Power System Preventive Transient Stability Control: A Comprehensive Review

With the growing integration of renewable energy systems, energy storage systems, and other emerging technologies, the transient stability of power systems is challenged by increased uncertainties. Effective and real-time preventive control has become an important part of ensuring the transient stability of power systems. It can keep the system at the optimal operating point. Due to the high nonlinearity of power system dynamics, there has been a lot of research surrounding preventive transient stability control (PTSC) and related subproblems of transient stability assessment (TSA) and transient stability constrained-optimal power flow (TSC-OPF). This paper introduces the fundamental mathematical models of PTSC and provides a comprehensive review of its development and various approaches, aiming to provide valuable views to address the challenges faced today. That is, using machine learning methods to implement some of the best conventional methods and then combining them with advanced deep learning methods by means of algorithms such as transfer learning. Potential future research directions are also presented, which include modern power systems with high percentages of renewable energy injection and new types of loads.