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AUTHOR(S): Abderrahmane El Rhatrif, Bouchra Bouihi, Mohammed Mestari
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TITLE Ai-based Solutions for Grid Stability and Efficiency: Challenges, Limitations, and Opportunities |
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ABSTRACT The complexity of managing power systems is increasing because of the dynamics and uncertainty introduced by the global transition toward deep decarbonisation as well as the growing volume of multidimensional data requiring rapid response times. Modern power grids face significant challenges owing to the increased penetration of distributed energy resources (DER). These challenges include maintaining power system stability, ensuring the proper functioning of protection and control systems, and balancing the supply and demand. Current technologies are insufficient to handle these future complexities. AI and ML technologies have emerged as transformative tools that offer new opportunities to improve the efficiency, reliability, and innovation in power system planning and operation. This study examines recent and representative academic research on state-of-the-art AI/ML techniques applied to modern power systems, and examines their application across various power system domains, including fault detection, asset management, predictive maintenance, and oscillation detection. Despite the promising potential of AI to enhance the stability and protection of power systems, its limitations must be recognised. Key barriers to practical AI implementation were analysed, including reliance on synthetic data, scarcity of real measurement data, issues with protection selectivity, and the black-box nature of AI models. Factors such as safety, security, transparency, and trustworthiness are crucial for successful implementation and adoption of AI/ML solutions. To overcome these limitations, this study emphasises the need for further research on Explainable AI (XAI) and physics-informed machine learning (ML) to enhance the transparency and reliability of AI applications in power grids. The study also underscores the importance of advanced human–machine interfaces, which allow human operators to validate AI/ML solutions, thereby fostering trust and ensuring the effective deployment of these technologies in modern power systems. |
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KEYWORDS artificial intelligence; machine learning; power system; distributed energy resources; power system protection; power system stability; explainability AI; Inverter based resources |
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Cite this paper Abderrahmane El Rhatrif, Bouchra Bouihi, Mohammed Mestari. (2024) Ai-based Solutions for Grid Stability and Efficiency: Challenges, Limitations, and Opportunities. International Journal of Internet of Things and Web Services, 9, 16-28 |
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