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All Journal International Journal of Electrical and Computer Engineering International Journal of Power Electronics and Drive Systems (IJPEDS)
Vivek Kushwaha
GLA University
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Stator interturn short circuit fault identification in DFIG and its analysis using an artificial neural network Vivek Kushwaha; Sanjay Kumar Maurya; Arvind Kumar Yadav
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v17.i3.pp1643-1658

Abstract

Inter-turn short circuit (ITSC) issues are a common electrical failure mainly caused by the deterioration of winding insulation in the machine over time. Failing to detect such issues early can lead to catastrophic consequences. This article investigates the interturn fault in the stator winding of a doubly-fed induction generator (DFIG) used in wind turbines. A flux linkage difference vector (FLDV) model is introduced in this study for fault detection. Additionally, an artificial neural network (ANN) model is proposed to classify these faults. Specifically, a short-circuit fault is induced in each phase of the stator winding, and the faults are classified by assigning different magnitudes to the respective phases. The ANN is trained to identify which phase contains an interturn fault, with output waveform amplitudes of 1, 2, or 3 corresponding to faults in phases "a," "b," and "c." If no fault is present, the waveform magnitude is designated as "0." This approach enables early fault diagnosis by analyzing waveform patterns, thereby preventing overheating caused by short circuits and avoiding severe, irreversible damage to the windings.
Stator interturn short circuit fault identification in doubly-fed induction generator and its analysis using artificial neural network Vivek Kushwaha; Sanjay Kumar Maurya; Arvind Kumar Yadav
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i5.pp2379-2392

Abstract

Inter-turn short circuit (ITSC) issues are a common electrical failure mainly caused by the deterioration of winding insulation in the machine over time. Failing to detect such issues early can lead to catastrophic consequences. This article investigates the interturn fault in the stator winding of a doubly-fed induction generator (DFIG) used in wind turbines. A flux linkage difference vector (FLDV) model is introduced in this study for fault detection. Additionally, an artificial neural network (ANN) model is proposed to classify these faults. Specifically, a short-circuit fault is induced in each phase of the stator winding, and the faults are classified by assigning different magnitudes to the respective phases. The ANN is trained to identify which phase contains an interturn fault, with output waveform amplitudes of 1, 2, or 3 corresponding to faults in phases "a," "b," and "c." If no fault is present, the waveform magnitude is designated as "0." This approach enables early fault diagnosis by analyzing waveform patterns, thereby preventing overheating caused by short circuits and avoiding severe, irreversible damage to the windings.
Co-Authors Arvind Kumar Yadav Arvind Kumar Yadav Sanjay Kumar Maurya Sanjay Kumar Maurya