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Research and Application of Image Recognition of Substation Inspection Robots Based On Edge Computing and Incremental Learning
Xiao Liu, Bangzhou Dong, Peiqi Li, Bin Yuan and Kesheng Wang

The large scale of the power grid and its high voltage make the fault detection have not only heavy workload but also high risks. This paper briefly introduced the inspection robot system, edge computing technology, and support vector machine (SVM)-based image recognition algorithm. To enable the inspection robot system to actively adjust the algorithm parameters and maintain the accuracy of the algorithm, the SVM algorithm was improved by the incremental learning technology. Then, the non-improved and improved SVM algorithms were simulated in MATLAB software. A one-month experiment was carried out in a substation. The results showed that the improved SVM algorithm trained faster and had a high accuracy in the face of new samples. The substation experiment verified that the inspection robot system maintained the infrared image recognition accuracy of the faulty equipment for a long time after the application of the improved SVM algorithm, and the system had a smaller delay in transmitting command information after the application of edge computing.

Keywords: edge computing, incremental learning, inspection robot, image recognition

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