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Machine Learning and Artificial Intelligence Techniques in Smart Grids Stability Analysis: A Review

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  • Arman Fathollahi

    (Department of Electrical and Computer Engineering, Aarhus University, 8200 Aarhus, Denmark)

Abstract

The incorporation of renewable energy sources in power grids has necessitated innovative solutions for effective energy management. Smart grids have emerged as transformative systems which integrate consumer, generator and dual-role entities to deliver secure, sustainable and economical electricity supplies. This review explores the important role of artificial intelligence and machine learning approaches in managing the developing stability characteristics of smart grids. This work starts with a discussion of the smart grid’s dynamic structures and subsequently transitions into an overview of machine learning approaches that explore various algorithms and their applications to enhance smart grid operations. A comprehensive analysis of frameworks illustrates how machine learning and artificial intelligence solve issues related to distributed energy supplies, load management and contingency planning. This review includes general pseudocode and schematic architectures of artificial intelligence and machine learning methods which are categorized into supervised, semi-supervised, unsupervised and reinforcement learning. It includes support vector machines, decision trees, artificial neural networks, extreme learning machines and probabilistic graphical models, as well as reinforcement strategies like dynamic programming, Monte Carlo methods, temporal difference learning and Deep Q-networks, etc. Examination extends to stability, voltage and frequency regulation along with fault detection methods that highlight their applications in increasing smart grid operational boundaries. The review underlines the various arrays of machine learning algorithms that emphasize the integration of reinforcement learning as a pivotal enhancement in intelligent decision-making within smart grid environments. As a resource this review offers insights for researchers, practitioners and policymakers by providing a roadmap for leveraging intelligent technologies in smart grid control and stability analysis.

Suggested Citation

  • Arman Fathollahi, 2025. "Machine Learning and Artificial Intelligence Techniques in Smart Grids Stability Analysis: A Review," Energies, MDPI, vol. 18(13), pages 1-33, June.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:13:p:3431-:d:1691188
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    References listed on IDEAS

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    1. 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.
    2. Ke Zhang & Zhi Hu & Yufei Zhan & Xiaofen Wang & Keyi Guo, 2020. "A Smart Grid AMI Intrusion Detection Strategy Based on Extreme Learning Machine," Energies, MDPI, vol. 13(18), pages 1-19, September.
    3. Deo, Ravinesh C. & Wen, Xiaohu & Qi, Feng, 2016. "A wavelet-coupled support vector machine model for forecasting global incident solar radiation using limited meteorological dataset," Applied Energy, Elsevier, vol. 168(C), pages 568-593.
    4. Bicer, Y. & Dincer, I. & Aydin, M., 2016. "Maximizing performance of fuel cell using artificial neural network approach for smart grid applications," Energy, Elsevier, vol. 116(P1), pages 1205-1217.
    5. Lasantha Meegahapola & Alfeu Sguarezi & Jack Stanley Bryant & Mingchen Gu & Eliomar R. Conde D. & Rafael B. A. Cunha, 2020. "Power System Stability with Power-Electronic Converter Interfaced Renewable Power Generation: Present Issues and Future Trends," Energies, MDPI, vol. 13(13), pages 1-35, July.
    6. Shi, Zhongtuo & Yao, Wei & Li, Zhouping & Zeng, Lingkang & Zhao, Yifan & Zhang, Runfeng & Tang, Yong & Wen, Jinyu, 2020. "Artificial intelligence techniques for stability analysis and control in smart grids: Methodologies, applications, challenges and future directions," Applied Energy, Elsevier, vol. 278(C).
    7. Yang, Yandong & Hong, Weijun & Li, Shufang, 2019. "Deep ensemble learning based probabilistic load forecasting in smart grids," Energy, Elsevier, vol. 189(C).
    8. 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.
    9. Tiago P. Abud & Andre A. Augusto & Marcio Z. Fortes & Renan S. Maciel & Bruno S. M. C. Borba, 2022. "State of the Art Monte Carlo Method Applied to Power System Analysis with Distributed Generation," Energies, MDPI, vol. 16(1), pages 1-24, December.
    10. Ahmed Sami Alhanaf & Hasan Huseyin Balik & Murtaza Farsadi, 2023. "Intelligent Fault Detection and Classification Schemes for Smart Grids Based on Deep Neural Networks," Energies, MDPI, vol. 16(22), pages 1-19, November.
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