基于BP神经网络递推积分PI-重复控制在微电网APF中的研究

为改善有源滤波器(APF)在微电网运行中对谐波起到抑制作用及补偿电能质量的能力,提出了一种适用于微电网有源电力滤波器中的BP神经网络递推积分PI-重复控制策略。结合并联型APF拓扑结构,在传统PI控制算法基础之上引进BP神经网络算法及递推积分函数,根据跟踪误差变化趋向将PI控制参数进行实时、快速整定,进而满足最优化要求。并与重复控制进行并联,提高跟踪稳态误差能力,保证系统运行的稳定性。建立并联型APF仿真模型和实验装置,通过仿真对比验证了所提出的控制策略能够很好地提高响应速度和补偿精度,提高了有源电力滤波器的鲁棒性。

Research on recursive integral PI-repetitive control based on BP neural network in micro-grid APF

In order to improve the ability of the Active Power Filter (APF) to suppress the harmonics and compensate the power quality in the operation of the micro-grid, a recursive integral PI-repetitive control strategy for BP neural network for APF in micro-grid is proposed. Combined with the parallel APF topology, BP neural network and recursive integral function are introduced on the basis of traditional PI control. According to the trend of tracking error change, the PI control parameters are adjusted in real time and quickly, and then the optimization requirements are met. In parallel with the repetitive control, the ability of tracking steady state error is improved to ensure the stability of the system operation. The parallel APF simulation model and experimental device are established. Simulation results show that the proposed control strategy can improve the response speed and the compensation precision, improving the robustness of active power filter. This work is supported by Foundation of Liaoning Education Department (No. LJYL016).