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To increase the output efficiency of a photovoltaic (PV) energy system, the real-time maximum power point (MPP) of the PV array must be tracked closely. Herein a recurrent fuzzy neural network controller (RFNNC) was proposed to track the MPP. A radial basis function neural network (RBFNN) was developed to provide the reference information to the RFNNC. With a derived learning algorithm, the parameters of the RFNNC were updated adaptively. The mean square error of the estimated tracking error is 0.4Ã10-2 which guarantees a good predicting performance of the RBFNN. The RFNNC only needs about 4 ms to reach steady state with small fluctuation. Compared with the fuzzy logic control algorithm, simulation results show that the proposed control algorithm yields much better tracking performance.
Natural Computation, 2009. ICNC '09. Fifth International Conference on (Volume:2 )
Date of Conference: 14-16 Aug. 2009