Static Security Risk Assessment of Power Grid under Planned Maintenance

Due to the high probability of power flow violation during the N-1 static safety check under maintenance, and the risk assessment indicators are single and subjective, a method of static security risk assessment based on multi-source heterogeneous information for planned maintenance is proposed. By regularizing dispatcher experience, the static security factors are analyzed from equipment failure rate, electrical characteristics and grid topology, etc. Then, the selected indicators are preprocessed. Finally, Deep Belief Neural Network (DBNN) is used to evaluate risk. Through deep mining of the membership and mapping relationship between multiple indicators, the risk self-assessment is realized, and dispatcher is given the assistant decision when orderly adjusting equipment that causes limit. Simulation results of IEEE 39-bus show that the method proposed in this paper can quickly assess risk level without analysis of dispatcher; compared with DNN algorithm, DBNN has higher accuracy. The validity and feasibility of this research are proved.