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.
Highlights
Thermal energy plays an important role in the electrical equipment of power substations for diagnosis of the fault in its early stages, which increases the operational reliability of the power grid’s working life
We investigate the application of thermography infrared technology for predictive maintenance to identify the presence of a defect and non-defect in electrical power substations of 110 kV .The maintenance cost of electrical components plays an essential part to reduce the cumulative working expense of power substations
The augmentation approach uses the graph to combine the multi-layered perceptron (MLP) and thermal image structure to increase the performance to 84%
Summary
Thermal energy plays an important role in the electrical equipment of power substations for diagnosis of the fault in its early stages, which increases the operational reliability of the power grid’s working life. In this article, for non-destructive defect analysis and prevention in power substations, we use the computer vision approach and machine learning to detect the problem in early stages of equipment breakdown by exploiting and taking advantage of the infrared thermal images. We investigate the application of thermography infrared technology for predictive maintenance to identify the presence of a defect and non-defect in electrical power substations of 110 kV .The maintenance cost of electrical components plays an essential part to reduce the cumulative working expense of power substations. We use the statistical features in infrared images to characterize the thermal status into the “defect” and “non-defect” categories in power substation equipment
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