Detection of TTF in Induction Motor Vector Drives for EV Applications via Ostu’s-Based DDWE

Abstract

In this study, an online fault diagnosis (FD) algorithm is developed to detect stator's turn-to-turn faults (TTFs) in direct torque control (DTC)-driven induction motors (IMs) for the electric vehicle's (EV) powertrains. The developed FD algorithm is based on the application of the discrete wavelet transformation (DWT) technique on the stator currents. Being a time-frequency domain-based algorithm, DWT can deal with nonstationary signals. The fault-sensitive decomposition levels were selected using Ostu's-thresholding formulation to detect the stator's faults at their embryonic stage. To determine the faulty phase, the concept of differential discrete wavelet energy (DDWE) was introduced. A finite-element (FE) cosimulation platform and an experimental setup were built to examine the proposed FD algorithm. The results depicted the robustness of the FD method. A comparative analysis is presented between the developed DDWE based FD and the conventional motor current signature analysis (MCSA)-based FD. The experimental results proved that developed FD is faster and more accurate than the MCSA-based FD one. Moreover, the presented FD routine was examined under load step-change and intermittent TTFs, and its diagnosing effectiveness was verified.