Neural Network-based Approach for Determining Optimal Reference Compensation Current of Shunt Active Power Filter

摘要

With the increasing use of non-linear loads, the harmonic problems have become of great concerns. One of the solutions to harmonics is the application of shunt active power filters (APFs). The APF reference compensation strategy proposed in this paper adopts the backpropagation neural network (BPNN) to train the control algorithm and calculate the reference compensation current under steady state and varying load levels. Simulation results obtained by using MATLAB/Simulink show that the proposed BPNN-based APF control strategy can effectively mitigate harmonic currents generated by the nonlinear load.