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Fault Identification and Classification of Asynchronous Motor Drive Using Optimization Approach with Improved Reliability

Author

Listed:
  • Gopu Venugopal

    (Department of Electrical and Electronics Engineering, Sri Ramakrishna Engineering College, Coimbatore 641022, Tamil Nadu, India)

  • Arun Kumar Udayakumar

    (Department of Electrical and Electronics Engineering, SRM Institute of Science and Technology, Ramapuram Campus, Chennai 600089, Tamil Nadu, India)

  • Adhavan Balashanmugham

    (Department of Electrical and Electronics Engineering, PSG Institute of Technology and Applied Research, Coimbatore 641062, Tamil Nadu, India)

  • Mohamad Abou Houran

    (School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China)

  • Faisal Alsaif

    (Department of Electrical Engineering, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia)

  • Rajvikram Madurai Elavarasan

    (Clean and Resilient Energy Systems (CARES) Laboratory, Texas A&M University, Galveston, TX 77553, USA)

  • Kannadasan Raju

    (Department of Electrical and Electronics Engineering, Sri Venkateswara College of Engineering, Sriperumbudur, Chennai 602117, Tamil Nadu, India)

  • Mohammed H. Alsharif

    (Department of Electrical Engineering, College of Electronics and Information Engineering, Sejong University, Seoul 05006, Republic of Korea)

Abstract

This article aims to provide a technique for identifying and categorizing interturn insulation problems in variable-speed motor drives by combining Salp Swarm Optimization (SSO) with Recurrent Neural Network (RNN). The goal of the proposed technique is to detect and classify Asynchronous Motor faults at their early stages, under both normal and abnormal operating conditions. The proposed technique uses a recurrent neural network in two phases to identify and label interturn insulation concerns, with the first phase being utilised to establish whether or not the motors are healthy. In the second step, it discovers and categorises potentially dangerous interturn errors. The SSO approach is used in the second phase of the recurrent neural network learning procedure, with the goal function of minimizing error in mind. The proposed CSSRN technique simplifies the system for detecting and categorizing the interturn insulation issue, resulting in increased system precision. In addition, the proposed model is implemented in the MATLAB/Simulink, where metrics such as accuracy, precision, recall, and specificity may be analysed. Similarly, existing methods such as Adaptive Neuro-Fuzzy Inference System (ANFIS), Recurrent Neural Network (RNN), and Salp Swarm Algorithm Artificial Neural Network (SSAANN) are utilised to evaluate metrics such as Root mean squared error (RMSE), Mean bias error (MBE), Mean absolute percentage error (MAPE), consumption, and execution time for comparative analysis.

Suggested Citation

  • Gopu Venugopal & Arun Kumar Udayakumar & Adhavan Balashanmugham & Mohamad Abou Houran & Faisal Alsaif & Rajvikram Madurai Elavarasan & Kannadasan Raju & Mohammed H. Alsharif, 2023. "Fault Identification and Classification of Asynchronous Motor Drive Using Optimization Approach with Improved Reliability," Energies, MDPI, vol. 16(6), pages 1-25, March.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:6:p:2660-:d:1095061
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    References listed on IDEAS

    as
    1. Luqman Maraaba & Zakariya Al-Hamouz & Mohammad Abido, 2018. "An Efficient Stator Inter-Turn Fault Diagnosis Tool for Induction Motors," Energies, MDPI, vol. 11(3), pages 1-18, March.
    2. Muhammad Rameez Javed & Zain Shabbir & Furqan Asghar & Waseem Amjad & Faisal Mahmood & Muhammad Omer Khan & Umar Siddique Virk & Aashir Waleed & Zunaib Maqsood Haider, 2022. "An Efficient Fault Detection Method for Induction Motors Using Thermal Imaging and Machine Vision," Sustainability, MDPI, vol. 14(15), pages 1-17, July.
    3. Syaiful Bakhri & Nesimi Ertugrul, 2022. "A Negative Sequence Current Phasor Compensation Technique for the Accurate Detection of Stator Shorted Turn Faults in Induction Motors," Energies, MDPI, vol. 15(9), pages 1-17, April.
    Full references (including those not matched with items on IDEAS)

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