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

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.

Deep Learning Image-Based Defect Detection in High Voltage Electrical Equipment

“…Texture features can be calculated by the gray-level value of the infrared images, and thus the consistency of such features can be utilized to predict the isolator condition [26]. The authors in [27] used 11 statistical features of the first and second orders from infrared thermal images to train the multi-layer perceptron (MLP) and then integrated the graph-cut to determine whether the power equipment was defective or non-defective. The automatic diagnosis of infrared images using an intelligent system is still in its early stages.…”

Remote Monitoring of Joints Status on In-Service High-Voltage Overhead Lines

“…At present, the actual status of advancement in this context is stuck in a controversial situation, where there are, on the one side, a lot of research efforts that explore a wide range of new and alternative monitoring techniques, such as the ones employing electromagnetic (EM) techniques [9][10][11][12][13][14], vibro-acoustic techniques [15], information technology (IT) solutions [16,17], and drones [17,18], whereas, on the other side, in the real-world, monitoring activity is mainly still linked with manual inspection methodologies using thermo-graphic cameras recordings [19,20].…”

Maintainability analysis of mining trucks with data analytics.

“…They used multilayer perceptron (MLP) to classify the thermal conditions of components of power substations into defect and non-defect classes. The performance of MLP reached 84 percent of accuracy with graph cut and this result showed the benefit of the proposed defect analysis approach (Ullah, Yang, Khan, Liu, Yang, Gao, & Sun, 2017).…”