Overview of Signal Processing and Machine Learning for Smart Grid Condition Monitoring

Nowadays, the main grid is facing several challenges related to the integration of renewable energy resources, deployment of grid-level energy storage devices, deployment of new usages such as the electric vehicle, massive usage of power electronic devices at different electric grid stages and the i...

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Autores principales:	Elhoussin Elbouchikhi, Muhammad Fahad Zia, Mohamed Benbouzid, Soumia El Hani
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	smart grid resilience signal processing disturbance classification power quality PSD estimation Electronics TK7800-8360
Acceso en línea:	https://doaj.org/article/157a79923cfd410487edb9c702d4852c
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spelling	oai:doaj.org-article:157a79923cfd410487edb9c702d4852c2021-11-11T15:42:54ZOverview of Signal Processing and Machine Learning for Smart Grid Condition Monitoring10.3390/electronics102127252079-9292https://doaj.org/article/157a79923cfd410487edb9c702d4852c2021-11-01T00:00:00Zhttps://www.mdpi.com/2079-9292/10/21/2725https://doaj.org/toc/2079-9292Nowadays, the main grid is facing several challenges related to the integration of renewable energy resources, deployment of grid-level energy storage devices, deployment of new usages such as the electric vehicle, massive usage of power electronic devices at different electric grid stages and the inter-connection with microgrids and prosumers. To deal with these challenges, the concept of a smart, fault-tolerant, and self-healing power grid has emerged in the last few decades to move towards a more resilient and efficient global electrical network. The smart grid concept implies a bi-directional flow of power and information between all key energy players and requires smart information technologies, smart sensors, and low-latency communication devices. Moreover, with the increasing constraints, the power grid is subjected to several disturbances, which can evolve to a fault and, in some rare circumstances, to catastrophic failure. These disturbances include wiring issues, grounding, switching transients, load variations, and harmonics generation. These aspects justify the need for real-time condition monitoring of the power grid and its subsystems and the implementation of predictive maintenance tools. Hence, researchers in industry and academia are developing and implementing power systems monitoring approaches allowing pervasive and effective communication, fault diagnosis, disturbance classification and root cause identification. Specifically, a focus is placed on power quality monitoring using advanced signal processing and machine learning approaches for disturbances characterization. Even though this review paper is not exhaustive, it can be considered as a valuable guide for researchers and engineers who are interested in signal processing approaches and machine learning techniques for power system monitoring and grid-disturbance classification purposes.Elhoussin ElbouchikhiMuhammad Fahad ZiaMohamed BenbouzidSoumia El HaniMDPI AGarticlesmart gridresiliencesignal processingdisturbance classificationpower qualityPSD estimationElectronicsTK7800-8360ENElectronics, Vol 10, Iss 2725, p 2725 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	smart grid resilience signal processing disturbance classification power quality PSD estimation Electronics TK7800-8360
spellingShingle	smart grid resilience signal processing disturbance classification power quality PSD estimation Electronics TK7800-8360 Elhoussin Elbouchikhi Muhammad Fahad Zia Mohamed Benbouzid Soumia El Hani Overview of Signal Processing and Machine Learning for Smart Grid Condition Monitoring
description	Nowadays, the main grid is facing several challenges related to the integration of renewable energy resources, deployment of grid-level energy storage devices, deployment of new usages such as the electric vehicle, massive usage of power electronic devices at different electric grid stages and the inter-connection with microgrids and prosumers. To deal with these challenges, the concept of a smart, fault-tolerant, and self-healing power grid has emerged in the last few decades to move towards a more resilient and efficient global electrical network. The smart grid concept implies a bi-directional flow of power and information between all key energy players and requires smart information technologies, smart sensors, and low-latency communication devices. Moreover, with the increasing constraints, the power grid is subjected to several disturbances, which can evolve to a fault and, in some rare circumstances, to catastrophic failure. These disturbances include wiring issues, grounding, switching transients, load variations, and harmonics generation. These aspects justify the need for real-time condition monitoring of the power grid and its subsystems and the implementation of predictive maintenance tools. Hence, researchers in industry and academia are developing and implementing power systems monitoring approaches allowing pervasive and effective communication, fault diagnosis, disturbance classification and root cause identification. Specifically, a focus is placed on power quality monitoring using advanced signal processing and machine learning approaches for disturbances characterization. Even though this review paper is not exhaustive, it can be considered as a valuable guide for researchers and engineers who are interested in signal processing approaches and machine learning techniques for power system monitoring and grid-disturbance classification purposes.
format	article
author	Elhoussin Elbouchikhi Muhammad Fahad Zia Mohamed Benbouzid Soumia El Hani
author_facet	Elhoussin Elbouchikhi Muhammad Fahad Zia Mohamed Benbouzid Soumia El Hani
author_sort	Elhoussin Elbouchikhi
title	Overview of Signal Processing and Machine Learning for Smart Grid Condition Monitoring
title_short	Overview of Signal Processing and Machine Learning for Smart Grid Condition Monitoring
title_full	Overview of Signal Processing and Machine Learning for Smart Grid Condition Monitoring
title_fullStr	Overview of Signal Processing and Machine Learning for Smart Grid Condition Monitoring
title_full_unstemmed	Overview of Signal Processing and Machine Learning for Smart Grid Condition Monitoring
title_sort	overview of signal processing and machine learning for smart grid condition monitoring
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/157a79923cfd410487edb9c702d4852c
work_keys_str_mv	AT elhoussinelbouchikhi overviewofsignalprocessingandmachinelearningforsmartgridconditionmonitoring AT muhammadfahadzia overviewofsignalprocessingandmachinelearningforsmartgridconditionmonitoring AT mohamedbenbouzid overviewofsignalprocessingandmachinelearningforsmartgridconditionmonitoring AT soumiaelhani overviewofsignalprocessingandmachinelearningforsmartgridconditionmonitoring
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Overview of Signal Processing and Machine Learning for Smart Grid Condition Monitoring

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