In this paper, a wireless sensor network (WSN) is combined with Convolutional Neural Network (CNN) forming a hybrid framework to detect the pollution state in high voltage insulators. The WSN is formed by the collection of sensor readings from each high voltage insulator over the transmission tower. The collected sensor readings from the sensor network is sent to the processing unit or detection unit, where CNN is used for the purpose of detecting the partial discharged high voltage insulator. The CNN is used with partial discharge diagnosis model to detect the dischargers in high voltage insulators. The extraction of relevant features from the CNN helps to improve the detection. The experimental validation are conducted on the proposed model with collected training datasets and real time testing datasets. The proposed method is compared with existing models to test the partial discharges in high voltage insulators, namely Artificial Neural Network, Fuzzy and Ant Colony Optimization. The result shows that the proposed method is effective in detecting the partial discharges than the existing methods in terms of False Acceptance Rate and Missing Detection Rate.

本文将无线传感器网络（WSN）与卷积神经网络（CNN）结合在一起，形成了一种混合框架，可用于检测高压绝缘子中的污染状态。通过从传输塔上每个高压绝缘子收集传感器读数来形成WSN。从传感器网络收集的传感器读数将发送到处理单元或检测单元，在此使用CNN来检测部分放电的高压绝缘子。CNN与局部放电诊断模型一起用于检测高压绝缘子中的放电器。从CNN中提取相关特征有助于改善检测。通过收集的训练数据集和实时测试数据集对提出的模型进行实验验证。将该方法与现有模型进行了比较，以测试高压绝缘子的局部放电，即人工神经网络，模糊和蚁群优化。结果表明，与现有方法相比，该方法在误接收率和漏检率方面均能有效地检测局部放电。