Integrating discrete wavelet transform with neural networks and machine learning for fault detection in microgrids

Microgrids are essential for integrating renewable energy sources into the power grid. However, fault detection is challenging due to bidirectional energy flow. Traditional relay-based systems struggle in microgrids, primarily because of limited fault currents from grid-connected renewable energy in...

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Detalhes bibliográficos
Autores: Cano-Ortega, Antonio, Arévalo, Paul, Benavides, Darío, Jurado-Melguizo, Francisco
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2023
País:España
Recursos:Universidad de Jaén
Repositorio:RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
OAI Identifier:oai:ruja.ujaen.es:10953/4967
Acesso em linha:https://hdl.handle.net/10953/4967
Access Level:acceso abierto
Palavra-chave:Microgrids Fault detection
Machine learning
Discrete Wavelet transform
Artificial neural network
621.31
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repository_id_str
spelling Integrating discrete wavelet transform with neural networks and machine learning for fault detection in microgridsCano-Ortega, AntonioArévalo, PaulBenavides, DaríoJurado-Melguizo, FranciscoMicrogrids Fault detectionMachine learningDiscrete Wavelet transformArtificial neural network621.31Microgrids are essential for integrating renewable energy sources into the power grid. However, fault detection is challenging due to bidirectional energy flow. Traditional relay-based systems struggle in microgrids, primarily because of limited fault currents from grid-connected renewable energy inverters. To address these challenges, this paper proposes a new methodology for fault detection and classification in a renewable microgrid. The main contributions encompass two key aspects. Firstly, it enhances fault detection performance in microgrids characterized by nonlinear relationships, including photovoltaic, hydrokinetic, and variable electric load systems. Secondly, the combination of the discrete wavelet transform with various types of neural networks and supervised learning techniques provides a robust methodology for fault detection and classification. The proposed approach is evaluated using an IEEE-5 feeder test bed representing a realistic ring network configuration. The results show that the radial basis function neural network model exhibited promising outcomes, yielding a low prediction error of 1.31 e-31, highlighting its practical potential for enhancing system reliability and performance. Furthermore, various test cases were conducted by altering the ground resistance to train the neural networks, demonstrating the effectiveness of this neural network in accurately identifying fault conditions. Additionally, this research achieved promising outcomes with other models, including support vector machine and nonlinear autoregressive with external input, emphasizing the adaptability of these models in fault detection.Elsevier202520252023info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://hdl.handle.net/10953/4967reponame:RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaéninstname:Universidad de JaénInglésinfo:eu-repo/semantics/openAccessoai:ruja.ujaen.es:10953/49672026-06-24T12:41:07Z
dc.title.none.fl_str_mv Integrating discrete wavelet transform with neural networks and machine learning for fault detection in microgrids
title Integrating discrete wavelet transform with neural networks and machine learning for fault detection in microgrids
spellingShingle Integrating discrete wavelet transform with neural networks and machine learning for fault detection in microgrids
Cano-Ortega, Antonio
Microgrids Fault detection
Machine learning
Discrete Wavelet transform
Artificial neural network
621.31
title_short Integrating discrete wavelet transform with neural networks and machine learning for fault detection in microgrids
title_full Integrating discrete wavelet transform with neural networks and machine learning for fault detection in microgrids
title_fullStr Integrating discrete wavelet transform with neural networks and machine learning for fault detection in microgrids
title_full_unstemmed Integrating discrete wavelet transform with neural networks and machine learning for fault detection in microgrids
title_sort Integrating discrete wavelet transform with neural networks and machine learning for fault detection in microgrids
dc.creator.none.fl_str_mv Cano-Ortega, Antonio
Arévalo, Paul
Benavides, Darío
Jurado-Melguizo, Francisco
author Cano-Ortega, Antonio
author_facet Cano-Ortega, Antonio
Arévalo, Paul
Benavides, Darío
Jurado-Melguizo, Francisco
author_role author
author2 Arévalo, Paul
Benavides, Darío
Jurado-Melguizo, Francisco
author2_role author
author
author
dc.subject.none.fl_str_mv Microgrids Fault detection
Machine learning
Discrete Wavelet transform
Artificial neural network
621.31
topic Microgrids Fault detection
Machine learning
Discrete Wavelet transform
Artificial neural network
621.31
description Microgrids are essential for integrating renewable energy sources into the power grid. However, fault detection is challenging due to bidirectional energy flow. Traditional relay-based systems struggle in microgrids, primarily because of limited fault currents from grid-connected renewable energy inverters. To address these challenges, this paper proposes a new methodology for fault detection and classification in a renewable microgrid. The main contributions encompass two key aspects. Firstly, it enhances fault detection performance in microgrids characterized by nonlinear relationships, including photovoltaic, hydrokinetic, and variable electric load systems. Secondly, the combination of the discrete wavelet transform with various types of neural networks and supervised learning techniques provides a robust methodology for fault detection and classification. The proposed approach is evaluated using an IEEE-5 feeder test bed representing a realistic ring network configuration. The results show that the radial basis function neural network model exhibited promising outcomes, yielding a low prediction error of 1.31 e-31, highlighting its practical potential for enhancing system reliability and performance. Furthermore, various test cases were conducted by altering the ground resistance to train the neural networks, demonstrating the effectiveness of this neural network in accurately identifying fault conditions. Additionally, this research achieved promising outcomes with other models, including support vector machine and nonlinear autoregressive with external input, emphasizing the adaptability of these models in fault detection.
publishDate 2023
dc.date.none.fl_str_mv 2023
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/10953/4967
url https://hdl.handle.net/10953/4967
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
instname:Universidad de Jaén
instname_str Universidad de Jaén
reponame_str RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
collection RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.811543

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