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Article

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

1
Department Computer Science/Cybersecurity, Princess Sumaya University for Technology (PSUT), Amman 11941, Jordan
2
Department of Electrical and Computer Engineering, University of Idaho, Moscow, ID 83844, USA
3
Department of Electrical Engineering, University of Tabuk, Tabuk 47512, Saudi Arabia
*
Author to whom correspondence should be addressed.
Academic Editor: Reza Tourani
Energies 2021, 14(21), 6935; https://doi.org/10.3390/en14216935
Received: 14 September 2021 / Revised: 15 October 2021 / Accepted: 19 October 2021 / Published: 21 October 2021
(This article belongs to the Special Issue Emerging Network Architectures for Smart Grids)
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. View Full-Text
Keywords: identification accuracy; identification overhead; machine learning; risk management; smart grid; support vector machines; voltage stability; predictive model identification accuracy; identification overhead; machine learning; risk management; smart grid; support vector machines; voltage stability; predictive model
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MDPI and ACS Style

Abu Al-Haija, Q.; Smadi, A.A.; Allehyani, M.F. Meticulously Intelligent Identification System for Smart Grid Network Stability to Optimize Risk Management. Energies 2021, 14, 6935. https://doi.org/10.3390/en14216935

AMA Style

Abu Al-Haija Q, Smadi AA, Allehyani MF. Meticulously Intelligent Identification System for Smart Grid Network Stability to Optimize Risk Management. Energies. 2021; 14(21):6935. https://doi.org/10.3390/en14216935

Chicago/Turabian Style

Abu Al-Haija, Qasem, Abdallah A. Smadi, and Mohammed F. Allehyani. 2021. "Meticulously Intelligent Identification System for Smart Grid Network Stability to Optimize Risk Management" Energies 14, no. 21: 6935. https://doi.org/10.3390/en14216935

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