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Three Phase Induction Motor's Stator Turns Fault Analysis Based on Artificial Intelligence

Author

Listed:
  • H. A. Taha Hussein

    (NAHDA University, Bani-Souf, Egypt)

  • M. E. Ammar

    (Department of Electrical Power and Machines, Cairo University, Egypt)

  • M. A. Moustafa Hassan

    (Department of Electrical Power and Machines, Cairo University, Egypt)

Abstract

This article presents a method for fault detection and diagnosis of stator inter-turn short circuit in three phase induction machines. The technique is based on modelling the motor in the dq frame for both health and fault cases to facilitate recognition of motor current. Using an Adaptive Neuro-Fuzzy Inference System (ANFIS) to provide an efficient fault diagnosis tool. An artificial intelligence network determines the fault severity values using the stator current history. The performance of the developed fault analysis method is investigated using Matlab/Simulink® software. Stator turns faults are detected through current monitoring of a 2 Hp three phase induction motor under various loading conditions. Fault history is calculated under various loading conditions, and a wide range of fault severity.

Suggested Citation

  • H. A. Taha Hussein & M. E. Ammar & M. A. Moustafa Hassan, 2017. "Three Phase Induction Motor's Stator Turns Fault Analysis Based on Artificial Intelligence," International Journal of System Dynamics Applications (IJSDA), IGI Global, vol. 6(3), pages 1-19, July.
    • Handle: RePEc:igg:jsda00:v:6:y:2017:i:3:p:1-19
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    Cited by:

    1. Sateesh Reddy Avutu & Sudip Paul, 2021. "Design and Optimization of Direct Drive Motor Alloy Wheel for Manual Wheelchair," International Journal of System Dynamics Applications (IJSDA), IGI Global, vol. 10(4), pages 1-13, October.
    2. Abha Jain & Ankita Bansal, 2022. "Models for Efficient Utilization of Resources for Upgrading Android Mobile Technology," International Journal of System Dynamics Applications (IJSDA), IGI Global, vol. 11(2), pages 1-22, August.

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