摘要
基于光波导边界传输特性和人工神经网络,文中介绍了一种可同时监测输电线路绝缘子表面等值盐密和灰密的污秽在线监测装置。在详细分析光波导表面污层对全反射光束影响机理的基础上,提出了光波导和光源设计方案。通过不同湿度、不同污秽等级、不同灰盐比条件下的积污标定试验,获取了大量样本数据,并依此建立和训练了人工神经网络模型。检验结果和运行实践表明,利用该装置及训练后的内嵌神经网络模型,可实现盐密和灰密的同时监测,且测试精度满足相关标准要求。
An online pollution monitoring equipment is developed based on boundary transmission characteristics of optical waveguide and artificial neural network (ANN),which can measure both equivalent salt deposit density (ESDD) and non-soluble deposit density(NSDD)on insulators”surfaces.By analyzing the effects of the optical waveguide”s surface pollution on the reflected beam,the optical waveguide and the optical source are designed.A series of validating and calibrating tests have been made under different humidity,different salt density and ash density conditions,from which a large quantity of sample data are obtained and a unique ANN model is built and trained.The inspecting and running results show that this equipment together with the embedded ANN model can be used to monitor ESDD and NSDD of the insulators at once,and with satisfactory test precision.
出处
《电力系统自动化》
EI
CSCD
北大核心
2014年第10期107-112,共6页
Automation of Electric Power Systems
基金
国家自然科学基金资助项目(61102170)~~
关键词
输电线路
绝缘子污秽
在线监测
等值盐密
灰密
光波导
transmission line
insulator pollution
online monitoring
equivalent salt deposit density(ESDD)
non-soluble deposit density(NSDD)
optical waveguide