Method for Fault Diagnosis and Speed Control of PMSM

Abstract

In the field of fault tolerance estimation, the increasing attention in electrical motors is the fault detection and diagnosis. The tasks performed by these machines are progressively complex and the enhancements are likewise looked for in the field of fault diagnosis. It has now turned out to be essential to diagnose faults at their very inception; as unscheduled machine downtime can upset deadlines and cause heavy financial burden. In this paper, fault diagnosis and speed control of permanent magnet synchronous motor (PMSM) is proposed. Elman Neural Network (ENN) is used to diagnose the fault of permanent magnet synchronous motor. Both the fault location and fault severity are considered. In this, eccentricity fault may occur in the motor. To control the speed of the permanent magnet synchronous motor, Dolphin Swarm Optimization (DSO) algorithm is used. The proposed work is simulated by using MATLAB in terms of amplitude, speed and torque. The comparison graph of speed vs. torque obtained by the proposed method gives better result compared to the other existing techniques. The proposed work is also compared with Particle Swarm Optimization (PSO) and Elephant Herding Optimization (EHO) algorithm. The proposed usage of Elman Neural Network to detect the fault and the usage of Dolphin Swarm Optimization algorithm to control the speed of the permanent magnet synchronous motor gives better outcome.