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The identification of interturn faults in power transformers at an early stage is crucial to prevent extensive damage to the windings. To address the said issue, an interturn fault localization approach based on the fault location factor (FLF) is introduced in this article. The value of FLF during the prefault condition for all possible fault locations is stored as FLFcal in a lookup table. During the fault event in a transformer, the value of FLF (FLFmeas) is computed using the measured winding impedance at a reference frequency. By comparing the values of FLFmeas and FLFcal, the precise fault location in the transformer is determined. The effectiveness of this approach is assessed through a comprehensive simulation study to ensure its reliability. In addition, two different laboratory-developed winding models of the transformer, each with distinct winding configurations, and 3-phase field transformer of 400 kVA are utilized to validate the proposed approach. Finally, a comparative analysis is conducted between the proposed approach and several existing methods, highlighting its dominance in localizing interturn faults.

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 (${{\bar{V}}_{\rm 1z/p/n}}$) and currents (${{\bar{I}}_{\rm 1z/p/n}}$) from the supply side and currents (${{\bar{I}}_{{\mathrm{2z}}/{\mathrm{p}}/{\mathrm{n}}}}$) from the remote side are calculated. Then, after calculating the difference of unbalanced factor ($\overline {DoUF}$) during both normal and faulty conditions, the abnormal condition indicator ($\overline {ACI}$) is derived for effective discrimination between normal conditions and abnormal situations. Thereafter, phase angle differences, i.e., $\ {{\angle }{\theta}_{1n}}{\rm{\ and }}\,{{\angle}{\theta}_{2n}}$ 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 $\ {{\angle }{\theta}_{1n}}$, and the analysis completed with the classification of internal faults utilizing $\ {{\angle }{\theta}_{\mathrm{2n}}}$, ${{\bar{I}}_{\rm 2n}}$, and ${{\bar{I}}_{2{\mathrm{z}}}}$. 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.