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Enhancing Power Grid Resilience through Real-Time Fault Detection and Remediation Using Advanced Hybrid Machine Learning Models

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  • Fahad M. Almasoudi

    (Department of Electrical Engineering, Faculty of Engineering, University of Tabuk, Tabuk 47913, Saudi Arabia)

Abstract

Ensuring a reliable and uninterrupted supply of electricity is crucial for sustaining modern and advanced societies. Traditionally, power systems analysis was mostly dependent on formal commercial software, mathematical models produced via a mix of data analysis, control theory, and statistical methods. As power grids continue to grow and the need for more efficient and sustainable energy systems arises, attention has shifted towards incorporating artificial intelligence (AI) into traditional power grid systems, making their upgrade imperative. AI-based prediction and forecasting techniques are now being utilized to improve power production, transmission, and distribution to industrial and residential consumers. This paradigm shift is driven by the development of new methods and technologies. These technologies enable faster and more accurate fault prediction and detection, leading to quicker and more effective fault removal. Therefore, incorporating AI in modern power grids is critical for ensuring their resilience, efficiency, and sustainability, ultimately contributing to a cleaner and greener energy future. This paper focuses on integrating artificial intelligence (AI) in modern power generation grids, particularly in the fourth industrial revolution (4IR) context. With the increasing complexity and demand for more efficient and reliable power systems, AI has emerged as a possible approach to solve these difficulties. For this purpose, real-time data are collected from the user side, and internal and external grid faults occurred during a time period of three years. Specifically, this research delves into using state-of-the-art machine learning hybrid models at end-user locations for fault prediction and detection in electricity grids. In this study, hybrid models with convolution neural networks (CNN) have been developed, such as CNN-RNN, CNN-GRU, and CNN-LSTM. These approaches are used to explore how these models can automatically identify and diagnose faults in real-time, leading to faster and more effective fault detection and removal with minimum losses. By leveraging AI technology, modern power grids can become more resilient, efficient, and sustainable, ultimately contributing to a cleaner and greener energy future.

Suggested Citation

  • Fahad M. Almasoudi, 2023. "Enhancing Power Grid Resilience through Real-Time Fault Detection and Remediation Using Advanced Hybrid Machine Learning Models," Sustainability, MDPI, vol. 15(10), pages 1-21, May.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:10:p:8348-:d:1152000
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    References listed on IDEAS

    as
    1. Fahad M. Almasoudi, 2023. "Grid Distribution Fault Occurrence and Remedial Measures Prediction/Forecasting through Different Deep Learning Neural Networks by Using Real Time Data from Tabuk City Power Grid," Energies, MDPI, vol. 16(3), pages 1-20, January.
    2. Amar Kumar Barik & Dulal Chandra Das & Abdul Latif & S. M. Suhail Hussain & Taha Selim Ustun, 2021. "Optimal Voltage–Frequency Regulation in Distributed Sustainable Energy-Based Hybrid Microgrids with Integrated Resource Planning," Energies, MDPI, vol. 14(10), pages 1-26, May.
    3. Wan Chen & Baolian Liu & Muhammad Shahzad Nazir & Ahmed N. Abdalla & Mohamed A. Mohamed & Zujun Ding & Muhammad Shoaib Bhutta & Mehr Gul, 2022. "An Energy Storage Assessment: Using Frequency Modulation Approach to Capture Optimal Coordination," Sustainability, MDPI, vol. 14(14), pages 1-15, July.
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    Cited by:

    1. Ulaa AlHaddad & Abdullah Basuhail & Maher Khemakhem & Fathy Elbouraey Eassa & Kamal Jambi, 2023. "Towards Sustainable Energy Grids: A Machine Learning-Based Ensemble Methods Approach for Outages Estimation in Extreme Weather Events," Sustainability, MDPI, vol. 15(16), pages 1-19, August.
    2. Mubarak Alrumaidhi & Mohamed M. G. Farag & Hesham A. Rakha, 2023. "Comparative Analysis of Parametric and Non-Parametric Data-Driven Models to Predict Road Crash Severity among Elderly Drivers Using Synthetic Resampling Techniques," Sustainability, MDPI, vol. 15(13), pages 1-30, June.
    3. Kamil Prokop & Andrzej Bień & Szymon Barczentewicz, 2023. "Compression Techniques for Real-Time Control and Non-Time-Critical Big Data in Smart Grids: A Review," Energies, MDPI, vol. 16(24), pages 1-26, December.

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