Neural network controlled grid interfaced solar photovoltaic power generation

Abstract

This study deals with a NN (neural-network)-based control algorithm of a grid interfaced SPV (solar photovoltaic) generating system. The proposed grid interfaced SPV generating system utilises a NN control algorithm-based on the LMS (least mean-square), known as Adaline (adaptive linear element) to estimate reference grid currents. A DC-DC boost converter is used for achieving the maximum power point tracking between SPV and DC bus of four-leg VSC (voltage source converter) interfaced to a three-phase, four-wire distribution system. The four-leg VSC of SPV generating system is also used for the compensation of the reactive power for zero voltage regulation or for power factor correction along with load balancing, elimination of load harmonics currents and mitigation of neutral current at PCC (point of common coupling) in three-phase four-wire distribution system. The DC bus of VSC is supported by a capacitor which is fed by SPV energy through a DC-DC boost converter. A laboratory prototype of proposed grid interfaced SPV generating system is developed to validate its developed model and the NN-based control algorithm.