Power System Transient Stability Preventive Control Optimization Method Driven by Stacking Ensemble Learning
PAN Xiaojie,
XU Youping,
XIE Zhijun,
WANG Yukun,
ZHANG Mujie,
SHI Mengxuan,
MA Kun,
HU Wei
Affiliations
PAN Xiaojie
Central China Branch of State Grid Corporation of China, Wuhan 430077, Hubei Province, China
XU Youping
Central China Branch of State Grid Corporation of China, Wuhan 430077, Hubei Province, China
XIE Zhijun
State Key Lab of Control and Simulation of Power Systems and Generation Equipments (Department of Electrical Engineering and Applied Electronic Technology, Tsinghua University), Haidian District, Beijing 100084, China
WANG Yukun
Central China Branch of State Grid Corporation of China, Wuhan 430077, Hubei Province, China
ZHANG Mujie
Central China Branch of State Grid Corporation of China, Wuhan 430077, Hubei Province, China
SHI Mengxuan
Central China Branch of State Grid Corporation of China, Wuhan 430077, Hubei Province, China
MA Kun
State Key Lab of Control and Simulation of Power Systems and Generation Equipments (Department of Electrical Engineering and Applied Electronic Technology, Tsinghua University), Haidian District, Beijing 100084, China
HU Wei
State Key Lab of Control and Simulation of Power Systems and Generation Equipments (Department of Electrical Engineering and Applied Electronic Technology, Tsinghua University), Haidian District, Beijing 100084, China
Aiming at the contradiction between the rapidity requirement of online calculation of transient stability preventive control and the computational complexity of time-domain equations, a stacking ensemable learning driven optimization method for power system transient stability preventive control was proposed. Firstly, a transient stability estimator based on a stacking ensemble deep belief network was constructed to replace the nonlinear differential algebraic equation solution process required for transient stability determination. Secondly, the trained transient stability estimator was used as a transient stability constraint discriminator, which was embedded in the iterative optimization process of the Aptenodytes Forsteri optimization algorithm. Finally, with the goal of minimizing the cost of preventive control, a stacking ensemble learning driven power system transient stability preventive control optimization algorithm was established. The algorithm realized the efficient judgment of transient stability constraints in preventive control, and improved the decision-making level of preventive control for power generation rescheduling. Based on the IEEE39 bus system, the proposed preventive control method was verified by experiments. The results show that the method has achieved good results in both evaluation accuracy and calculation efficiency.
