Accurate Identification of Closing State of Horizontal Rotary Disconnectors in a Substation Based on Mask R-CNN

Disconnectors are extremely important equipments in a substation. Long-term use may lead to abnormal structure, resulting in inadequate closing, which means a serious accident. Therefore, the judgment of disconnector closing in place has become a very important problem. Judging whether the closing i...

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Bibliographic Details
Published in2022 7th International Conference on Computational Intelligence and Applications (ICCIA) pp. 90 - 94
Main Authors Cao, Renwei, Li, Wenliang, Huang, Yijun, Lu, Deling, Lin, Qiuying
Format Conference Proceeding
LanguageEnglish
Published IEEE 24.06.2022
Subjects
Computational modeling
deep learning
disconnectors
Image edge detection
image recognition
Lighting
R-CNN
Shape
Substations
Switches
Target recognition
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DOI10.1109/ICCIA55271.2022.9828427

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Summary:Disconnectors are extremely important equipments in a substation. Long-term use may lead to abnormal structure, resulting in inadequate closing, which means a serious accident. Therefore, the judgment of disconnector closing in place has become a very important problem. Judging whether the closing is in place requires very accurate state identification, which is difficult to judge by the current method. Firstly, this paper used mask R-CNN network for target recognition, and obtained a model with high recognition accuracy. Then, this paper used HED method to extract the edge, and proposed an algorithm to detect the closing state. Experiments show that this method can effectively identify the closing state of a horizontal rotary disconnector.
DOI:10.1109/ICCIA55271.2022.9828427