基于BP神经网络和模糊逻辑的绝缘子污秽等级预测  

Prediction of Insulator Pollution Severity Class based on BP Neural Network and Fuzzy Logic

作　　者：王健[1,2] 杨志超[1,2] 葛乐[1,2] 杨成顺[1,2] 张成龙[1,2] 顾云峰 

机构地区：[1]南京工程学院电力工程学院,江苏南京211167 [2]配电网智能技术与装备江苏省协同创新中心,江苏南京211167 [3]启东市供电公司,江苏启东226200

出　　处：《南京工程学院学报（自然科学版）》2013年第4期17-22,共6页Journal of Nanjing Institute of Technology(Natural Science Edition)

基　　金：江苏省高校自然科学研究面上项目(13KJB470006);2013年江苏省电力公司科技项目(南通);南京工程学院大学生科技创新基金项目(N20131707)

摘　　要：针对复杂地理环境下的绝缘子污闪问题,提出一种基于BP人工神经网络和模糊逻辑补偿的绝缘子污秽等级预测新方法.该方法首先利用BP神经网络预测在温度、湿度、降雨量、风速等基本因素下绝缘子的污秽等级,然后充分考虑与复杂地理位置相关的海拔高度、气压比、覆冰水电导率等因素对绝缘子污秽度的影响,采用模糊逻辑补偿的方法进行校正.算例结果表明方法的正确性和有效性.In view of the insulator pollution flashover problem in the complex geographical environment, this article puts forward a new method to predict insulator pollution severity class based on BP artificial neural network and fuzzy logic compensation. This method first predicts the filth of the insulator caused by the basic factors such as temperature, humidity, rainfall, wind speed using BP neural network. And then the effects of such factors associated with complex geographic location as elevation, pressure ratio, and freezing water conductivity on the insulator filthy are taken into account when the method is corrected using fuzzy logic compensation method. The results of the numerical examples show the correctness and effectiveness of the method.

关 键 词：绝缘子 污秽等级 神经网络 模糊逻辑 

分 类 号：TM216[一般工业技术—材料科学与工程]