The Applications of Artificial Intelligence and Digital Twin in Power Systems: An In-Depth Review

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 supporting modern 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), false data injection attack (FDIA) detection, state estimation 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 integration of these approaches substantially 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 in advancing the shift toward a sustainable energy future.