A Comprehensive Review on the Role of Artificial Intelligence in Power System Stability, Control, and Protection: Insights and Future Directions
Source abstract
Abstract
This review comprehensively examines the burgeoning field of intelligent techniques to enhance power systems’ stability, control, and protection. As global energy demands increase and renewable energy sources become more integrated, maintaining the stability and reliability of both conventional power systems and smart grids is crucial. Traditional methods are increasingly insufficient for handling today’s power grids’ complex, dynamic nature. This paper discusses the adoption of advanced intelligence methods, including artificial intelligence (AI), deep learning (DL), machine learning (ML), metaheuristic optimization algorithms, and other AI techniques such as fuzzy logic, reinforcement learning, and model predictive control to address these challenges. It underscores the critical importance of power system stability and the new challenges of integrating diverse energy sources. The paper reviews various intelligent methods used in power system analysis, emphasizing their roles in predictive maintenance, fault detection, real-time control, and monitoring. It details extensive research on the capabilities of AI and ML algorithms to enhance the precision and efficiency of protection systems, showing their effectiveness in accurately identifying and resolving faults. Additionally, it explores the potential of fuzzy logic in decision-making under uncertainty, reinforcement learning for dynamic stability control, and the integration of IoT and big data analytics for real-time system monitoring and optimization. Case studies from the literature are presented, offering valuable insights into practical applications. The review concludes by identifying current limitations and suggesting areas for future research, highlighting the need for more robust, flexible, and scalable intelligent systems in the power sector. This paper is a valuable resource for researchers, engineers, and policymakers, providing a detailed understanding of the current and future potential of intelligent techniques in power system stability, control, and protection.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Power System Optimization and Stability · Energy Load and Power Forecasting · Power Systems Fault Detection
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
British Malaysian Institute, Universiti Kuala Lumpur, Bt. 8, Jalan Sungai Pusu, Gombak 53100, Selangor, Malaysia
School of Electrical and Electronic Engineering, Universiti Sains Malaysia (USM), Nibong Tebal 14300, Penang, Malaysia
Libyan Authority for Scientific Research, Tripoli P.O. Box 80045, Libya
Department of Electrical Engineering, College of Engineering, University of Baghdad, Baghdad 10001, Iraq
Department of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU), 7034 Trondheim, Norway
Faculty of New Sciences and Technologies, University of Tehran, Tehran 1417935840, Iran
School of Engineering & Technology, Duy Tan University, Da Nang 550000, Vietnam
Research and Development Cell, Lovely Professional University, Phagwara 144411, India
Funding
Universiti Kuala Lumpur
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