基于P-CNN的局部放电绝缘故障融合诊断

Diagnosis of Partial Discharge Insulation Fault Fusion Based on P-CNN

绝缘缺陷作为引发封闭式开关设备局部放电的重要因素,对故障类型的有效判断具有重要工程意义,而气体组分分析法作为非电类局部放电检测法,不存在引入噪声,环境干扰等问题。为此提出一种基于概率卷积神经网络(probabilistic-convolutional neural network,P-CNN)融合故障诊断算法,即将特征数据组分快速特征提取和灰度图均衡化的数据预处理方式,引入3种气体组分特征,通过概率神经网络和卷积神经网络分别进行训练,将结果通过DS(Dempster-Shafer)证据理论进行融合。通过设计4种绝缘缺陷模型来模拟故障放电,并进行气体分解组分的特性研究。仿真预测结果表明,与传统机器学习对比,该算法可以在保证识别速度的情况下,有效提升识别正确率。

Insulation defect is an important factor to induce partial discharge of enclosed switching equipment.As a non-electric partial discharge detection method,the gas component analysis method has no problems such as introduction of noise and environmental interference.Consequently,four kinds of common insulation defect models were simulated to study the characteristics of gas decomposition components.A P-CNN fusion fault diagnosis algorithm was proposed,and a data preprocessing method was proposed to rapidly extract the features of characteristic data components and equalize the gray scale map.Three gas component features were introduced,respectively,and were trained by probabilistic neural network and convolutional neural network.Meanwhile,the results were fused to the conclusion level by DS evidence theory.Simulation results show that,compared with traditional machine learning,this algorithm can effectively improve the recognition accuracy while ensuring the recognition speed.