Quantitatively aging diagnosis of conductor surface remains critical challenging in fault diagnosis of smart high-voltage electricity grid. Inspired by the facial age estimation in computer vision, this work proposes a… Click to show full abstract
Quantitatively aging diagnosis of conductor surface remains critical challenging in fault diagnosis of smart high-voltage electricity grid. Inspired by the facial age estimation in computer vision, this work proposes a label-distribution deep convolutional neural networks (CNNs) model, which includes an AlexNet-based deep convolution network and a designed loss embedded with Gaussian label distribution. The aging diagnosis problem of conductor morphology is transformed into a multiclassification problem. The proposed model is improved via a weakly labeled training dataset and a designed loss function (combination of entropy loss, cross-entropy loss, and Kullback–Leibler divergence loss). Compared with four frequently used CNN-based classifiers, the proposed classifier on the collected dataset achieves a better performance. In addition, the influence of parameters and types of label distribution on classification accuracy is also investigated. Here, a promising technique is presented for the aging estimation of aged conductor with high accuracy when the images of conductor surface are available.
               
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