基于声发射特性的玻璃绝缘子污闪预测模型

Prediction Model for Pollution Flashover on Glass Insulator According to Acoustical Characteristics

绝缘子污秽闪络是电力系统不可忽视的灾害之一,绝缘子污秽局部放电声信号可以有效反映绝缘子接近污闪的"危险情况"。首先,在人工污秽实验室内进行大量试验,模拟不同可溶污秽附着密度(soluble contamination density,SCD)、不同灰密对玻璃绝缘子声发射信号的影响。之后,提取了污秽放电声发射信号2个典型特征量并分析了其变化规律。然后,建立了基于广义回归神经网络(general regression neural network,GRNN)的绝缘子外绝缘危险度预测模型,提取7个有效声发射特征量作为GRNN模型的输入,以绝缘子污秽闪络的危险度系数作为输出,得到不同可溶物、不同SCD下的预测结果。结果表明:基于声发射特性的GRNN预测模型准确性较高,声发射特征量的变化受到SCD的影响较大,SCD越低,特征量随机性变化越大,GRNN模型的预测准确性随之降低。所提模型为不同污秽度地区采用声发射测量法监测绝缘子外绝缘状况提供了可信度参考。

Insulator pollution flashover is a main disaster of electrical power system.A large number of artificial pollution tests are investigated under different contamination levels(different soluble contaminants densities or dust densities).According to experiment data,seven acoustic signal characteristics are extracted and analyzed.According to the conclusion,the general regression neural network(GRNN)model of risk degree prediction is established,in which the seven acoustic signal characteristics are as the inputs with the risk degrees used as outputs.It is found that the prediction accuracy is affected by soluble contaminants density mostly.The results show that the greater the soluble contaminants density,the smaller the acoustic signal characteristics’randomness,and the better prediction accuracy can be obtained.The conclusion of this paper provides reference for acoustic monitoring of insulators in different regions with different pollution levels.