Design and implementation of Legendre‐based neural network controller in grid‐connected PV systems

Abstract

This study presents the development of Legendre-based functional neural network algorithm for shunt compensation in photovoltaic (PV)-based grid-connected system. The controller is developed for improving power quality (PQ) and the compensator is controlled to work in current control mode. It injects the requisite compensating current depending on the nature of the load current. The compensator is also interfaced with PV source and the controller design incorporates its contribution too. Some of the PQ problems studied include curtailment of harmonics, providing necessary reactive power, power factor improvement and so on. Results under distorted grid, varying solar irradiation and variety of loads have been presented. The proposed algorithm is designed using non-linear functional Legendre expansion of load current and has not been used for compensation or PQ problem alleviation till date. Both simulation and experimental results verify that the proposed algorithm performs far better than the adaptive popular backpropagation multilayer perceptron neural network, recurrent neural network and non-adaptive conventional synchronous reference frame theory based techniques.