A Novel Neural Instantaneous Power Theory for A Shunt Active Power Filter Interfaced Solar Photovoltaic System

Abstract

This study introduces a novel neural instantaneous power theory (Neural-IPT) based on a feed forward backpropagation artificial neural network (FFBP-ANN), for a three-phase shunt active power filter (SAPF) associated with a PV power plant. In order to check the performance of the proposed method of control, the studied system is subject to sudden disturbances caused at first by the variation of the ignition angle of the nonlinear load and then by an abrupt changes of the PV output parameters as well. Simulation outcome using MATLAB/Simulink tools, show that the proposed strategy of harmonics extraction, in conjunction with a Neural Network Hysteresis Controller, provides a quick and a precise estimation of the fundamental component of the nonlinear load, compared to the classical instantaneous power theory IPT, and consequently offers a best dynamic performance tracking, guarantee the imaginary power compensation and reduces source current distortion.