Wavelet ANN based stator internal faults protection scheme for 3-phase induction motor

TLDR: In this article, the wavelet decomposition of three-phase stator currents is carried out with Bior5.5 and the normalized peak d1 coefficients of these currents are fed to a feedforward neural network to classify various faults.

Abstract: This paper proposes a protection scheme based on Wavelet Multi Resolution Analysis and Artificial neural network which detects and classifies various possible stator winding fault of a three-phase induction motor such as inter turn faults, line to ground faults and line to line faults. The wavelet decomposition of three-phase stator currents is carried out with Bior5.5. The maximum value of absolute peak d1 coefficients of three-phase currents is defined as fault index which is compared with a predefined threshold to detect the fault. The normalized peak d1 coefficients of these currents are fed to a Feedforward neural network to classify various faults. The algorithm has been tested for various incidence angles and proved to be simple, reliable and effective in detecting and classifying the various stator winding faults.