Transmission Line Equipment Infrared Diagnosis Using an Improved Pulse-Coupled Neural Network

Jie Tong, Xiangquan Zhang, Changyu Cai, Zhouqiang He, Yuanpeng Tan () and Zhao Chen
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Jie Tong: Artificial Intelligence Application Department, China Electric Power Research Institute, Beijing 100192, China
Xiangquan Zhang: State Grid Gansu Electric Power Company, Lanzhou 730070, China
Changyu Cai: Artificial Intelligence Application Department, China Electric Power Research Institute, Beijing 100192, China
Zhouqiang He: State Grid Gansu Electric Power Company, Lanzhou 730070, China
Yuanpeng Tan: Artificial Intelligence Application Department, China Electric Power Research Institute, Beijing 100192, China
Zhao Chen: State Grid Gansu Electric Power Company, Lanzhou 730070, China

Sustainability, 2022, vol. 15, issue 1, 1-12

Abstract: In order to detect the status of power equipment from infrared transmission line images under the spatial positioning relationship of the transmission line equipment, such as corridor, substation equipment, and facilities, this paper presents an improved PCNN model which merges an optimized parameter setting method. In this PCNN model, the original iteration mechanism is abandoned, and instead, the thresholding model is built by the maximum similarity thresholding rule. To ensure similarity during classifying neighboring neurons into cluster centers, a local clustering strategy is used for setting the linking coefficient, thus improving the efficiency of the method to detect the power equipment in infrared transmission line images. Finally, experimental results on transmission line infrared images show that the proposed method can provide the basis for the diagnosis of power equipment, preventing the casualties and property damage caused by the thermal damage of power equipment, and effectively improving the safety risk identification and operation control ability of power grid engineering.

Keywords: pulse-coupled neural network; image segmentation; parameter setting; similarity thresholding (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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