基于卡尔曼滤波器及深度强化学习的双有源全桥变换器控制策略

Research on Control Strategy of Dual-active Full-bridge Converter Based on Deep Reinforcement Learning and Kalman Filter

高比例的新能源以及随机性负载大量接入微电网,其不确定性带来的大扰动对直流母线电压的稳定性造成不良影响。为了实现大扰动下快速、自适应的电压调节,针对双有源桥式DC-DC变换器(dualactivebridge,DAB)提出了一种基于卡尔曼滤波器(Kalmanfilter,KF)及深度强化学习的新型复合控制策略。设计了基于Actor-Critic架构的深度确定性策略梯度强化学习智能体,采用KF的最佳观测结果作为前馈补偿提高输出电压调节的准确性,通过在线学习自动调整DAB变换器的控制参数,保证直流变换器在面临系统各种扰动问题时均保持稳定,最后通过仿真和实验验证了该控制策略的有效性。

A large proportion of new energy and random loads are connected to microgrids,and the large disturbance caused by its uncertainty has a negative impact on the stability of DC bus voltage.In order to realize fast adaptive voltage regulation under large disturbance,a new compound control strategy based on Kalman filter(KF)and deep reinforcement learning is proposed for dual-active bridge(DAB)DC-DC converter.A deep deterministic strategy gradient reinforcement learning agent based on the Actor-Critic architecture is designed.The best observation results of KF are used as feed-forward compensation to improve the accuracy of output voltage regulation.The control parameters of DAB con-verter are automatically adjusted through online learning to ensure that the DC converter is stable in the face of various system disturbances.Finally,the effectiveness of the control strategy is verified by simulation and experiments.