Rotor position estimation of 6/4 Switched Reluctance Motor using a novel neural network algorithm

Abstract

This paper presents a novel approach for estimating the rotor position of a Switched Reluctance Motor (SRM) drive system using the Cascade Correlation Artificial Neural Network Algorithm (CCNNA). This technique estimates rotor position by measuring the three-phase voltages and currents and using magnetic characteristics of the SRM, with the aid of an ANN. The rotor position estimating technique is used in a high-performance sensor less variable speed SRM drive. The results are compared with the measured values, and the error analyses are given to determine the performance of the developed method. The error analyses have shown great accuracy and successful rotor position estimation technique for a 6/4 pole SRM using the cascade correlation algorithm-based ANN.