Sequence Component Based Approach to Detect Faults in Induction Motor Stator Winding

As 26%–37% of total faults occurring in induction motors (IMs) are stator winding faults, their detection is crucial to prevent unexpected downtime. Therefore, a new technique for fault detection, discrimination, and categorization of stator winding faults occurring in IM is presented. Initially, sequence components of voltages ( <tex-math notation="LaTeX">V̄1z/p/n</tex-math> ) and currents ( <tex-math notation="LaTeX">Ī1z/p/n</tex-math> ) from the supply side and currents ( <tex-math notation="LaTeX">Ī_2z/p/n</tex-math> ) from the remote side are calculated. Then, after calculating the difference of unbalanced factor ( <tex-math notation="LaTeX">DoUF̄</tex-math> ) during both normal and faulty conditions, the abnormal condition indicator ( <tex-math notation="LaTeX">ACĪ</tex-math> ) is derived for effective discrimination between normal conditions and abnormal situations. Thereafter, phase angle differences, i.e., <tex-math notation="LaTeX">\ ∠ ₁ₙ\ and \,∠₂ₙ</tex-math> utilizing supply-side negative sequence voltages and currents and remote-side negative sequence currents are estimated. Finally, discrimination between internal and external faults is achieved using <tex-math notation="LaTeX">\ ∠ ₁ₙ</tex-math> , and the analysis completed with the classification of internal faults utilizing <tex-math notation="LaTeX">\ ∠ ₂ₙ</tex-math> , <tex-math notation="LaTeX">Ī₂ₙ</tex-math> , and <tex-math notation="LaTeX">Ī_2z</tex-math> . The suggested technique is validated by generating numerous test cases whose signals are acquired from the laboratory prototype of induction motors (IMs). The outcomes demonstrate the robustness of the presented method to identify and classify internal faults even with varying fault resistance and load conditions. Additionally, it stays extremely stable against external faults even under current transformer saturation conditions.