Intelligent detection method for substation insulator defects based on CenterMask

Abstract

With the development of intelligent operation and maintenance of substations, the daily inspection of substations needs to process massive video and image data. This puts forward higher requirements on the processing speed and accuracy of defect detection. Based on the end-to-end learning paradigm, this article proposes an intelligent detection method for substation insulator defects based on CenterMask. First, the backbone network VoVNet is improved according to the residual connection and eSE module, which effectively solves the problems of deep network saturation and gradient information loss. On this basis, an insulator mask generation method based on a spatial attention-directed mechanism is proposed. Insulators with complex image backgrounds are accurately segmented. Then, three strategies of pixel-wise regression prediction, multi-scale features, and centerness are introduced. The anchor-free single-stage target detector accurately locates the defect points of insulators. Finally, an example analysis is carried out with the substation inspection image of a power supply company in a certain area to verify the effectiveness and robustness of the proposed method.

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“…Therefore, applying the deep learning method to the smart grid monitoring system can adapt to the stability requirements of the grid. At the same time, the centralized management of ISSSCN monitoring data reduces the load capacity of power supply equipment's own data storage, making fault prediction and intelligent scheduling possible [7][8] .…”

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“…The stable operation and the transmission of electric energy greatly impact the substation equipment's life, performance, safety, and other factors (Wang et al, 2022). During actual operation, power equipment will be affected by overload, overvoltage, internal insulation aging, abnormal natural environment and other events, and abnormal operation status will lead to equipment defects and failures (Ye et al, 2022).…”