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

Author

Listed:
  • Qasem Abu Al-Haija

    (Department Computer Science/Cybersecurity, Princess Sumaya University for Technology (PSUT), Amman 11941, Jordan)

  • Abdallah A. Smadi

    (Department of Electrical and Computer Engineering, University of Idaho, Moscow, ID 83844, USA)

  • Mohammed F. Allehyani

    (Department of Electrical Engineering, University of Tabuk, Tabuk 47512, Saudi Arabia)

Abstract

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.

Suggested Citation

  • Qasem Abu Al-Haija & Abdallah A. Smadi & Mohammed F. Allehyani, 2021. "Meticulously Intelligent Identification System for Smart Grid Network Stability to Optimize Risk Management," Energies, MDPI, vol. 14(21), pages 1-19, October.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:21:p:6935-:d:661990
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    References listed on IDEAS

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    1. Heide, Dominik & von Bremen, Lueder & Greiner, Martin & Hoffmann, Clemens & Speckmann, Markus & Bofinger, Stefan, 2010. "Seasonal optimal mix of wind and solar power in a future, highly renewable Europe," Renewable Energy, Elsevier, vol. 35(11), pages 2483-2489.
    2. Walter M. Villa-Acevedo & Jesús M. López-Lezama & Delia G. Colomé, 2020. "Voltage Stability Margin Index Estimation Using a Hybrid Kernel Extreme Learning Machine Approach," Energies, MDPI, vol. 13(4), pages 1-19, February.
    3. Roman V. Kirin, 2021. "A Theoretical Analysis of Logistic Regression and Bayesian Classifiers," Papers 2108.03715, arXiv.org.
    4. Bassamzadeh, Nastaran & Ghanem, Roger, 2017. "Multiscale stochastic prediction of electricity demand in smart grids using Bayesian networks," Applied Energy, Elsevier, vol. 193(C), pages 369-380.
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    1. Mazen Gazzan & Frederick T. Sheldon, 2023. "Opportunities for Early Detection and Prediction of Ransomware Attacks against Industrial Control Systems," Future Internet, MDPI, vol. 15(4), pages 1-18, April.

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