EAI Endorsed Transactions on Energy Web

Enhancing Power Grid Reliability with AGC and PSO: Insights from the Timimoun Photovoltaic Park

2024-11-22T12:16:13+00:00

This article investigates the impact of integrating Variable Renewable Energy (VRE), specifically solar energy from the Timimoun Photovoltaic Park, on the PIAT electrical grid stability in southern Algeria. The study focuses on how fluctuations in power demand and changes in weather conditions can affect grid frequency control, potentially leading to transient stability issues. To address these challenges, the research proposes the implementation of an Automatic Generation Control (AGC) system combined with the Particle Swarm Optimization (PSO) algorithm to optimize solar energy distribution. This approach effectively regulates real-time frequency deviations resulting from VRE integration, ensuring balanced supply and demand, and controllable power factor injection. The findings demonstrate that the integration of AGC and PSO stabilizes the frequency at the Timimoun Photovoltaic Park and reduces total active losses in the PIAT network by 13.88%. Additionally, strategic power factor control at the injection buses ensures optimal power quality and maximizes the utilization of the photovoltaic park, leading to a 4.84% reduction in the PIAT grid's reliance on gas turbines. This approach contributes to lowering operational costs, reducing carbon emissions, and supporting a transition to greener energy.

Construction of a Fast Monitoring System for Electric Energy Equipment Status Based on Data Mining

2024-04-23T06:26:03+00:00

In modern power system operation, it is crucial to achieve fast and accurate monitoring of the electrical equipment status. To achieve this fast and accurate detection, this study proposes a generative adversarial network that combines edge features to amplify and recognize infrared images of devices, aiming to improve the model’s training effect. This model extracted edge features from infrared images to eliminate background noise in infrared images to achieve the goal of improving the accurate monitoring of the status of electrical equipment. The results showed that on the balanced dataset, the recognition accuracy of the model could reach about 96%, and the recognition effect of the model was relatively stable. On imbalanced datasets, the highest model recognition accuracy was around 89%, and the model recognition accuracy fluctuated greatly. The constructed model effectively improves the accuracy of monitoring the operating status of electric energy equipment, achieving fast and accurate monitoring of this state. This study can achieve rapid monitoring of the operating status of electric energy equipment, effectively reducing the operation and maintenance costs of the power system.

Improving Fault Classification Accuracy Using Wavelet Transform and Random Forest with STATCOM Integration

2024-11-05T09:03:23+00:00

INTRODUCTION: Fault detection in transmission lines is critical for keeping the grid stable and reliable. This research offers a new methodology, the Wavelet Transform-Enhanced Random Forest Fault Classification System with STATCOM Integration (WERFCS-SI), to solve the shortcomings of existing fault detection approaches.

OBJECTIVES: The integration of STATCOM-compensated transmission lines improves fault detection capabilities. The Wavelet Transform finds faults by analysing approximation and detail coefficients, allowing for multiresolution analysis and exact fault localisation.

METHODS: Feature selection approaches, such as information gain, are used to discover and keep relevant features, increasing classification accuracy.

RESULTS: Due to its ability to process complex, high-dimensional data and identify minute feature connections, Random Forest (RF) is utilised for classification tasks. The proposed approach improves RF model performance while maintaining precision.

CONCLUSION: The integrated technique simplifies fault categorisation, increasing accuracy and efficiency by detecting problems in the transmission line system.

CNN Based Fault Classification and Predition of 33kw Solar PV System with IoT Based Smart Data Collection Setup

2024-12-12T12:31:44+00:00

A Solar Photovoltaic (PV) System is an energy conversion system that uses the photovoltaic effect to convert sunlight into electricity. A fault in a Solar Photovoltaic (PV) system refers to any abnormal condition or defect that disrupts the normal operation and performance of the solar system. These faults can arise from a variety of factors, including environmental conditions, manufacturing defects, installation errors, and wear and tear of the components. Fault diagnosis in solar PV systems involves the detection, identification, and rectification of faults or abnormalities that can occur due to various reasons. By detecting and addressing faults early, systems can maintain optimal performance levels. Machine Learning (ML) in Solar Photovoltaic (PV) systems refers to the application of algorithms and statistical models that enable computers to perform specific tasks without using explicit instructions, relying instead on patterns and inference. In the context of solar PV systems, ML is used to analyse and interpret vast amounts of data generated by these systems to enhance their efficiency, predict energy production, detect and diagnose faults, and optimize maintenance and operation. By analysing data from sensors and system logs, ML algorithms can identify patterns indicative of faults or inefficiencies, such as shading, soiling, or equipment malfunctions, often before they become serious issues. Convolutional Neural Networks (CNNs) are a class of deep learning algorithms most commonly applied. They are particularly powerful for tasks involving data recognition, classification, and analysis due to their ability to automatically and adaptively learn spatial hierarchies of features. This research presents a unique machine learning model based fault diagnosis and detection method for a 33 KW solar PV system at P.S.R. Engineering College, Sivakasi. The real-time data from the PV system for five years, covering 23,000 instances of eight types of faults such as Cell Cracks or Hot Spots, Partial Shading, sensor fault, Module failure, Ground Faults, Communication Errors, Environmental Factors, Grid Connectivity Issues are collected. CNN is applied to the data and analysed their performance in terms of accuracy, precision, and standard deviation (SD)-score. It is found that CNN achieved the best results, with an accuracy of 98.7% a precision of 95%, a recall of 98%, and an F1 score of 96.5%. Therefore, CNN is used as the fault prediction also. The model is implemented using Python programming language and demonstrated its effectiveness on test cases. The smart data gathering system was achieved utilizing an ESP32 node with several sensors. The obtained data was stored in an authorized Google Sheet and compared to predetermined threshold ranges. When any parameter deviates from its threshold value, the ESP32 node starts a cooling and dust cleaning procedure with a water pump and drip pipe configuration. If the divergence persists, the ESP32 node activates a camera to capture an image of the panel and sends it to the Google Sheet via a connection for further analysis and fault correction.

Investigating Safe and Economic Adjustment of Power Balance in Smart Grids Based on Integration of Renewable Energy

2024-12-19T12:14:59+00:00

The present pace of integration of renewable sources into the electrical grid is insufficient, failing to fulfill the expectations of producers or coincide with sustainable national objectives. Furthermore, sustainable national policies are not being executed. Despite the growth of the solar and wind energy industry and the installation of decentralized energy production systems, this scenario has emerged. Several factors contribute to this scenario, including advancements in administration, forecasting, and oversight, along with enhancements in infrastructure. These issues may arise notwithstanding the decentralized nature of renewable energy sources. The integration rate of renewable energy sources into networks, along with the efficiency of these networks, is clearly hindered as a result of this. Furthermore, we will examine the problems associated with the implementation of this network. We will focus on the low injection rate and the balance between supply and demand. Subsequently, we will examine the impact they have on the operation of the interconnected system. We will provide management solutions tailored to each detected issue, along with the suggested cures for any recognized concerns. The aim is to discover the structures, procedures, and tools that will enhance the network's reliability and energy efficiency while simultaneously reducing installation costs and fortifying the network. The findings indicate that the interruptions in voltage, frequency, and power have been mitigated due to the dynamic simulations using the proposed method. The calculations were predicated on an integration of solar and wind energy, with twenty percent of the energy derived from wind.

Fuzzy Allocation Optimization Algorithm for High-Density Storage Locations with Low Energy Consumptions

2024-11-04T15:16:08+00:00

The global demand for stored and processed data has surged due to the development of IoTs and similar computational structures, which has led to further energy consumption by concentrated data storage facilities and thus the demands of global energy and environmental needs. The current paper introduces Fuzzy Allocation Optimization Algorithm to mitigate energy consumption in high storage density settings. It uses the principles of Fuzzy logic to determine the best way to assign the tasks in relation to storage density necessity, urgency and energy consumption. Thus, the proposed approach incorporates fuzzy inference systems with multi-objective optimization methods where location of storage is dynamically assessed and assigned according to energy efficiency parameters. The findings of the simulation and case study prove that the algorithm is successful in saving energy while at the same time lowering storage I/O response time, which provides a viable solution to energy issues in evolving data centres. This work satisfies the lack of energy efficient algorithms in high density storage areas and responds to the recent calls for green technology and smart utilization of resources in the energy field. The findings are used in the promotion of significant IT infrastructures towards developing the next generation of energy efficient data centers with respect to Future Internet and evolving energy web environments.

Construction and Application Analysis of an Intelligent Distribution Network Identification System Based on Deep Neural Networks

2024-11-21T09:33:48+00:00

INTRODUCTION: At present, the communication between measuring data and network topology in the distribution system cannot be accurately established. Therefore, deep neural networks were utilized to learn the mapping relationship between the measurement data and network topology, achieving topology structure discrimination under different working conditions.

OBJECTIVES: This study aims to establish a machine learning-based Intelligent Distribution Network (IDN) online topology recognition model to address the limited measurement equipment in distribution networks and improve the accuracy and efficiency of network topology recognition.

METHODS: First, light GBM was used for feature selection to reduce computational complexity and improve learning efficiency. Then, a DNN model was constructed for topological identification and enhances the model scalability through incremental and transfer learning mechanisms. In addition, the Cross-Validation Grid Search Algorithm (GSA) was used to optimize the hyperparameters to ensure that the model can achieve the optimal performance on different data sets. Finally, a new intelligent distribution network identification model (Intelligent Distribution Electricity Network Identification System, IDENIS) was constructed.

RESULTS: The study was experimentally verified on the distribution system of IEEE 33 and PG&E 69. The experimental results showed that the accuracy of the DNN-based model reached 0.9817 on the test set, while the accuracy after feature selection only decreased by 1.3%, and the features decreased by 81.8%. In the PG&E 69 node system, the features were reduced by 85.5%, while the identification accuracy was decreased by only 0.51%. These results demonstrated that the proposed method maintained high identification accuracy while reducing the computational resource consumption.

CONCLUSION: Its efficient computing speed fully meets the real-time requirements in practical applications. This paper provides new ideas and methods for achieving intelligent distribution network topology recognition of high proportion distributed power sources.

Research on Fault Diagnosis Method for Photovoltaic Array Based on XGBoost Algorithm

2024-11-19T10:09:12+00:00

INTRODUCTION: Photovoltaic (PV) energy sources frequently experience issues, including fragmentation, open-circuit, short-circuiting, and other common and hazardous problems. The current focus of PV research is on fault detection within solar arrays. Traditional models encounter challenges in identifying errors due to uncertainties in panel settings and the complex nature of the actual PV structure.

OBJECTIVES: This study aims to introduce a novel Extreme Gradient Boosting (XGBoost) approach for fault diagnosis in PV arrays.

METHODS: The XGBoost algorithm is trained using collected PV array defect data samples. Data preprocessing is performed to manage missing values and remove noisy data. Feature extraction is conducted using Linear Discriminant Analysis (LDA) to improve detection accuracy. To further enhance XGBoost’s performance, the World Cup Optimization (WCO) approach is applied to select optimal features from the extracted data. Fault detection is then conducted using the XGBoost algorithm on the processed data. Various indicators are utilized for performance assessment within the Python environment.

RESULTS: The comparative analysis demonstrates that this research improves fault detection efficiency in PV arrays compared to existing methodologies.

CONCLUSION: The study presents an effective method for enhancing fault detection in PV systems, showcasing the advantages of the XGBoost and WCO-based approach over conventional methods.

Research on a New Maximum Power Tracking Algorithm for Photovoltaic Power Generation Systems

2024-11-19T08:58:13+00:00

INTRODUCTION: Significant advances have been made in photovoltaic (PV) systems, resulting in the development of new Maximum Power Point Tracking (MPPT) methods. The output of PV systems is heavily influenced by the varying performance of solar-facing PV panels under different weather conditions. Partial shading (PS) conditions pose additional challenges, leading to multiple peaks in the power-voltage (P-V) curve and reduced output power. Therefore, controlling MPPT under partial shading conditions is a complex task.

OBJECTIVES: This study aims to introduce a novel MMPT algorithm based on the ant colony incorporated bald eagle search optimization (AC-BESO) method to enhance the efficiency of PV systems.

METHODS: The effectiveness of the proposed MPPT algorithm was established through a series of experiments using MATLAB software, tested under various levels of solar irradiance.

RESULTS: Compared to existing methods, the proposed AC-BESO algorithm stands out for its simplicity in implementation and reduced computational complexity. Furthermore, its tracking performance surpasses that of conventional methods, as validated through comparative analyses.

CONCLUSION: This study confirms the efficacy of the AC-BESO method over traditional strategies. It serves as a framework for selecting an MPPT approach when designing PV systems.

Harmonic Measurement Algorithm of Power System Integrating Wavelet Transform and Deep Learning

2024-12-05T14:41:16+00:00

INTRODUCTION: The problem of low accuracy in harmonic measurement is a significant challenge in power systems. Traditional methods often exhibit higher measurement errors, leading to unreliable detection of harmonics. To address this, the author proposes a new approach that integrates wavelet transform and deep learning techniques for enhanced harmonic measurement accuracy.

OBJECTIVES: The primary goal of this study is to develop a more accurate harmonic measurement algorithm by combining full phase fast Fourier transform (FFT) and adaptive neural networks. The research aims to automatically detect power system harmonics with minimal error and improve upon the limitations of traditional methods.

METHODS: The study implemented a harmonic measurement method using full phase FFT integrated with an adaptive neural network. This approach calculates harmonic amplitudes based on the fundamental component and its amplitude, while determining the precise start and end times of harmonics. The system also incorporates mean filtering for automatic detection of harmonics. The effectiveness of the proposed method was evaluated through experiments that compared it to traditional harmonic measurement techniques.

RESULTS: Experimental results demonstrated that the proposed method achieved an average measurement error of 0.02V, with a maximum error of 0.03V, both of which are below the acceptable error limit. In contrast, traditional methods exhibited significantly higher average errors of 3.31V and a maximum error of 5.17V. The new method consistently showed higher accuracy in harmonic detection compared to conventional approaches.

CONCLUSION: The study concludes that the proposed harmonic measurement algorithm significantly improves accuracy compared to traditional methods. With its lower measurement error and effective automatic detection capabilities, the method proves to be highly suitable for harmonic measurement in power systems.