Intelligent PV Power Smoothing Control Using Probabilistic Fuzzy Neural Network with Asymmetric Membership Function

Abstract

An intelligent PV power smoothing control using probabilistic fuzzy neural network with asymmetric membership function (PFNN-AMF) is proposed in this study. First, a photovoltaic (PV) power plant with a battery energy storage system (BESS) is introduced. The BESS consisted of a bidirectional DC/AC 3-phase inverter and LiFePO4 batteries. Then, the difference of the actual PV power and smoothed power is supplied by the BESS. Moreover, the network structure of the PFNN-AMF and its online learning algorithms are described in detail. Furthermore, the three-phase output currents of the PV power plant are converted to the dq-axis current components. The resulted q-axis current is the input of the PFNN-AMF power smoothing control, and the output is a smoothing PV power curve to achieve the effect of PV power smoothing. Comparing to the other smoothing methods, a minimum energy capacity of the BESS with a small fluctuation of the grid power can be achieved by the PV power smoothing control using PFNN-AMF. In addition, a personal computer- (PC-) based PV power plant emulator and BESS are built for the experimentation. From the experimental results of various irradiance variation conditions, the effectiveness of the proposed intelligent PV power smoothing control can be verified.