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Signal-piloted processing and machine learning based efficient power quality disturbances recognition

Significant losses can occur for various smart grid stake holders due to the Power Quality Disturbances (PQDs). Therefore, it is necessary to correctly recognize and timely mitigate the PQDs. In this context, an emerging trend is the development of machine learning assisted PQDs management. Based on...

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Autor principal: Mian Qaisar, Saeed
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8162588/
https://www.ncbi.nlm.nih.gov/pubmed/34048442
http://dx.doi.org/10.1371/journal.pone.0252104
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author Mian Qaisar, Saeed
author_facet Mian Qaisar, Saeed
author_sort Mian Qaisar, Saeed
collection PubMed
description Significant losses can occur for various smart grid stake holders due to the Power Quality Disturbances (PQDs). Therefore, it is necessary to correctly recognize and timely mitigate the PQDs. In this context, an emerging trend is the development of machine learning assisted PQDs management. Based on the conventional processing theory, the existing PQDs identification is time-invariant. It can result in a huge amount of unnecessary information being collected, processed, and transmitted. Consequently, needless processing activities, power consumption and latency can occur. In this paper, a novel combination of signal-piloted acquisition, adaptive-rate segmentation and time-domain features extraction with machine learning tools is suggested. The signal-piloted acquisition and processing brings real-time compression. Therefore, a remarkable reduction can be secured in the data storage, processing and transmission requirement towards the post classifier. Additionally, a reduced computational cost and latency of classifier is promised. The classification is accomplished by using robust machine learning algorithms. A comparison is made among the k-Nearest Neighbor, Naïve Bayes, Artificial Neural Network and Support Vector Machine. Multiple metrics are used to test the success of classification. It permits to avoid any biasness of findings. The applicability of the suggested approach is studied for automated recognition of the power signal’s major voltage and transient disturbances. Results show that the system attains a 6.75-fold reduction in the collected information and the processing load and secures the 98.05% accuracy of classification.
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spelling pubmed-81625882021-06-10 Signal-piloted processing and machine learning based efficient power quality disturbances recognition Mian Qaisar, Saeed PLoS One Research Article Significant losses can occur for various smart grid stake holders due to the Power Quality Disturbances (PQDs). Therefore, it is necessary to correctly recognize and timely mitigate the PQDs. In this context, an emerging trend is the development of machine learning assisted PQDs management. Based on the conventional processing theory, the existing PQDs identification is time-invariant. It can result in a huge amount of unnecessary information being collected, processed, and transmitted. Consequently, needless processing activities, power consumption and latency can occur. In this paper, a novel combination of signal-piloted acquisition, adaptive-rate segmentation and time-domain features extraction with machine learning tools is suggested. The signal-piloted acquisition and processing brings real-time compression. Therefore, a remarkable reduction can be secured in the data storage, processing and transmission requirement towards the post classifier. Additionally, a reduced computational cost and latency of classifier is promised. The classification is accomplished by using robust machine learning algorithms. A comparison is made among the k-Nearest Neighbor, Naïve Bayes, Artificial Neural Network and Support Vector Machine. Multiple metrics are used to test the success of classification. It permits to avoid any biasness of findings. The applicability of the suggested approach is studied for automated recognition of the power signal’s major voltage and transient disturbances. Results show that the system attains a 6.75-fold reduction in the collected information and the processing load and secures the 98.05% accuracy of classification. Public Library of Science 2021-05-28 /pmc/articles/PMC8162588/ /pubmed/34048442 http://dx.doi.org/10.1371/journal.pone.0252104 Text en © 2021 Saeed Mian Qaisar https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Mian Qaisar, Saeed
Signal-piloted processing and machine learning based efficient power quality disturbances recognition
title Signal-piloted processing and machine learning based efficient power quality disturbances recognition
title_full Signal-piloted processing and machine learning based efficient power quality disturbances recognition
title_fullStr Signal-piloted processing and machine learning based efficient power quality disturbances recognition
title_full_unstemmed Signal-piloted processing and machine learning based efficient power quality disturbances recognition
title_short Signal-piloted processing and machine learning based efficient power quality disturbances recognition
title_sort signal-piloted processing and machine learning based efficient power quality disturbances recognition
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8162588/
https://www.ncbi.nlm.nih.gov/pubmed/34048442
http://dx.doi.org/10.1371/journal.pone.0252104
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