This paper portrays the application of a Partial Discharge (PD) signal combined with the dual-input VGG Convolution Neural Network (CNN) to predict the location of the pollution layer on 11 kV polymer insulators subjected to alternating current for smart grid applications. First, a non-uniform pollution layer artificially created for HV insulator with three straight shed ball end fitting in a laboratory setup and corresponding PD readings are measured. The wavelet transform is employed to represent the measured PD signal as scalogram patterns. In general CNN uses a single input pattern for feature extraction. If the pattern quality is low, it is easy to cause misclassification. Hence in this proposed work, the feature fusion of a dual-input Visual Geometry Group (VGG) based CNN is used for the classification of contamination layer. VGG 19 is a pretrained deep learning network used for extracting the rich features from the patterns. In continuation to that, hyperparameter (HP) play a vital role in deep learning algorithms because they directly manage the behaviours of training algorithms and have a significant effect on the performance of deep learning models. Hence, Bayesian Optimization (BO) is used for tuning the HP. At last, to check the practicality of the proposed algorithm, a new dataset is created for 11 kV polymer insulator with three alternate shed clevis end fitting and different pollution levels—acceptable results obtained by using dual-input CNN with the minimum quantity of data.

中文翻译：

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双输入VGG卷积神经网络识别智能电网11 kV聚合物绝缘子污染层位置

本文描述了局部放电 (PD) 信号与双输入 VGG 卷积神经网络 (CNN) 相结合的应用，以预测智能电网应用中交流电作用下 11 kV 聚合物绝缘子上污染层的位置。首先，在实验室设置中为具有三直棚球端配件的高压绝缘子人工创建的不均匀污染层，并测量相应的局部放电读数。小波变换用于将测得的局放信号表示为尺度图模式。一般来说，CNN 使用单一输入模式进行特征提取。如果图案质量低，很容易造成误分类。因此，在这项工作中，基于双输入视觉几何组（VGG）的 CNN 的特征融合被用于污染层的分类。VGG 19 是一个预训练的深度学习网络，用于从模式中提取丰富的特征。除此之外，超参数（HP）在深度学习算法中发挥着至关重要的作用，因为它们直接管理训练算法的行为并对深度学习模型的性能产生重大影响。因此，贝叶斯优化 (BO) 用于调整 HP。最后，为了检查所提出算法的实用性，为具有三个交替伞裙U形端部配件和不同污染水平的11 kV聚合物绝缘子创建了一个新的数据集——使用双输入CNN以最少的数据量获得了可接受的结果。