基于RBF神经网络的绝缘子污秽智能诊断  

Intelligent diagnosis of insulator contamination based on RBF neural network

作　　者：张恩锋[1,2] 

机构地区：[1]山东能源枣矿集团供电工程处,山东省枣庄市277011 [2]中国矿业大学,江苏省徐州市221008

出　　处：《中国煤炭》2015年第7期84-87,共4页China Coal

摘　　要：选用RBF神经网络建立绝缘子泄漏电流特征量、气象参数与绝缘子污秽程度之间的映射关系,通过人工污秽试验数据对网络进行训练,搭建了基于RBF神经网络的绝缘子污秽智能诊断模型,并对该模型的预测做了验证,结果表明,可以对绝缘子污秽程度实现很好的预测效果。The mapping relationships of the insulator leakage current characteristics, mete- orological parameters and the degree of insulator contamination were established by means of RBF neural network, the network was trained by inputting the artificial insulator contamination test data, and then the intelligent diagnosis model of insulator contamination based on RBF neu- ral network was built and the prediction results of the model were validated. The results show that good prediction effect can be achieved using the degree of insulator contamination.

关 键 词：RBF神经网络 智能诊断 绝缘子 泄漏电流 

分 类 号：TD611[矿业工程—矿山机电]