RBF neural network based predictive control of active power filter

A RBF neural network based predictive control of active power filter is presented in this paper. RBF neural network is employed to predict future harmonic compensating current. In order to make the predictive model much simpler and tighter, an adaptive learning algorithm for RBF network is proposed. 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. The RBF 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 the RBF neural network based predictive control eliminates supply current and voltage harmonics greatly and is more effective than PI control.