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:	Saeed Mian Qaisar
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7036da8f36594d67a5dbe17e9ed074b1
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spelling	oai:doaj.org-article:7036da8f36594d67a5dbe17e9ed074b12021-12-02T20:05:27ZSignal-piloted processing and machine learning based efficient power quality disturbances recognition.1932-620310.1371/journal.pone.0252104https://doaj.org/article/7036da8f36594d67a5dbe17e9ed074b12021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0252104https://doaj.org/toc/1932-6203Significant 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.Saeed Mian QaisarPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 5, p e0252104 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Saeed Mian Qaisar Signal-piloted processing and machine learning based efficient power quality disturbances recognition.
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.
format	article
author	Saeed Mian Qaisar
author_facet	Saeed Mian Qaisar
author_sort	Saeed Mian Qaisar
title	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_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_sort	signal-piloted processing and machine learning based efficient power quality disturbances recognition.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/7036da8f36594d67a5dbe17e9ed074b1
work_keys_str_mv	AT saeedmianqaisar signalpilotedprocessingandmachinelearningbasedefficientpowerqualitydisturbancesrecognition
_version_	1718375487968903168

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

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