Transformer Oil Leakage Detection Based on Loop Training Method

Abstract

<p indent=0mm>Inspecting whether the transformer has oil leakage problem is of great value in maintaining the safety and stability of the power grid. The oil stain on the ground is an important basis for judging whether the transformer leaks oil. The different shapes of the oil stain areas, complex background and the influence of shadow have brought challenges to the oil stain detection. Shadow is a ubiquitous physical phenomenon in nature, and the impact on oil stain detection is inevitable. In order to eliminate the influence of shadow, we propose a loop training method. The histogram equalization is adopted to enhance the contrast of the hard example between the oil stain area and the shadow, and the enhanced images are iteratively trained to reduce the interference of the shadow to improve the recall. At the same time, we introduce negative examples to alleviate the false detection of oil stain to improve the precision. The data are collected in the real environment of the substation. To validate the effectiveness of the proposed method, 8 schemes are designed for comparison experiments. Experimental results show that the models using the proposed method can effectively eliminate the influence of shadow on oil stain detection, and significantly improve the accuracy of oil stain detection.