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Intelligent Starting Current-Based Fault Identification of an Induction Motor Operating under Various Power Quality Issues

Author

Listed:
  • Sakthivel Ganesan

    (Department of Mechatronics Engineering, Kamaraj College of Engineering and Technology, Madurai 625701, India)

  • Prince Winston David

    (Department of Electrical & Electronics Engineering, Kamaraj College of Engineering and Technology, Madurai 625701, India)

  • Praveen Kumar Balachandran

    (Department of Electrical & Electronics Engineering, Bharat Institute of Engineering and Technology, Hyderabad 501510, India)

  • Devakirubakaran Samithas

    (Department of Electrical & Electronics Engineering, Sethu Institute of Technology, Madurai 626115, India)

Abstract

Since most of our industries use induction motors, it is essential to develop condition monitoring systems. Nowadays, industries have power quality issues such as sag, swell, harmonics, and transients. Thus, a condition monitoring system should have the ability to detect various faults, even in the presence of power quality issues. Most of the fault diagnosis and condition monitoring methods proposed earlier misidentified the faults and caused the condition monitoring system to fail because of misclassification due to power quality. The proposed method uses power quality data along with starting current data to identify the broken rotor bar and bearing fault in induction motors. The discrete wavelet transform (DWT) is used to decompose the current waveform, and then different features such as mean, standard deviation, entropy, and norm are calculated. The neural network (NN) classifier is used for classifying the faults and for analyzing the classification accuracy for various cases. The classification accuracy is 96.7% while considering power quality issues, whereas in a typical case, it is 93.3%. The proposed methodology is suitable for hardware implementation, which merges mean, standard deviation, entropy, and norm with the consideration of power quality issues, and the trained NN proves stable in the detection of the rotor and bearing faults.

Suggested Citation

  • Sakthivel Ganesan & Prince Winston David & Praveen Kumar Balachandran & Devakirubakaran Samithas, 2021. "Intelligent Starting Current-Based Fault Identification of an Induction Motor Operating under Various Power Quality Issues," Energies, MDPI, vol. 14(2), pages 1-13, January.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:2:p:304-:d:476599
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    References listed on IDEAS

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    1. Majidi Nezhad, M. & Heydari, A. & Groppi, D. & Cumo, F. & Astiaso Garcia, D., 2020. "Wind source potential assessment using Sentinel 1 satellite and a new forecasting model based on machine learning: A case study Sardinia islands," Renewable Energy, Elsevier, vol. 155(C), pages 212-224.
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    Cited by:

    1. Juan Carlos Travieso-Torres & Manuel A. Duarte-Mermoud & Matías Díaz & Camilo Contreras-Jara & Francisco Hernández, 2022. "Closed-Loop Adaptive High-Starting Torque Scalar Control Scheme for Induction Motor Variable Speed Drives," Energies, MDPI, vol. 15(10), pages 1-15, May.
    2. Mlungisi Ntombela & Kabeya Musasa, 2023. "Load Profile and Load Flow Analysis for a Grid System with Electric Vehicles Using a Hybrid Optimization Algorithm," Sustainability, MDPI, vol. 15(12), pages 1-23, June.
    3. Akash Saxena & Ahmad M. Alshamrani & Adel Fahad Alrasheedi & Khalid Abdulaziz Alnowibet & Ali Wagdy Mohamed, 2022. "A Hybrid Approach Based on Principal Component Analysis for Power Quality Event Classification Using Support Vector Machines," Mathematics, MDPI, vol. 10(15), pages 1-16, August.
    4. Ángel Adrián Orta-Quintana & Rogelio Ernesto García-Chávez & Ramón Silva-Ortigoza & Magdalena Marciano-Melchor & Miguel Gabriel Villarreal-Cervantes & José Rafael García-Sánchez & Rocío García-Cortés , 2023. "Sensorless Tracking Control Based on Sliding Mode for the “Full-Bridge Buck Inverter–DC Motor” System Fed by PV Panel," Sustainability, MDPI, vol. 15(13), pages 1-27, June.
    5. Damian Grzechca & Paweł Rybka & Roman Pawełczyk, 2021. "Level Crossing Barrier Machine Faults and Anomaly Detection with the Use of Motor Current Waveform Analysis," Energies, MDPI, vol. 14(11), pages 1-14, May.
    6. Chung-Seong Lee & Hae-Joong Kim, 2022. "Harmonic Order Analysis of Cogging Torque for Interior Permanent Magnet Synchronous Motor Considering Manufacturing Disturbances," Energies, MDPI, vol. 15(7), pages 1-13, March.

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