An Efficient Simplified Physical Faulty Model of a Permanent Magnet Synchronous Generator Dedicated to Stator Fault Diagnosis Part II: Automatic Stator Fault Diagnosis

Abstract

This paper proposes an automatic and intelligent stator fault diagnosis system for permanent magnet synchronous generators. The system is based on the use of a feedforward multilayer perception artificial neural network (ANN) performed by the back-propagation training algorithm. From a thorough study and analysis of the behavior of the negative-sequence voltage of the machine under different stator faults and different operating conditions, two new robust indicators of faults are selected as the ANN inputs. The two fault indicators are the phase angle of the negative sequence voltage and the frequency of the machine voltages. After successfully training and testing the ANN with specific and meaningful databases, the ANN behavior is validated by using an experimental database acquired from a real machine. The accurate results provided by the ANN prove the reliability of the proposed system to automatically diagnose different stator faults under variable speed, variable fault current, and variable load conditions overcoming different disturbances, such as the noisy signals and the harmonics in the machine.