Wavelet — ANN based fault diagnosis in three phase induction motor

TLDR: In this paper, the authors proposed a protection scheme based on Wavelet Multi Resolution Analysis and Artificial Neural Networks which detects and classifies various faults like Single phasing, Under voltage, Unbalanced supply, Stator Turn fault, Stators Line to Ground fault, STator Line to Line fault, Broken bars and Locked rotor of a three-phase induction motor.

Abstract: This paper proposes a protection scheme based on Wavelet Multi Resolution Analysis and Artificial Neural Networks which detects and classifies various faults like Single phasing, Under voltage, Unbalanced supply, Stator Turn fault, Stator Line to Ground fault, Stator Line to Line fault, Broken bars and Locked rotor of a three-phase induction motor. The three phase Induction Motor is represented by a universal model which is valid for a wide range of frequencies. The same has been simulated using MATLAB/Simulink software and tested for various types of motor faults. The wavelet decomposition of three-phase stator currents is carried out with Bi-Orthogonal 5.5 (Bior5.5). The maximum value of the absolute peak value of the highest level (d1) coefficients of three-phase currents is defined as fault index which is compared with a predefined threshold to detect the fault. The normalized fourth level approximate (a4) coefficients of these currents are fed to a Feedforward neural network to classify various faults. The normalized peak d1 coefficients of three-phase currents are fed to another Feedforward neural network to identify the faulty phase of stator internal faults. The algorithm has been tested for various incidence angles and proved to be simple, reliable and effective in detecting and classifying the various faults and also in identifying the faulty phase of stator.