A Review on Data-Driven Security Assessment of Power Systems: Trends and Applications of Artificial Intelligence

Abstract

Boosting the complexity of the electricity network, penetration of renewable resources, and modernization of power systems has resulted in an increase in the complexity of the power systems security assessment (PSSA). In this context, to decrease the vulnerability of the systems to multiple instability threats and security issues while ensuring the safe operation of the power systems, providing effective online security assessment methods capable of monitoring the systems' security under varying conditions is vital. However, although the traditional methods have demonstrated efficient PSSA performance, intelligent data-driven approaches have effectively overcome the traditional approaches by delivering impressive and rapid PSSA performance. Artificial intelligence (AI) -based techniques are required to guarantee the efficient, optimal, and safe security assessment. The usage of AI is emphasized due to its computational speed for online performance and its flexibility for providing corrective actions for insecure operating conditions to achieve a seamless transition in power systems. In this review, various available data-driven methods in power system security are comprehensively reviewed into two primary classifications: static and dynamic security assessment. The evaluated study aims to highlight the merits and demerits of developed techniques as well as their limitations to provide decision-making assistant for future investigations.