Expert and intelligent systems for assessment and mitigation of cascading failures in smart grids: Research challenges and survey

Abstract

Blackouts can result from cascade failure events caused by network instability in smart grid power systems. Network operators can lessen the spread of these incidents throughout the electrical system by identifying and analyzing them early. To detect, examine, and stop these failures, a variety of artificial intelligence-based techniques have been put forth; nonetheless, choosing the best strategy depending on network design continues to be a significant difficulty. With an emphasis on their advantages and disadvantages, this analysis examines cutting-edge approaches for evaluating and preventing cascading failures in smart grids. It looks at a wider range of methodologies than previous studies, such as digital twins, blockchain techniques, artificial intelligence (AI), probabilistic approaches, dynamic methods, quasi-steady-state methods, metaverse applications, and sophisticated control strategies. The article also identifies mitigation techniques that be included in smart grid infrastructure to stop cascading failures, such as optimal power flow algorithms driven by cutting-edge machine learning. This review facilitates the discovery of proactive approaches to detect and mitigate vulnerabilities early by offering researchers and network operators meaningful information through comparative analysis. These findings provide a substantial contribution to the body of knowledge in this area by guaranteeing the resilience of smart grids. To encourage developments in intelligent cascading failure management for smart grid systems, this paper also identifies research gaps and makes recommendations for possible future approaches.