An Action Dependent Heuristic Dynamic Programming-controlled Superconducting Magnetic Energy Storage for Transient Stability Augmentation

To enhance the stability of power system, the active power and reactive power can be absorbed from or released to Superconducting magnetic energy storage (SMES) unit according to system power requirements. This paper proposes a control strategy based on action dependent heuristic dynamic programing (ADHDP) which can control SMES to improve the stability of electric power system with on-line learning ability. Based on back propagation (BP) neural network, ADHDP approximates the optimal control solution of nonlinear system through iteration step by step. This on-line learning ability improves its performance by learning from its own mistakes through reinforcement signal from external environment, so that it can adjust the neural network weights according to the back propagation error to achieve optimal control performance. To investigate the effectiveness of the proposed control strategy, simulation tests are carried out in Matlab/Simulink. And a conventional Proportional-Integral (PI) controlled method is used to compare the performance of ADHDP. Simulation results show that the proposed controller demonstrates superior damping performance on power system oscillation caused by three-phase fault and wind power fluctuation over the PI controller.