Abstract：The contact area of the catenary suspension is small and easy to be confused with the power line. The traditional fault identification algorithms have problems such as misidentification of the suspension, low recognition efficiency and inability to detect in real time. Compared with the traditional convolutional neural network (CNN), the capsule network (CapsNet) uses vectors as input, which can well retain the feature information such as the direction and angle of the target, and is more suitable for identifying the suspension under complex background. Therefore, a suspension fault recognition algorithm based on improved capsule network and CV model is proposed. The convolution layer of traditional 9×9 capsule network is simplified by 1×1 reduction layer and 3×3 convolution layer, and the optimization algorithm is adopted to shorten the training weight time. At the same time, the output retains the direction and angle, which can more accurately classify the chords of burns, current-carrying ring breaks, and falling off, and so on. The method can be applied to the suspension inspection image. In this way, the accuracy of the catenary suspension positioning is increased to 95%. Finally, compared with Back Propagation Neural Network and AlexNet designed by Alex Krizhevsky, the proposed method of suspension fault identification can identify the suspension from the complex background and find the suspension accurately and quickly. The efficiency of the transmission line intelligent inspection can be greatly improved.
卞建鹏, 郝嘉星, 赵帅, 李凡, 孙晓云. 基于改进胶囊网络的接触网吊弦故障识别与定位[J]. 电工技术学报, 2020, 35(24): 5187-5196.
Bian Jianpeng, Hao Jiaxing, Zhao Shuai, Li Fan, Sun Xiaoyun. Fault Identification and Location of Catenary Suspension Based on Improved Capsule Network. Transactions of China Electrotechnical Society, 2020, 35(24): 5187-5196.
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