Optimal control of power electronic systems based on deep learning

The goal of this study is to build an optimization control framework based on deep reinforcement learning (DRL), which is committed to enhancing the dynamic stability and anti-interference performance of the power system. The multi-band PSS2B controller is combined with the TCSC power oscillation damper (POD), and the deep Q learning algorithm is optimized for the coordination work, so that the performance of the power system is significantly enhanced in a complex operating environment. The proposed framework is simulated and tested with the help of the MATLAB/Simulink platform, and the influence of the interference type on the system stability is investigated. The results show that the control strategy adopted by the deep learning technology is better than the traditional control method in reducing the system transient overshoot, oscillation amplitude and shortening the steady-state time, especially when encountering large disturbances, it shows superior emergency response ability. The robustness and adaptability of the framework in the dynamic power system are further verified with the help of simulation analysis. The research conclusion shows that the framework has shown significant effects in improving the stability of the power system and has obvious practical value.