Artificial intelligence in renewable energy systems small signal stability control

Abstract

This chapter focuses on designing stability control methods based on artificial intelligence techniques for renewable energy integration systems. Among them, for the wind power grid-connected system, Sections 8.2 and 8.3 present the effects of different controllers (CPSS, STATCOM-ADC) on the low-frequency oscillation performance of the system and train the agents by reinforcement learning methods to realize adaptive control under different wind speed conditions, respectively. For the hydropower dominant system, Section 8.4 describes the mechanism of governor PID influence on ultra-low-frequency oscillations and the use of a Bi-level optimization strategy (the inner optimization is based on a reinforcement learning approach) to re-tune the PID parameters in order to ensure that the system still has a good stable performance under extreme operating conditions.