Novel Static Security and Stability Control of Power Systems Based on Artificial Emotional Lazy Q-Learning

Abstract

The stability problem of power grids has become increasingly serious in recent years as the size of novel power systems increases. In order to improve and ensure the stable operation of the novel power system, this study proposes an artificial emotional lazy Q-learning method, which combines artificial emotion, lazy learning, and reinforcement learning for static security and stability analysis of power systems. Moreover, this study compares the analysis results of the proposed method with those of the small disturbance method for a stand-alone power system and verifies that the proposed lazy Q-learning method is able to effectively screen useful data for learning, and improve the static security stability of the new type of power system more effectively than the traditional proportional-integral-differential control and Q-learning methods.