Fuzzy Neural Network based Predictive Control for Active Power Filter

Fuzzy neural network based predictive control of active power filter is presented in this paper. In the scheme, B-spline membership fuzzy neural network is employed to predict future harmonic compensating current. In order to make the predictive model compact and accurate, a genetic algorithm with an efficient search strategy is developed to optimize the weighting factors, the knot positions and the control points of the B-spline membership fuzzy neural networks. Based on the model output, branch-and-bound optimization method is adopted to produce proper value of control vector. This control vector is adequately modulated by means of a space vector PWM modulator which generates proper gating patterns of the inverter switches to maintain tracking of reference current. Fuzzy neural network based predictive algorithm is used in internal model control scheme to compensate for process disturbances, measurement noise and modeling errors. Experiment on an actual system is implemented. The results show fuzzy neural network based predictive control eliminates supply current and is more effective than PI control.