Active disturbance rejection control strategy of a DC microgrid load interface converter based on a DQN algorithm
In the DC microgrid,to ensure the stability of the energy flow between the DC bus and the load,the disturbance problem caused by the uncertain factors in the energy flow is solved.Based on the mathematical model of DC-DC converter,an active disturbance rejection control strategy for a DC-DC converter based on deep reinforcement learning is designed.The active disturbance rejection control structure is simplified using the estimation compensation of the total disturbance and the linear error feedback control characteristics of the linear expansion observer,and the controller parameters are optimized online by deep reinforcement learning.From the load-side voltage waveform in different working conditions,the stability,immunity and robustness of the DC-DC converter using the control strategy,linear active disturbance rejection control and proportional integral control are analyzed,and the correctness and effectiveness of the control strategy are verified.Finally,Monte Carlo experiments are carried out under parameter perturbation,and the simulation results show that the control strategy has good robustness.
DC microgriddeep reinforcement learningdeep-Q-network(DQN)algorithmDC-DC converterslinear active disturbance rejection control
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维普
