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Distribution Network Target Framework Planning Algorithm Based on Fuzzy Optimization and Grey System Theory
INTRODUCTION: The global energy transition, driven by the rapid growth of distributed renewable energy, stochastic load profiles (e.g., EV charging spikes), and conflicting stakeholder objectives, has brought unprecedented complexities to distribution network planning. Traditional deterministic methods fail to handle qualitative fuzziness (e.g., subjective reliability thresholds) and quantitative uncertainty (e.g., sparse historical data), leading to inflexible and inefficient solutions. This study addresses these challenges by developing a hybrid planning framework. OBJECTIVES: This paper aims to solve the dual challenges of qualitative fuzziness and quantitative uncertainty in distribution network planning, providing a systematic solution to accommodate distributed renewable energy, handle load uncertainty, and balance conflicting stakeholder preferences through integrating fuzzy optimization theory and grey system theory. METHODS: The hybrid algorithm combines fuzzy optimization and grey system theory. Fuzzy optimization uses triangular fuzzy numbers for load growth rates ([3%, 5%, 8%]) and trapezoidal fuzzy intervals for voltage constraints ([−10%, −5%, 5%, 10%]) with membership functions (threshold λ≥0.8) to convert qualitative requirements into solvable constraints. Grey system theory applies the GM(1,1) model for load forecasting (achieving 4.2% MAPE with 15-month data) and grey relational analysis (GRA) for data-driven objective weighting to eliminate expert bias. An improved particle swarm optimization (IPSO) algorithm is used for optimization, validated in a 33-node network with 8.5 MW PV and 6 MW wind capacity. RESULTS: In the 33-node case study, compared to the deterministic genetic algorithm (D-GA), the hybrid algorithm reduces lifecycle costs by 19% (from $8.91M to $7.23M), increases renewable energy accommodation by 24% (from 9.8 MW to 12.3 MW), and improves the system average supply availability index (ASAI) from 99.92% to 99.95%. Under extreme uncertainties (±40% renewable output, ±30% load shifts), cost deviations remain within 6% and reliability metrics within 5%, demonstrating strong robustness. CONCLUSION: This research presents a robust hybrid framework that bridges fuzzy qualitative reasoning and grey data efficiency, effectively addressing both qualitative fuzziness and quantitative uncertainty in distribution network planning. It provides a science-based tool for resilient grid design, with potential for extension to multi-energy system integration and real-time optimization in future work.
Copyright (c) 2024 Jinsen Liu, Molin He, Jie Wang, Jie Lu https://creativecommons.org/licenses/by-nc-sa/4.0
Distributed photovoltaic power prediction considering spatiotemporal correlation and dual Attention-LSTM
Predicting the power output of photovoltaic clusters is crucial for optimizing regional solar power scheduling. To enhance the accuracy of distributed photovoltaic station power forecasts, a method incorporating spatiotemporal correlation and dual Attention-LSTM is introduced. The K-means algorithm is first employed to classify the distributed photovoltaic power station clusters in the area. The reference station for the target photovoltaic station is determined by calculating the Euclidean distance between the target station and the typical daily power profiles of other stations in the cluster. Simultaneously, pivotal weather features that influence photovoltaic output are ascertained through computation of the Pearson correlation coefficient. Subsequently, an Attention-LSTM-based power prediction and error correction model is constructed, utilizing both meteorological and power traits as input variables to finalize the photovoltaic power generation forecast. To validate the approach, a simulation analysis is conducted using empirical data from Arizona, USA. The experimental results indicate that the suggested method greatly improves the accuracy of predictions for distributed photovoltaic power.
Advanced Wireless Power Transmission Technology
Copyright (c) 2024 Yueyuan Zhang, Yifan Zhang, Dazhi Pan, Mingyu Sun, Huawei Mei, Wangbin Cao https://creativecommons.org/licenses/by-nc-sa/4.0
Short-term photovoltaic power prediction based on dual decomposition with TCN-Informer-xLSTM
As renewable energy generation is increasingly integrated into power grids worldwide, the random nature of renewable energy output poses significant challenges to the stability of power systems. Therefore, it is essential to accurately predict the output of renewable energy sources. In this paper, a dual decomposition algorithm based on variational mode decomposition (VMD) and improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is proposed to decompose the original photovoltaic power sequence and combine the entropy values of the subsequences to obtain the predicted sequences for the high frequency and low frequency components. Then, different prediction models are used for the high-frequency and low-frequency sequences to predict the photovoltaic outputs, where the Temporal Convolutional Networks (TCN)-Informer model is used for the high-frequency component and the xLSTM model is used for the low-frequency component, and finally, the RIME algorithm is applied to find the optimization of the hyperparameters. The results of simulation analysis show that the quadratic decomposition method proposed in this paper significantly improves the prediction accuracy of photovoltaic sequences and reduces the computational complexity
Copyright (c) 2024 Guancheng Jin, He Jiang, Mofan Wei, Rui Guo https://creativecommons.org/licenses/by-nc-sa/4.0
A reliability enhancement strategy of critical nodes in power system communication network based on quantitative calculation method and criticality index
INTRODUCTION: Power system communication networks are essential for smart grid operations, enabling real-time monitoring and control. Disruptions at critical communication nodes can jeopardize grid stability and lead to cascading failures, highlighting the need for accurate reliability assessment of these vital components. However, traditional methods often overlook the complex, dynamic, and interdependent nature of modern communication infrastructures. OBJECTIVES: This paper aims to develop a precise and scalable methodology for assessing and enhancing the reliability of critical nodes in smart grid communication networks. METHODS: The proposed approach integrates probabilistic failure modeling, graph-theoretic analysis, and heuristic optimization. Key techniques include a newly designed Criticality Index (CI) accounting for failure probabilities, repair dynamics, and topological relevance; a Monte Carlo simulation framework to assess network behavior under stochastic disturbances; and a genetic algorithm (GA) for optimizing node reinforcement strategies. RESULTS: Experiments conducted on the IEEE-118 bus system demonstrate that the GA-CI methodology improves the Network Robustness Index by 12.45%, consistently outperforming baseline methods with acceptable computational efficiency. CONCLUSION: The proposed framework provides a robust and interpretable solution for reinforcing critical communication infrastructure in smart grids. It holds potential for broader application in the reliability assessment of other complex networked systems.
Copyright (c) 2024 Xiaoyu Deng, Yabin Chen, Yu Sui, Hao Yu https://creativecommons.org/licenses/by-nc-sa/4.0
Intelligent substation communication network fault location method based on dynamic spatiotemporal graph association perception
INTRODUCTION: Accurately locating faults in intelligent substation communication networks is crucial for power grid safety. Existing methods fail to fully capture dynamic fault characteristic evolution and complex dependencies within network topologies OBJECTIVES: This paper aims to (1) model spatiotemporal fault features in communication networks, (2) enhance fault pattern capture through multi-view learning, and (3) improve fault location accuracy. METHODS: We propose a multi-view spatiotemporal dynamic graph network. First, a multi-view graph neural network models spatial dependencies via cross-view comparative learning using topological and attribute data. Second, a gated recurrent unit with dynamic time windows extracts temporal evolution trends, focusing on local fault patterns and short-term dependencies. RESULTS: Evaluations on a 220kV substation communication network show our method achieves higher fault location accuracy versus baselines, effectively capturing spatiotemporal fault characteristics. CONCLUSION: The proposed framework addresses dynamic fault evolution and topological dependencies, providing a robust solution for intelligent substation fault diagnosis.
Copyright (c) 2024 Ligang Ye, Wenzhang Li, Jing Zhao, Yuanyuan Liu https://creativecommons.org/licenses/by-nc-sa/4.0
Deep Learning and GIM Standard-Based Intelligent 3D Modeling Algorithm for Substations with Distributed Collaborative Design System
INTRODUCTION: Contemporary substation design methodologies encounter fundamental limitations in achieving optimal geometric precision and collaborative efficiency, particularly when addressing the integration of heterogeneous data sources while maintaining strict adherence to Geographic Information Modeling (GIM) standards. Existing computational approaches demonstrate significant deficiencies characterized by prolonged processing durations and constrained accuracy levels, thereby necessitating the development of innovative solutions that leverage cutting-edge artificial intelligence techniques to overcome these systematic challenges. OBJECTIVES: This investigation aims to develop and validate a comprehensive intelligent 3D modeling algorithm specifically designed for electrical substation applications that seamlessly integrates advanced deep learning methodologies with rigorous GIM standard compliance and sophisticated distributed collaborative design functionalities, while simultaneously achieving substantial improvements in geometric accuracy and computational efficiency compared to conventional design paradigms. METHODS: The proposed algorithmic framework employs sophisticated hierarchical neural network architectures that incorporate multi-scale convolutional feature extraction mechanisms and adversarial generative training protocols. The comprehensive system architecture integrates four critical components: intelligent data acquisition and preprocessing modules, advanced deep learning computational engines, automated GIM standard compliance verification systems, and distributed collaborative design platforms. Experimental validation was conducted using an extensive dataset encompassing 12,847 technical engineering drawings, 1,156 high-resolution point cloud segments, and 3,428 photogrammetric image collections, with comprehensive field testing involving up to 32 concurrent collaborative users across diverse operational scenarios. RESULTS: The developed intelligent modeling algorithm achieved exceptional geometric accuracy of 96.8% compared to 87.3% demonstrated by traditional methodologies, representing a substantial 9.5 percentage point improvement in modeling precision. Computational efficiency demonstrated remarkable optimization with processing time reduced by 94%, decreasing from the conventional range of 180-240 minutes to an unprecedented 12.4 minutes per complete substation model. Extensive field validation trials confirmed seamless collaborative scalability with negligible performance degradation under multi-user operational conditions, while maintaining GIM standard compliance exceeding 99.2% across all tested configurations and operational scenarios. CONCLUSION: The developed intelligent 3D modeling system establishes a new technological paradigm for substation design applications, delivering exceptional improvements in both geometric accuracy and computational efficiency while maintaining stringent GIM compliance requirements. The framework's robust integration capabilities enable seamless deployment within existing power system management infrastructures without necessitating extensive modifications to established operational workflows, thereby providing a comprehensive foundation for next-generation collaborative engineering design platforms in critical infrastructure applications.
Copyright (c) 2024 Zhongsheng Kan, Yu Xin, Delong Meng, Jie Chen, Xiaoming Ju https://creativecommons.org/licenses/by-nc-sa/4.0
Phase Robust Optimization of PV-Energy Storage Microgrid Based on Deep Reinforcement Learning and Mixed Integer Constraint Model
The pronounced dependence of photovoltaic (PV) generation on meteorological conditions, coupled with substantial fluctuations in load demand, renders conventional deterministic optimization approaches inadequate. Addressing the need for robust multi-phase decision-making across temporal domains (e.g., day-ahead scheduling and real-time adjustment) and the coordinated optimization of continuous variables (such as energy storage charge/discharge rates) and discrete variables (such as unit commitment states), this research proposes a phased robust optimization strategy for PV-storage microgrids. This strategy integrates Deep Reinforcement Learning (DRL) with a Mixed-Integer Constrained Model (M-ICM). The methodology explicitly accounts for the coupling effects between irradiance intensity, temporal sequence efficiency, and the state-of-charge of energy storage systems. This ensures that the microgrid control system provides sufficient resilience mechanisms for dynamic energy allocation in practical applications, facilitating global optimization of microgrid energy utilization. The simulation results show significant improvements over conventional methods, which includes reduction in time-to-peak under dynamic balancing conditions, maintenance of lower output current-to-power ratios, and enhanced convergence speed of the neural network model.
Copyright (c) 2024 Wei Li, Zhihang Qin https://creativecommons.org/licenses/by-nc-sa/4.0
Fine-Tuning the Qwen2.5-VL Model for Intelligent Applications in the Electrical Domain
This study explores the fine-tuning application of the Qwen2.5-VL multi modal large model in the electrical domain. The electrical industry faces numerous challenges in maintaining and managing complex electrical systems. Traditional methods often rely on manual inspection and analysis. With the rapid advancement of artificial intelligence (AI) technologies, there is a growing need to explore how these tools can be applied to improve efficiency and accuracy in the electrical domain. Qwen2.5-VL is a state-of-the-art visual language model. We adopted the LoRA (Low Rank Adaptive) method to fine tune the model, which enables efficient parameter updates in low resource environments while maintaining high performance. This study analyzes the data characteristics and task requirements in the electrical domain, designs fine-tuning strategies with a focus on image-based applications, including data preprocessing, model fine-tuning, and training parameter optimization. The experimental re-sults show that the fine tuned model has achieved significant performance im-provements in tasks such as electrical equipment fault detection, image recogni-tion, and text classification. This study provides new ideas and methods for the application of artificial intelligence in the electrical domain, which is of great significance for promoting the development of electrical intelligence.
Copyright (c) 2024 Yao Song, Chunli Lv, Kun Zhu, Xiaobin Qiu https://creativecommons.org/licenses/by-nc-sa/4.0
Demand Response Control Strategy of Photovoltaic Grid-connected Inverter Based on Improved Linear Active Disturbance Rejection Dynamic Decoupling
In power systems, because the Auto Disturbances Rejection Controller (ADRC) performs relatively reliable stability and safety, it finds extensive application in the design of power inverters. However, its performance is constrained by inherent response lag and limited harmonic suppression capabilities. To address the evolving demands of modern power systems, Linear Active Disturbance Rejection Control (LADRC) has emerged as an upgraded alternative to conventional control strategies through its enhanced adaptability in practical applications. We propose a frequency-domain-equivalent-based LADRC control strategy, where systematic parameter calibration is implemented through transfer function methodology. Subsequent fine-tuning of critical parameters, which includes proportional gain, derivative gain, observer's natural frequency, control gain and so on, are made fine adjustments to optimize circuit performance. Experimental validation confirms the efficacy of first-order LADRC in improving voltage-current conversion characteristics of LLC-operated transformers. Furthermore, an adaptive control framework for third-order LADRC is established to achieve coupled parameter optimization. This research innovatively incorporates Particle Swarm Optimization (PSO) into the LADRC parameter configuration process, enhancing adjustment efficiency and facilitating optimal solutions. Simulation results verify that the enhanced LADRC effectively mitigates phase delay and improves dynamic response characteristics. The modified PSO algorithm exhibits technical superiority with 11.7% precision enhancement. The response speed has obvious technical advantages, and the coefficient of variation is significantly lower than that of the comparison algorithm, which verifies the advantages of the improved PSO algorithm in robustness. The proposed control strategy successfully compensates for ADRC limitations, achieving an optimal balance between control precision and steady-state performance through gain regulation in repetitive control systems. This advancement will provide critical technical support for addressing control challenges in renewable energy integration.
Active Disturbance Rejection Dynamic Decoupling
Copyright (c) 2024 Chao Zhang, Yangrui Zhang, Yongliang Jia, Peng Tao, Guinan Han, Junpeng Zhao https://creativecommons.org/licenses/by-nc-sa/4.0
A fault diagnosis and location method for power grid simulators based on voltage threshold and MCNN
To accommodate the testing requirements of high-power wind turbines, this paper designs a power grid simulator topology and investigates fault diagnosis and localization methods by integrating mathematical models and neural networks. To address the drawback of lengthy computation times associated with intelligent diagnostic methods, this paper employs a threshold-based approach using voltage mathematical models to achieve rapid preliminary diagnostics. To address the positioning challenges brought about by symmetrical structures, a multi-layer convolutional neural network (MCNN) model is utilized to achieve accurate positioning. To tackle the issue of insufficient fault samples, a sliding window technique and frequency domain transformation methods are applied to expand the sample set, enabling the diagnosis and localization of 36 types of faults. This paper builds an inverter-side model of the power grid simulator using Simulink to verify the proposed method. And the diagnostic accuracy rate reaches 100%, and the overall localization accuracy exceeds 96%.
Copyright (c) 2024 Jinyue Su, Yingwen Yang, YiLin Yu, Yueshuo Li, Shiwei Zhao, Zhidong Wang, Ling Yang, Fengqiang Deng https://creativecommons.org/licenses/by-nc-sa/4.0
On-Site Protection Scheme for Microgrid with High Penetration Rate of Grid-Forming Inverter Interfaced Distributed Energy Resources
INTRODUCTION: Renewable energy represented by grid forming inverter interfaced distributed energy resources is increasingly connected to microgrids that can operate independently and have various modes, resulting in traditional relay protection schemes that cannot meet the requirements of protection selectivity and speed. OBJECTIVES: To this end, the fault characteristics of the grid forming distributed energy resources are analyzed. On this basis, the current, voltage and phase angle characteristics between them under the grid-connected/islanded operation mode of the microgrids are analyzed. METHODS: Aiming at the problem that the traditional protection loses selectivity, the phase angle difference between the pre-fault voltage and the post-fault current is used to judge the fault direction, and the internal fault acceleration factor of the integrated phase angle difference and the measured impedance is used to improve the action equation of the inverse time overcurrent protection. Aiming at the problem that the fault current is too small and the inverse-time overcurrent protection cannot be started when the two-phase short-circuit fault occurs in the islanded state, a low-voltage protection criterion based on the comprehensive phase angle difference characteristics is set up, and a microgrid protection scheme combining improved inverse-time over-current protection and low-voltage protection is obtained. RESULTS: The model is built by PSCAD, the theoretical analysis and simulation results show that the scheme can operate quickly and reliably in the grid-connected/islanded operation state of the microgrid. CONCLUSION: The results verify the effectiveness of the proposed protection scheme.
Inverter Interfaced Distributed Energy Resources
Voltage and Current Phase Angle Difference
Copyright (c) 2024 Yubo Yuan, Juan Li, Yunlong Jiang, Sudi Xu, Haiou Cao https://creativecommons.org/licenses/by-nc-sa/4.0
Application of Offshore Wind Power Digital Twin Technology in Remote Operation and Maintenance
INTRODUCTION: Offshore wind (OsW) energy has emerged as a key factor in the global transition towards high-energy-return renewable energy, with stable winds and minimal land take. However, the Maintenance and Operation (O&M) of OsW wind farms are very challenging due to their hostile marine conditions, huge running costs, and poor accessibility. OBJECTIVES: To address these issues, the proposed method offers a Digital Twin (DT) strategy designed to enhance the remote operation and maintenance (O&M) of OsW wind power equipment. From the use of high-resolution global OSW wind turbine observations from Sentinel-1, combined with domain-specific feature engineering and data preprocessing, including outlier removal and normalization, the approach provides robust input for modelling and analysis. METHODS: One of the most important aspects of the system is the integration of real-time sensor feeds through Arduino devices, secure data exchange through OPC UA, and middleware processing through Node-RED. The sensor-based data architecture feeds into a Unity 3D-based digital twin environment, which continuously synchronizes virtual models with the physical conditions of the turbines. RESULTS: Besides, fault classification is handled with a Categorical Network (CatNet), where attention mechanisms and convolutional layers are used to detect abnormalities such as gearbox faults, generator faults and yaw misalignment. Interactive dashboards, 3D visualization, and predictive analytics are supported within the framework, enabling operators to monitor, diagnose, and control offshore turbines remotely. CONCLUSION: Ultimately, this approach significantly reduces unplanned downtime, enhances safety, and maximizes power output through intelligent, data-driven decision-making.
Deep Learning for Real-Time Prediction and Optimization in Renewable Energy Systems
Copyright (c) 2024 Huatao Si, Xiaotao Chen, Qingyu Men https://creativecommons.org/licenses/by-nc-sa/4.0
Data-Driven Decision-Making Method of Intelligent Supervision and Command Platform in Offshore Wind Power Operation and Maintenance
INTRODUCTION: Innovations in offshore wind farm operation and maintenance require intelligent monitoring platforms that can leverage high-resolution SCADA data to enhance predictive precision and operational effectiveness. With the use of deep learning algorithms, specifically LSTM, this work achieves improved forecasting and anomaly detection accuracy on a real wind turbine dataset recorded in Turkey in 2018. OBJECTIVES: The proposed approach involves extensive data preprocessing, including cleaning, synchronization, and normalization, followed by advanced feature extraction using signal processing transforms such as the Fast Fourier Transform and wavelet transforms. METHODS: Different predictive models, including Linear Regression, Random Forest Regression, Support Vector Regression, Gradient Boosting Machines, and LSTM, were trained and tested within a Python setting. The LSTM model achieved a remarkable improvement, with a Mean Absolute Error of 78.6 kW, compared to traditional machine learning methods such as RF Regression, SV Regression, and Gradient Boosting Machines. The enhanced accuracy results from the LSTM's ability to derive intricate temporal patterns and nonlinear relationships inherent in sequential turbine operational data. RESULTS: The results affirm the potential of deep learning approaches in reshaping offshore wind turbine management and highlight the importance of tailored temporal modeling for resolving the specific challenges of renewable energy systems. CONCLUSION: The system not only accurately predicts power production but also performs anomaly detection and optimises maintenance scheduling, resulting in enhanced reliability and energy production for offshore wind farms. By integrating these data-oriented approaches with an intelligent command and supervision system, the strategy facilitates proactive decision-making and real-time operation control.
Copyright (c) 2024 Jia Kun Wang, Yi Liu, Suowei Song https://creativecommons.org/licenses/by-nc-sa/4.0
Towards Real-Time Spatial Distance Monitoring of Power Transmission Lines Using LiDAR Point Clouds and Visual Imaging
INTRODUCTION: Efficient monitoring of power transmission lines is paramount to grid safety, clearance violation prevention, and uninterrupted supply of electricity. Classic inspection approaches like ground surveys by manual methods and visual inspections by drones are time-consuming, costly, and susceptible to human error. OBJECTIVES: Current LiDAR-based approaches are limited in automation, with extensive post-processing based on manual intervention. Additionally, most existing models are not scalable and fail under changing environmental conditions because of a lack of generalization. In this research, a spatial monitoring platform that combines LiDAR point clouds with high-resolution imagery through RandLA-Net is presented for semantic segmentation and hazard detection. METHODS: Combining geometric information (LiDAR) and visual features (images) with an optimized RandLA-Net architecture allows for accurate, real-time infrastructure features and hazard detection in dense or cluttered scenarios. RESULTS: The system presented here attained a semantic segmentation accuracy of 99.1% and a mean Intersection over Union (mIoU) of 93.2%. Spatial distance estimation had a low Mean Absolute Error (MAE) of 0.16 meters and Root Mean Square Error (RMSE) of 0.23 meters. The rate of safety violations detected never exceeded 4% among all object pairs. Compared to alternative techniques the proposed approach offers higher segmentation accuracy and more comprehensive hazard detection. CONCLUSION: It uniquely combines LiDAR and image data with advanced algorithms for precise, real-time distance measurement and monitoring. This study provides a cost-effective, scalable, and real-time-enabled monitoring solution, lessening reliance on human inspections and hugely enhancing hazard detection accuracy for power transmission infrastructure.
Copyright (c) 2024 Li Zhendong, Wang Feiran, Han Geng, Guo Xinyang, Shi Zhaoyang https://creativecommons.org/licenses/by-nc-sa/4.0
Research on the Mechanism of Power Quality in Renewable Energy Marketing Based on BMA Algorithm
INTRODUCTION: Based on the BMA (Bayesian Model Average) algorithm, this paper deeply studies the mechanism of power quality in renewable energy marketing. By collecting a large amount of renewable energy market data and utilising the BMA algorithm for model building and data analysis, this paper aims to reveal the internal relationship between power quality and renewable energy marketing and explore its impact on actual market operations. OBJECTIVES: In the context of data analysis, this paper selects multidimensional information, including sales data, user feedback data, and power quality monitoring data, from the renewable energy market over recent years. By integrating and analysing these data with the BMA algorithm, we find that power quality has a significant positive impact on the marketing of renewable energy. METHODS: Specifically, when power quality is improved by 10%, the sales of renewable energy products are expected to increase by approximately 6% on average, and customer satisfaction will rise by 7% accordingly. This data shows that improving power quality can effectively promote the expansion of the renewable energy market and enhance user loyalty. Our research, leveraging the BMA algorithm, reveals that improving power quality serves as a dual catalyst for promoting renewable energy. RESULTS: Firstly, it directly bolsters consumer trust and purchase intention towards renewable energy products. Secondly, it indirectly enhances the market competitiveness of these products by optimising their performance and reducing failure rates. These insights provide renewable energy enterprises with a pivotal foundation for optimising their marketing strategies. CONCLUSION: In conclusion, power quality emerges as a crucial factor in the marketing of renewable energy. By prioritising its improvement, enterprises can simultaneously enhance sales, customer satisfaction, and product competitiveness, thereby underscoring the importance of power quality as a core component of their marketing strategy.
Copyright (c) 2024 Qinghua Fan https://creativecommons.org/licenses/by-nc-sa/4.0
Intelligent Optimization of Combustion Process and NOx Emission Control of Power Plant Boiler Based on Deep Learning and Multi-objective Optimization
This study proposes an intelligent optimization and nitrogen oxide (NOx) emission control method for power plant boiler combustion processes by integrating deep learning and multi-objective optimization. While traditional empirical tuning and single-objective algorithms struggle with dynamic, multi-variable combustion environments and lack real-time adaptability and synergistic optimization of efficiency and emissions, this research addresses these gaps by establishing a rolling optimization model that considers load and emissions. By analyzing the relationship between boiler combustion efficiency and nitrogen oxides generation, a rolling optimization model considering load and emission is established. The study analyzes and predicts the operation data and optimizes the combustion strategy in real time by a dynamic multi-objective optimization evolutionary algorithm. Performance evaluation shows that the model achieves high prediction accuracy, with an average absolute error of 2.36×10-5 kW for boiler load, and outperforms existing models in key metrics such as ignition success rate (98.7%) and load adjustment accuracy (3.4 MW). The approach significantly improves combustion efficiency and tightens NOx control, reducing energy waste and improving power plant energy efficiency. These advances demonstrate their effectiveness in improving combustion efficiency, enhancing nitrogen oxide control, and reducing energy waste, providing a powerful solution for operating smart power plants that integrates real-time adaptability and multi-objective synergy, outperforming traditional methods.
Copyright (c) 2024 Xiaowei Chen, Lei Yan https://creativecommons.org/licenses/by-nc-sa/4.0
Deep Learning Model for Feature Extraction and Anomaly Recognition in High-Dimensional Energy Metering Data
Introduction: The rapid expansion of energy networks has significantly increased energy consumption, resulting in higher electricity costs. Abnormal energy usage in buildings and industries, often caused by system malfunctions, leads to substantial energy waste. Detecting such anomalies is essential for cost control and efficient energy management. Objectives: This study aims to develop a deep learning-based method to detect anomalies in high-dimensional energy metering data, overcoming the limitations of existing techniques that struggle with data complexity and lack effective contextual analysis. Methods: High-dimensional metering data from a city energy provider is processed using a Convolutional Autoencoder (CAE) to extract deep features and reduce dimensionality. These features are then fed into a Cascaded Long Short-Term Memory (CLSTM) network, which identifies anomalous patterns in the data. Results: The cascaded CLSTM model effectively detects anomalies in the energy consumption data by accurately predicting deviations from normal patterns. Conclusion: The proposed CAE-CLSTM approach enhances anomaly detection in complex energy datasets, enabling more effective monitoring and reducing unnecessary energy waste and costs.
Copyright (c) 2024 Huakun Que, Zetao Jiang, Zhifeng Zhou, Yongsheng He, Xin Liu https://creativecommons.org/licenses/by-nc-sa/4.0
Coal Energy Production Efficiency Improvement Strategy with Integrated Genetic Algorithm and Its Economic Impact Assessment
INTRODUCTION: Global energy systems heavily rely on coal energy generation, particularly in emerging nations. OBJECTIVES: Strategies that maximize the efficiency of coal energy generation while limiting environmental harm are essential to addressing these issues. With an emphasis on increasing productivity and minimizing environmental effects, this study suggests an integrated strategy for optimizing coal energy production processes using Genetic Algorithms (GA). METHODS: Key factors, including GDP growth rate, pollution abatement investment, coal intensity, and clean technology efficiency, are all optimized using GA in the suggested approach. Finding the best combination of these factors to maximize coal production efficiency while reducing CO2 emissions and other pollutants is made possible by GA-based optimization. A Social Cost-Benefit Analysis (SCBA) and environmental impact appraisal are also included to assess various scenarios' economic and environmental consequences. The findings show that, particularly in situations with slower GDP growth, more pollution abatement expenditures and cleaner technology adoption result in notable emissions reductions and increased overall efficiency. RESULTS: The results show how crucial it is to balance environmental sustainability and economic prosperity. The study offers insightful information to industry executives and regulators, highlighting GA's potential to maximize the efficiency of coal energy generation. CONCLUSION: Scenario A provided the best economic advantages, with a greater GDP growth rate and higher environmental costs.
Copyright (c) 2024 Fangmin Chen, Zheng Ma https://creativecommons.org/licenses/by-nc-sa/4.0
Smart Grid Implementation Based on Solar Generation for Load Demand Management in Najaf City, Iraq
Electrical Smart Grids (SG) contain numerous energy and operational measures, including renewable energy resources, energy efficiency resources, smart meters, and appliances. Investigating their applicability in a geographical area is important to provide a firm background to practical considerations. In this paper, the SG model is investigated, considering the available solar generation, hence enhancing residential load demand in Najaf city, Iraq. The analysis begins with an assessment of the electrical load profile characteristics, followed by choosing the optimum orientation of solar panels for the cities, which has been presented using MATLAB software. The optimum tilt angle for each month is estimated for three Photovoltaic (PV) systems of three sizes (1.5kWp, 2kWp, and 2.5 kWp). Furthermore, the study of their generation and impact on load demand is analyzed. The economic and financial analysis has been presented with payback period and benefits. The duck curve generated by the proposed PV systems is discussed. It is found that the annual demand saving of the proposed solar systems is 22%, 31%, and 36%, respectively. The hourly demand saving is presented, and it has been observed that there is overgeneration using 2kWp and 2.5 kWp PV systems, resulting in reshaping the load curve and producing the duck curve. The future work can be summarized by suggesting management of the battery system for the overgeneration.
Copyright (c) 2024 Dr. Hasan N. Muslim https://creativecommons.org/licenses/by-nc-sa/4.0
A Level-Shift Carrier PWM Modulation Technique and Predictive Current Control of a Multilevel DC-AC Converter for Interfacing Renewable Energy Sources
The integration of renewable energy sources into the electrical power grid is a crucial aspect for achieving sustainable development and reducing environmental impacts. This paper explores the implementation of a three phase Neutral Point Clamped (NPC) DC AC converter with level shift carrier PWM modulation technique and predictive current control to facilitate the grid interconnection of renewable energy systems. Multilevel converters offer important advantages over traditional two level converters, such as reduced harmonic distortion and improved efficiency. Computational simulations using the PSIM software were conducted to validate the operation of the converter, both as active rectifier and as inverter. The results demonstrated the ability of the converter to adjust its current generation, even when considering sudden power variations in the load, as well as variations in the electrical power grid. Moreover, it effectively controls the DC link voltage, while maintaining a minimal ripple in the controlled AC current. The obtained results allow to verify the robustness and reliability of the converter for the integration of renewable energy (solar photovoltaic panels) into the power grid, contributing to the pursuit of sustainable energy solutions for a greener future.
Copyright (c) 2024 Vitor Pinto, Joao L. Afonso, Vitor Monteiro https://creativecommons.org/licenses/by-nc-sa/4.0
Comparison of Machine Learning and Deep Learning Models Performance in predicting wind energy
The prediction of wind energy generation is important to enhance the performance and dependability of renewable energy systems due to the rising demand for wind-generated electricity and advancements in wind energy technology competitiveness. This study leverages advanced machine learning (ML) and some other statistical and deep learning based time series forecasting models to enhance the accuracy of wind energy predictions. This comprehensive analysis includes nine ML models—Linear Regression, Random Forests (RF), Gradient Boosting Machines (GBM), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), AdaBoost, XGBoost, Support Vector Regression (SVR), and Neural Networks—as well as Four time-series forecasting models—ARIMA, Temporal Convolutional Networks (TCNs), Long Short-Term Memory (LSTM) networks and GRU. Each ML model underwent rigorous cross-validation to ensure optimal performance. The assessment criteria utilized here comprised the Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the R² Score. It was found that among the nine ML models, Random Forests, GBM and KNN consistently provided superior accuracy and robustness, making them the top choices for wind energy prediction whereas the performance of linear regression, SVM and SVR were very poor for the considered dataset. From the experiment, Random Forest, GBM, and KNN showed the best performance with low MSE values of 0.77, 1.95, and 1.51 respectively, while other models had MSEs above 7.5, with AdaBoost reaching 30. Their RMSEs (0.88, 1.40, 1.23) and MAEs (0.093, 0.73, 0.10) also indicate strong predictive accuracy compared to the rest.In this paper, time series forecasting, TCNs, LSTM and GRU networks showed strong capabilities in capturing temporal dependencies and trends within the wind energy data. Visualization techniques were employed to compare model performances comprehensively, providing clear insights into their predictive power. Therefore, this present study offers a robust framework for researchers and practitioners aiming to leverage machine learning and time series forecasting in the realm of renewable energy prediction.
Copyright (c) 2024 Saswati Rakshit, Anal Ranjan Sengupta https://creativecommons.org/licenses/by-nc-sa/4.0
Research on the Construction of a Short-Term Voltage Prediction Model Integrating Topological Data Analysis and Deep Neural Network under the Power System Resilience Assessment Framework
INTRODUCTION: This paper examines the stability of small disturbances in wind farm grid-connected systems within the framework of power system resilience. With increasing renewable integration, minor disturbances can escalate into cascading failures, threatening grid reliability. OBJECTIVES: The goal is to build a short-term voltage prediction model by integrating Topological Data Analysis (TDA) with Deep Belief Networks (DBN) and to propose a coordinated reactive power control strategy that enhances system dynamic performance under small disturbances. METHODS: The study adopts a VSC-HVDC system based on Modular Multilevel Converters (MMC) to model wind farm connectivity. A cluster-based reactive power control approach is applied by grouping wind turbines with similar operational characteristics. Small disturbance signals are simulated, and both unified and decentralised Doubly Fed Induction Generator (DFIG) control schemes are compared using impedance modelling and time-domain analysis. RESULTS: Simulations indicate that small AC-side disturbances have a significant impact on reactive power and system voltage, whereas DC-side faults affect frequency stability. The decentralised DFIG coordination strategy achieved a lower network loss (0.467 MW) compared to the unified approach (0.473 MW) while also improving reactive power allocation and system responsiveness. CONCLUSION: By combining TDA and DBN with decentralised control, the proposed model enhances the stability of small disturbances in wind-integrated power systems. It enhances fault tolerance, mitigates power fluctuations, and facilitates the resilient operation of renewable-rich grids.
Copyright (c) 2024 Hongjun Wang, Tao Li, Zhiliang Dong https://creativecommons.org/licenses/by-nc-sa/4.0
Fault Diagnosis of Power Equipment Based on Improved SVM Algorithm
Fault diagnosis of power equipment is a crucial task to credit the safe and stable operation of equipment. However, fault diagnosis of power equipment faces challenges such as high dimensionality, complexity, and nonlinearity. Therefore, this study proposes an improved support vector machine model, combined with grey wolf optimization algorithm, aimed at improving the accuracy and efficiency of power equipment fault diagnosis. To validate the model’s performance, this study divided a dataset of 3870 power equipment defects into training and testing sets using an 8:2 ratio, with evaluation metrics including accuracy, recall, and F1 score. The results showed that the fault recognition rate of the support vector machine model based on the improved grey wolf optimization algorithm reached 92.76%, with an accuracy close to 0.95 and a loss rate of 0.13. The model exhibited faster convergence speed, as well as better stability and convergence. At the same time, the optimized model had good feature extraction ability on different types of model faults, and the comprehensive recognition error of the model was basically stable in the interval of (-0.005, 0.005). The experiment validates that the research model improves the optimization algorithm through modal decomposition strategy. Meanwhile, the improvement of support vector machine parameter selection has strengthened the recognition and analysis of fault characteristics, providing an effective solution for power equipment fault diagnosis.
Copyright (c) 2024 Youle Song, Yuting Duan, Tong Rao https://creativecommons.org/licenses/by-nc-sa/4.0
Control Method of Microgrid Grid Connected Inverter Based on Quantum GA-PID
The current trend is towards ever-increasingly rigorous control performance requirements for grid-connected inverters. Therefore, a proportional integral derivative control method of the quantum genetic algorithm and particle swarm optimization has been proposed, which can achieve stable and efficient operation of microgrids.The total harmonic distortion of the improved controller was 1.03%, which was 1.98% lower than the traditional method and far below the national standard of 5%; The prediction accuracy wad on average 82%, 51%, and 54% higher than the other three classic algorithms; After the improvement of sag, the smoothness of microgrid switching had been increased, avoiding severe shaking. The convergence time was 68.3%, 54.5%, and 35.5% shorter, and the average convergence algebra was improved by 60.1%, 67.2%, and 87.3%, respectively; The control step response only approached 1.5 after 0.4 seconds, which was 75% longer than the improved time, and the overshoot was 0. Accordingly, the proposed method is able to utilizefor the regulation of actual microgrid grid-connected inverters, achieving effective voltage control in dynamic and complex environments.
Copyright (c) 2024 Yunqing Qu https://creativecommons.org/licenses/by-nc-sa/4.0
Optimized design and application of slot-type solar concentrator based on BIM technology
The application of BIM (Building Information Modeling) technology allows designers in the construction industry to complete their design work with lower energy consumption and higher efficiency and is therefore increasingly being used in other design fields. In this study, a finite element model of the concentrator was constructed using BIM technology to reduce the amount of steel used in the manufacture of the trough solar concentrator and thus reduce its production cost, and static analysis and steel minimization planning analysis was conducted. The analysis results show that the multiple linear regression algorithm is more reasonable to calculate the structural optimization evaluation index. The steel consumption of the *02 solution is lower, and the lowest values are 805. 962 kg and 3974. 9￥ for the total steel consumption and steel cost, respectively, which is 10. 99% lower than the steel cost of 4465. 5￥ for the concentrator before optimization. Although its maximum displacement and maximum stress are not the lowest among the alternatives, they are still far below the corresponding allowable values of the structure, so this solution has the best comprehensive optimization effect. The results of this research show that the application of BIM technology to the design of trough solar concentrators can indeed save equipment production materials and reduce production costs, and has certain application prospects.
Copyright (c) 2024 Miaoling Wang, Hao Wang https://creativecommons.org/licenses/by-nc-sa/4.0
Power System Operation Status Based on MRMR Algorithm and Multiple ELM
To validate the operation status of the power system after encountering faults and restoring it to equilibrium, an efficient and accurate evaluation method is raised to promote the accuracy and efficiency of operation status evaluation model. The study first introduced the minimum redundancy maximum correlation algorithm and multiple extreme learning machine, and then constructed a multi-layer evaluation model grounded on multiple extreme learning machine. The experiment findings indicated that 1225 samples were sent to the second layer after the first evaluation layer, and 531 samples were sent to the third layer after the second evaluation layer. Only 10 samples could not be evaluated at the fifth level. Moreover, there were only 2 cases of missed judgments in the fifth layer. The experiment data indicated that the probability of missed judgments in the hierarchical evaluation model was very small, and it could evaluate almost all samples. This demonstrates that the power system operation state evaluation method based on the minimum redundancy maximum correlation algorithm and multiple extreme learning machine proposed by the research can timely and effectively evaluate feature samples, providing strong support for the stable operation of the power system.
Copyright (c) 2024 Benkang Jia https://creativecommons.org/licenses/by-nc-sa/4.0
Smart Grid and Economic Growth: Driving Industrial Upgrading through Efficient Energy Management
INTRODUCTION: In an era marked by rising energy demand and environmental concerns, integrating smart grid technologies is a crucial solution for promoting industrial expansion through efficient electricity control. OBJECTIVES: Here, we examine the revolutionary capabilities of smart grids to enhance economic growth by leveraging an innovative hybrid optimisation method, Trevally Optimisation (TrevOpt), at its core. The study's main focus is to show how Turnbridge's new approach can efficiently manage energy distribution and use within industrial ecosystems by leveraging TrevOpt's computational power, which combines the benefits of evolutionary algorithms and heuristics. METHODS: The heart of this paper is a discussion of how the use of smart grid technologies can catalyse industrial development. Operation eff was on another level in this experiment, enabling the optimisation of production parameters using the TrevOpt model. Integrating real-time analytics into the TrevOpt framework allows proactive management of energy resources through dynamic tuning, thereby reducing waste and enhancing system reliability. RESULTS: This highlights the potential of these technologies to inspire industrial competitiveness, drive investment in sustainability, and open new areas in energy-intensive industries to spur economic growth. The simulation presented at the end of the numerical section outlines the concrete benefits of using the TrevOpt method, including a 20% reduction in energy consumption, a 15% reduction in operational costs, and a 25% increase in overall system reliability. CONCLUSION: This study therefore provides a solid foundation that enables industries to leverage smart grid developments as a cornerstone for transforming their businesses while also protecting the environments in which people live.
Copyright (c) 2025 Xilin Zhang, Kaiyuan Dong, Yujiao Mao, Yanhong Wang; Yaqian Liu https://creativecommons.org/licenses/by-nc-sa/4.0
Consistency Monitoring of Wind Turbine Blade Loads Based on MEMS Optical Fiber Sensing
INTRODUCTION: This paper presents a comprehensive structural monitoring framework for wind turbine blades based on MEMS-FBG (micro-electro-mechanical systems—fiber Bragg grating) sensor fusion technology. OBJECTIVES: The system integrates high-resolution strain and vibration sensing across multiple blade segments, combined with real-time data processing, fault detection, and SCADA-level visualization. METHODS: A multilayered load consistency model is introduced, incorporating thermal compensation, strain-to-load calibration, and a novel consistency index (η) to quantify inter-blade aerodynamic symmetry. RESULTS: Experimental validation was conducted on a 2.0 MW wind turbine over a 60-day continuous monitoring campaign. Static calibration demonstrated a load reconstruction accuracy within ±3%, while dynamic data revealed a strong correlation between load and blade vibration (R ≥ 0.86). CONCLUSION: Fault simulation through pitch angle manipulation confirmed the system’s rapid alarm response within 3 seconds for major asymmetry events. Additionally, signal drift testing showed MEMS-FBG sensors exhibited 80–95% lower drift than conventional resistance strain gauges under rotating and EMI-intensive conditions.
Copyright (c) 2024 Yong Xue, Yang Li, Xiangye Fan, Binshan Xie, Zhiyuan Ma, Lin Lin, Mengnan Cao https://creativecommons.org/licenses/by-nc-sa/4.0
Power System Operation Stability Assessment Method Based on Deep Convolutional Neural Network
INTRODUCTION: For the assessment of power system stability, a power system assessment method based on a deep convolutional neural network is studied. OBJECTIVES: Through the improvement of the integrated convolutional neural network (CNN) network model, the impact of insufficient transient stability assessment caused by sample misjudgment and sample omission is effectively reduced. METHODS: We adopt the hierarchical real-time prediction model to evaluate the stability and instability of the determined stable samples and unstable samples, thereby improving the timeliness and accuracy of transient evaluation. RESULTS: Through experimental comparison, the integrated CNN network model in this study has obvious advantages in accuracy compared with the single CNN network. Compared with other algorithm reference models, this model has a higher evaluation accuracy of 98.39%, far exceeding other comparison models. CONCLUSION: By further evaluating the model’s accuracy, it is proved that the model can provide an effective reference for the follow-up power system stability prevention and has important application value.
Copyright (c) 2024 Jinman Luo, Yuqing Li, Qile Wang, Liyuan Liu https://creativecommons.org/licenses/by-nc-sa/4.0
Energy management system design for high energy consuming enterprises integrating the Internet of Things and neural networks
INTRODUCTION: High energy consuming enterprises continue to pay increasing attention to energy consumption. Therefore, designing an energy management system is significant. OBJECTIVES: To improve the management level and economic benefits of enterprises, a high energy consuming enterprise energy management system design based on Internet of Things technology and neural network algorithms is proposed. METHODS: Internet of Things devices are used for data collection and transmission. The combination of neural network model prediction and optimization algorithms can achieve real-time monitoring, prediction, and optimization control of energy consumption. RESULTS: The research results indicated that the response time of the high energy consuming enterprise energy management system proposed in the study was 80.2 ms when the number of people was 600. The fluctuation range of CPU usage within 24 hours was 14% to 45%. CONCLUSION: A high energy consuming enterprise energy management system that integrates the Internet of Things and neural networks can manage energy more efficiently and intelligently, thereby improving the production efficiency and economic benefits of the enterprise. This helps companies gain greater advantages in fierce market competition.
Intelligent Energy Monitoring System Using Internet of Things (IoT)
Copyright (c) 2024 Zhaolin Wang, Zhiping Zhang https://creativecommons.org/licenses/by-nc-sa/4.0