Control Strategy for PMSG Wind Turbine Variable System Using Neural Network Controller

Abstract

The present work shown in the paper is an innovative, reliable MPPT-pitch angle control method based on neural network aimed at a running speed driven permanent magnet synchronous generator (PMSG) in wind energy conversion system (WECS). Wind turbine drives PMSG are directly connected by autonomous controllers which supply AC power to the utility grid. The generator tie inverter is operated for the adjustment of the synchronous generator likewise it separates grid tie generator when necessary. Grid tie inverter manages DC and AC side power flow. End-to-end frequency inverters are oriented and governed by pulse width modulation (PWM). Furthermore, the pitch angle and inverters are governed by a proportional-integral (PI) controller. An innovative combined MPPT, pitch angle control is built based on Neural Network (NN). To run PMSG at optimum speed, the wind turbines operate at variable speeds to obtain maximum power when necessary to reduce additional power. The proposed controller of the neural network is subjected using wind turbine PMSG, which practices a nonlinear neural network model to forecast plant potential output.