Predictive Maintenance of Power Substation Equipment by Infrared Thermography Using a Machine-Learning Approach
1
State Key Laboratory of Power Transmission Equipment & System Security and New Technology, School of Electrical Engineering, Chongqing University, Chongqing 400044, China
2
Department of IT, CoC, Qassim University, Buraydah 51452, Saudi Arabia
3
State Grid Shanxi Electric Power Company Jinzhong Power Supply Company, Jinzhong 030600, China
*
Author to whom correspondence should be addressed.
Energies 2017, 10(12), 1987; https://doi.org/10.3390/en10121987
Received: 21 October 2017 / Revised: 23 November 2017 / Accepted: 27 November 2017 / Published: 1 December 2017
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.
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MDPI and ACS Style
Ullah, I.; Yang, F.; Khan, R.; Liu, L.; Yang, H.; Gao, B.; Sun, K. Predictive Maintenance of Power Substation Equipment by Infrared Thermography Using a Machine-Learning Approach. Energies 2017, 10, 1987. https://doi.org/10.3390/en10121987
AMA Style
Ullah I, Yang F, Khan R, Liu L, Yang H, Gao B, Sun K. Predictive Maintenance of Power Substation Equipment by Infrared Thermography Using a Machine-Learning Approach. Energies. 2017; 10(12):1987. https://doi.org/10.3390/en10121987
Chicago/Turabian StyleUllah, Irfan, Fan Yang, Rehanullah Khan, Ling Liu, Haisheng Yang, Bing Gao, and Kai Sun. 2017. "Predictive Maintenance of Power Substation Equipment by Infrared Thermography Using a Machine-Learning Approach" Energies 10, no. 12: 1987. https://doi.org/10.3390/en10121987
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