Neural Network Model Reference Adaptive System Speed Estimation for Sensorless Control of a Doubly Fed Induction Generator

This article proposes a neural network model reference adaptive system for the rotor angle and speed estimation of the doubly fed induction generator used in wind turbines. The model reference adaptive system reference signal is the measured rotor current. The adaptive neural network adjusts the weights minimizing the rotor current vector squared error using the steepest descent algorithm. The neural network maximum stable learning rate will be determined for this application. The validity of the proposed neural network model reference adaptive system is verified and analyzed in a real prototype of 7.5-kW doubly fed induction generator. To validate the proposed estimator, the estimated rotor angle and speed in the process of connecting the doubly fed induction generator to the grid and the sensorless regulation according to a random wind speed profile are presented.