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Predictive Maintenance of Power Substation Equipment by Infrared Thermography Using a Machine-Learning Approach

Author

Listed:
  • Irfan Ullah

    (State Key Laboratory of Power Transmission Equipment & System Security and New Technology, School of Electrical Engineering, Chongqing University, Chongqing 400044, China)

  • Fan Yang

    (State Key Laboratory of Power Transmission Equipment & System Security and New Technology, School of Electrical Engineering, Chongqing University, Chongqing 400044, China)

  • Rehanullah Khan

    (Department of IT, CoC, Qassim University, Buraydah 51452, Saudi Arabia)

  • Ling Liu

    (State Grid Shanxi Electric Power Company Jinzhong Power Supply Company, Jinzhong 030600, China)

  • Haisheng Yang

    (State Grid Shanxi Electric Power Company Jinzhong Power Supply Company, Jinzhong 030600, China)

  • Bing Gao

    (State Key Laboratory of Power Transmission Equipment & System Security and New Technology, School of Electrical Engineering, Chongqing University, Chongqing 400044, China)

  • Kai Sun

    (State Grid Shanxi Electric Power Company Jinzhong Power Supply Company, Jinzhong 030600, China)

Abstract

A variety of reasons, specifically contact issues, irregular loads, cracks in insulation, defective relays, terminal junctions and other similar issues, increase the internal temperature of electrical instruments. This results in unexpected disturbances and potential damage to power equipment. Therefore, the initial prevention measures of thermal anomalies in electrical tools are essential to prevent power-equipment failure. In this article, we address this initial prevention mechanism for power substations using a computer-vision approach by taking advantage of infrared thermal images. The thermal images are taken through infrared cameras without disturbing the working operations of power substations. Thus, this article augments the non-destructive approach to defect analysis in electrical power equipment using computer vision and machine learning. We use a total of 150 thermal pictures of different electrical equipment in 10 different substations in operating conditions, using 300 different hotspots. Our approach uses multi-layered perceptron (MLP) to classify the thermal conditions of components of power substations into “defect” and “non-defect” classes. A total of eleven features, which are first-order and second-order statistical features, are calculated from the thermal sample images. The performance of MLP shows initial accuracy of 79.78%. We further augment the MLP with graph cut to increase accuracy to 84%. We argue that with the successful development and deployment of this new system, the Technology Department of Chongqing can arrange the recommended actions and thus save cost in repair and outages. This can play an important role in the quick and reliable inspection to potentially prevent power substation equipment from failure, which will save the whole system from breakdown. The increased 84% accuracy with the integration of the graph cut shows the efficacy of the proposed defect analysis approach.

Suggested Citation

  • Irfan Ullah & Fan Yang & Rehanullah Khan & Ling Liu & Haisheng Yang & Bing Gao & Kai Sun, 2017. "Predictive Maintenance of Power Substation Equipment by Infrared Thermography Using a Machine-Learning Approach," Energies, MDPI, vol. 10(12), pages 1-13, December.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:12:p:1987-:d:121076
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    Citations

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    Cited by:

    1. Patrick Zschech & Kai Heinrich & Raphael Bink & Janis S. Neufeld, 2019. "Prognostic Model Development with Missing Labels," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 61(3), pages 327-343, June.
    2. Arnaldo Rabello de Aguiar Vallim Filho & Daniel Farina Moraes & Marco Vinicius Bhering de Aguiar Vallim & Leilton Santos da Silva & Leandro Augusto da Silva, 2022. "A Machine Learning Modeling Framework for Predictive Maintenance Based on Equipment Load Cycle: An Application in a Real World Case," Energies, MDPI, vol. 15(10), pages 1-41, May.
    3. Saez, Yago & Mochon, Asuncion & Corona, Luis & Isasi, Pedro, 2019. "Integration in the European electricity market: A machine learning-based convergence analysis for the Central Western Europe region," Energy Policy, Elsevier, vol. 132(C), pages 549-566.
    4. Osni Silva Junior & Jose Carlos Pereira Coninck & Fabiano Gustavo Silveira Magrin & Francisco Itamarati Secolo Ganacim & Anselmo Pombeiro & Leonardo Göbel Fernandes & Eduardo Félix Ribeiro Romaneli, 2023. "Impacts of Atmospheric and Load Conditions on the Power Substation Equipment Temperature Model," Energies, MDPI, vol. 16(11), pages 1-15, May.
    5. Irfan Ullah & Rehan Ullah Khan & Fan Yang & Lunchakorn Wuttisittikulkij, 2020. "Deep Learning Image-Based Defect Detection in High Voltage Electrical Equipment," Energies, MDPI, vol. 13(2), pages 1-17, January.
    6. 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.
    7. Andreas Anael Pereira Gomes & Francisco Itamarati Secolo Ganacim & Fabiano Gustavo Silveira Magrin & Nara Bobko & Leonardo Göbel Fernandes & Anselmo Pombeiro & Eduardo Félix Ribeiro Romaneli, 2023. "A Semantically Annotated 15-Class Ground Truth Dataset for Substation Equipment to Train Semantic Segmentation Models," Data, MDPI, vol. 8(7), pages 1-16, July.
    8. Moamin A. Mahmoud & Naziffa Raha Md Nasir & Mathuri Gurunathan & Preveena Raj & Salama A. Mostafa, 2021. "The Current State of the Art in Research on Predictive Maintenance in Smart Grid Distribution Network: Fault’s Types, Causes, and Prediction Methods—A Systematic Review," Energies, MDPI, vol. 14(16), pages 1-27, August.
    9. Richter, Lucas & Lehna, Malte & Marchand, Sophie & Scholz, Christoph & Dreher, Alexander & Klaiber, Stefan & Lenk, Steve, 2022. "Artificial Intelligence for Electricity Supply Chain automation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    10. Garrido, I. & Lagüela, S. & Otero, R. & Arias, P., 2020. "Thermographic methodologies used in infrastructure inspection: A review—Post-processing procedures," Applied Energy, Elsevier, vol. 266(C).
    11. Carlo Olivieri & Francesco de Paulis & Antonio Orlandi & Giorgio Giannuzzi & Roberto Salvati & Roberto Zaottini & Carlo Morandini & Lorenzo Mocarelli, 2019. "Remote Monitoring of Joints Status on In-Service High-Voltage Overhead Lines," Energies, MDPI, vol. 12(6), pages 1-17, March.
    12. Olcay Özge Ersöz & Ali Fırat İnal & Adnan Aktepe & Ahmet Kürşad Türker & Süleyman Ersöz, 2022. "A Systematic Literature Review of the Predictive Maintenance from Transportation Systems Aspect," Sustainability, MDPI, vol. 14(21), pages 1-18, November.
    13. Lixiao Mu & Xiaobing Xu & Zhanran Xia & Bin Yang & Haoran Guo & Wenjun Zhou & Chengke Zhou, 2021. "Autonomous Analysis of Infrared Images for Condition Diagnosis of HV Cable Accessories," Energies, MDPI, vol. 14(14), pages 1-15, July.

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