Artificial neural network control system for autonomous volt/var control of inverter in interfaced units

Abstract

A system is described herein for controlling an inverter comprising an artificial neural network (ANN) and a space vector pulse-width modulation converter. The ANN comprises an input layer, plurality of hidden layers, an output layer, and a processor. The ANN receives a plurality of input signals and produces output signals that control a SVPWM converter. The ANN is trained to minimize a cost function and to implement optimal control based on ADP. Further, the ANN can be configured to handle rated current and PWM saturation constraints in providing volt/VAR control functions. Finally, the ANN uses averaged feedback signals instead of instantaneous feedback signals to improve volt/VAR control performance of parallel inverters. Performance evaluation shows that an ANN controller has a strong ability to maintain volt and VAR control at the grid edge and prevent fighting between inverters thereby allowing an inverter to work effectively in parallel with other ANN-controlled inverters.