Meticulously Intelligent Identification System for Smart Grid Network Stability to Optimize Risk Management

The heterogeneous and interoperable nature of the cyber-physical system (CPS) has enabled the smart grid (SG) to operate near the stability limits with an inconsiderable accuracy margin. This has imposed the need for more intelligent, predictive, fast, and accurate algorithms that are able to operat...

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Autores principales:	Qasem Abu Al-Haija, Abdallah A. Smadi, Mohammed F. Allehyani
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	identification accuracy identification overhead machine learning risk management smart grid support vector machines Technology T
Acceso en línea:	https://doaj.org/article/997783bd4e10461fb0c8f6c5b6f850ec
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spelling	oai:doaj.org-article:997783bd4e10461fb0c8f6c5b6f850ec2021-11-11T15:45:52ZMeticulously Intelligent Identification System for Smart Grid Network Stability to Optimize Risk Management10.3390/en142169351996-1073https://doaj.org/article/997783bd4e10461fb0c8f6c5b6f850ec2021-10-01T00:00:00Zhttps://www.mdpi.com/1996-1073/14/21/6935https://doaj.org/toc/1996-1073The heterogeneous and interoperable nature of the cyber-physical system (CPS) has enabled the smart grid (SG) to operate near the stability limits with an inconsiderable accuracy margin. This has imposed the need for more intelligent, predictive, fast, and accurate algorithms that are able to operate the grid autonomously to avoid cascading failures and/or blackouts. In this paper, a new comprehensive identification system is proposed that employs various machine learning architectures for classifying stability records in smart grid networks. Specifically, seven machine learning architectures are investigated, including optimizable support vector machine (SVM), decision trees classifier (DTC), logistic regression classifier (LRC), naïve Bayes classifier (NBC), linear discriminant classifier (LDC), k-nearest neighbor (kNN), and ensemble boosted classifier (EBC). The developed models are evaluated and contrasted in terms of various performance evaluation metrics such as accuracy, precision, recall, harmonic mean, prediction overhead, and others. Moreover, the system performance was evaluated on a recent and significant dataset for smart grid network stability (SGN_Stab2018), scoring a high identification accuracy (99.90%) with low identification overhead (4.17 μSec) for the optimizable SVM architecture. We also provide an in-depth description of our implementation in conjunction with an extensive experimental evaluation as well as a comparison with state-of-the-art models. The comparison outcomes obtained indicate that the optimized model provides a compact and efficient model that can successfully and accurately predict the voltage stability margin (VSM) considering different operating conditions, employing the fewest possible input features. Eventually, the results revealed the competency and superiority of the proposed optimized model over the other available models. The technique also speeds up the training process by reducing the number of simulations on a detailed power system model around operating points where correct predictions are made.Qasem Abu Al-HaijaAbdallah A. SmadiMohammed F. AllehyaniMDPI AGarticleidentification accuracyidentification overheadmachine learningrisk managementsmart gridsupport vector machinesTechnologyTENEnergies, Vol 14, Iss 6935, p 6935 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	identification accuracy identification overhead machine learning risk management smart grid support vector machines Technology T
spellingShingle	identification accuracy identification overhead machine learning risk management smart grid support vector machines Technology T Qasem Abu Al-Haija Abdallah A. Smadi Mohammed F. Allehyani Meticulously Intelligent Identification System for Smart Grid Network Stability to Optimize Risk Management
description	The heterogeneous and interoperable nature of the cyber-physical system (CPS) has enabled the smart grid (SG) to operate near the stability limits with an inconsiderable accuracy margin. This has imposed the need for more intelligent, predictive, fast, and accurate algorithms that are able to operate the grid autonomously to avoid cascading failures and/or blackouts. In this paper, a new comprehensive identification system is proposed that employs various machine learning architectures for classifying stability records in smart grid networks. Specifically, seven machine learning architectures are investigated, including optimizable support vector machine (SVM), decision trees classifier (DTC), logistic regression classifier (LRC), naïve Bayes classifier (NBC), linear discriminant classifier (LDC), k-nearest neighbor (kNN), and ensemble boosted classifier (EBC). The developed models are evaluated and contrasted in terms of various performance evaluation metrics such as accuracy, precision, recall, harmonic mean, prediction overhead, and others. Moreover, the system performance was evaluated on a recent and significant dataset for smart grid network stability (SGN_Stab2018), scoring a high identification accuracy (99.90%) with low identification overhead (4.17 μSec) for the optimizable SVM architecture. We also provide an in-depth description of our implementation in conjunction with an extensive experimental evaluation as well as a comparison with state-of-the-art models. The comparison outcomes obtained indicate that the optimized model provides a compact and efficient model that can successfully and accurately predict the voltage stability margin (VSM) considering different operating conditions, employing the fewest possible input features. Eventually, the results revealed the competency and superiority of the proposed optimized model over the other available models. The technique also speeds up the training process by reducing the number of simulations on a detailed power system model around operating points where correct predictions are made.
format	article
author	Qasem Abu Al-Haija Abdallah A. Smadi Mohammed F. Allehyani
author_facet	Qasem Abu Al-Haija Abdallah A. Smadi Mohammed F. Allehyani
author_sort	Qasem Abu Al-Haija
title	Meticulously Intelligent Identification System for Smart Grid Network Stability to Optimize Risk Management
title_short	Meticulously Intelligent Identification System for Smart Grid Network Stability to Optimize Risk Management
title_full	Meticulously Intelligent Identification System for Smart Grid Network Stability to Optimize Risk Management
title_fullStr	Meticulously Intelligent Identification System for Smart Grid Network Stability to Optimize Risk Management
title_full_unstemmed	Meticulously Intelligent Identification System for Smart Grid Network Stability to Optimize Risk Management
title_sort	meticulously intelligent identification system for smart grid network stability to optimize risk management
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/997783bd4e10461fb0c8f6c5b6f850ec
work_keys_str_mv	AT qasemabualhaija meticulouslyintelligentidentificationsystemforsmartgridnetworkstabilitytooptimizeriskmanagement AT abdallahasmadi meticulouslyintelligentidentificationsystemforsmartgridnetworkstabilitytooptimizeriskmanagement AT mohammedfallehyani meticulouslyintelligentidentificationsystemforsmartgridnetworkstabilitytooptimizeriskmanagement
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Meticulously Intelligent Identification System for Smart Grid Network Stability to Optimize Risk Management

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