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In the current landscape, the accelerated evolution of Artificial Intelligence (AI) technology has garnered widespread attention and acclaim across various domains, owing to its remarkable performance in diverse applications. Power systems and critical infrastructures supporting the modern way of life are no exception to this transformative wave. This paper stands as the first comprehensive review of various AI techniques in power systems, spanning applications such as load forecasting, security assessment, voltage stability assessment, load shedding (LS), state estimation, false data injection attack (FDIA) detection and localization, fault detection and location, and power quality disturbances (PQDs) detection. In addressing the challenges inherent in the practical implementation of AI in power systems, this study introduces two potent tools: the strategic utilization of transfer learning (TL) in conjunction with AI algorithms and the leveraging of digital twin technology. The synergistic integration of these approaches remarkably enhances the performance and accuracy of AI models. Overall, this work aims to contribute to the foundational knowledge necessary for harnessing the full spectrum of AI's capabilities and to lay the foundation for a future of sustainable energy. Ultimately, it addresses emerging challenges and opens new perspectives for further research in the relevant field
Rahmani-Sane et al. (Sat,) studied this question.