Sequence Component Based Approach to Detect Faults in Induction Motor Stator Winding
Naveenkumar R. Sharma, Bhavesh R. Bhalja, O.P. Malik
Abstract
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 ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${{\bar{V}}_{\rm 1z/p/n}}$</tex-math></inline-formula> ) and currents ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${{\bar{I}}_{\rm 1z/p/n}}$</tex-math></inline-formula> ) from the supply side and currents ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${{\bar{I}}_{{\mathrm{2z}}/{\mathrm{p}}/{\mathrm{n}}}}$</tex-math></inline-formula> ) from the remote side are calculated. Then, after calculating the difference of unbalanced factor ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\overline {DoUF} $</tex-math></inline-formula> ) during both normal and faulty conditions, the abnormal condition indicator ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\overline {ACI} $</tex-math></inline-formula> ) is derived for effective discrimination between normal conditions and abnormal situations. Thereafter, phase angle differences, i.e., <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ {{\angle }{\uptheta}_{1n}}{\rm{\ and }}\,{{\angle}{\uptheta}_{2n}}$</tex-math></inline-formula> utilizing supply-side negative sequence voltages and currents and remote-side negative sequence currents are estimated. Finally, discrimination between internal and external faults is achieved using <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ {{\angle }{\uptheta}_{1n}}$</tex-math></inline-formula> , and the analysis completed with the classification of internal faults utilizing <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ {{\angle }{\uptheta}_{\mathrm{2n}}}$</tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${{\bar{I}}_{\rm 2n}} $</tex-math></inline-formula> , and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${{\bar{I}}_{2{\mathrm{z}}}}$</tex-math></inline-formula> . 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.