance of the coupling plate is small, the operation of Class-E amplifiers under Zero-Voltage-Switching (ZVS) conditions is very sensitive to their circuit parameters. By adding an additional capacitor to the Class-E amplifier, the coupling capacitance can be increased, resulting in better circuit performance. The high efficiency of the Class-E amplifier is verified by simulation and experimental results.

Research on Optimization of Power Battery Recycling Logistics Network

Yanlin Zhao — 2024-04-15

With the popularity and development of electric vehicles, the demand for power batteries has increased significantly. Power battery recycling requires a complex and efficient logistics network to ensure that used batteries can be safely and cost-effectively transported to recycling centers and properly processed. This paper constructs a dual-objective mathematical model that minimizes the number of recycling centers and minimizes the logistics cost from the service center to the recycling center, and designs the power battery disassembly and recycling process and the recycling logistics network, and finally uses a genetic algorithm to solve it. Finally, this article takes STZF Company as an example to verify the effectiveness of this method. The verification results show that the logistics intensity of the optimized power battery recycling logistics network has been reduced by 36.2%. The method proposed in this article can provide certain reference for power battery recycling logistics network planning.

Pedestrian Perception Tracking in Complex Environment of Unmanned Vehicles Based on Deep Neural Networks

Ruru Liu — 2024-04-15

INTRODUCTION: In recent years, machine learning and deep learning have emerged as pivotal technologies with transformative potential across various industries. Among these, the automobile industry stands out as a significant arena for the application of these technologies, particularly in the development of smart cars with unmanned driving systems. This article delves into the extensive research conducted on the detection technology employed by autonomous vehicles to navigate road conditions, a critical aspect of driverless car technology.

OBJECTIVES: The primary aim of this research is to explore and highlight the intricacies of road condition detection for autonomous vehicles. Emphasizing the importance of this key component in the development of driverless cars, we aim to provide insights into cutting-edge algorithms that enhance the capabilities of these vehicles, ultimately contributing to their widespread adoption.

METHODS: In addressing the challenge of road condition detection, we introduce the TidyYOLOv4 algorithm. This algorithm, deemed more advantageous than YOLOv4, particularly excels in pedestrian recognition within urban traffic environments. Its real-time capabilities make it a suitable choice for detecting pedestrians on the road under dynamic conditions.

RESULTS: The application of the TidyYOLOv4 algorithm in autonomous vehicles has yielded promising results, especially in enhancing pedestrian recognition in urban traffic settings. The algorithm's real-time functionality proves crucial in ensuring the timely detection of pedestrians on the road, thereby improving the overall safety and efficiency of autonomous vehicles.

CONCLUSION: In conclusion, the detection of road conditions is a critical aspect of autonomous vehicle technology, with implications for safety and efficiency. The TidyYOLOv4 algorithm emerges as a noteworthy advancement, outperforming its predecessor YOLOv4 in pedestrian recognition within urban traffic environments. As companies continue to invest in driverless technology, leveraging such advanced algorithms becomes imperative for the successful deployment of autonomous vehicles in real-world scenarios.

Suppression of Torque Ripple in Switched Reluctance Motors Which is Based on Synchronization Technology

Huixiu Li — 2024-04-16

The double salient pole structure of Switched Reluctance Motor (SRM) makes its electromagnetic field exist nonlinear saturation characteristics, resulting in its large torque pulsation in operation, so it is difficult to achieve speed regulation smoothly by traditional control methods. In view of this problem, a sliding mode control strategy which is based on synchronous transmission technology was proposed.Firstly, the basic structure of switched reluctance motor was analyzed, and the mathematical model of mechanical motion of switched reluctance motor was established. Secondly, an improved sliding mode controller which is based on synchronous signal transmission technology was designed by analyzing the reason of large torque ripple of switched reluctance motor, and the stability of the system was proved. Finally, simulation is used to verify the effectiveness of the control strategy.Compared with the traditional PID (Proportional Integral Differential) control algorithm, this control technology not only suppresses the SRM torque ripple effectively , but also makes the sliding mode controller output the precise target electromagnetic torque quickly by increasing the control variables. The results of research indicate that this design can not only restrain the torque ripple effectively, but also adjust the convergence speed and overshoot of the controller by adjusting the design parameters.