基于光波导边界传输特性的污秽在线监测技术
发布时间：2018-11-06 19:36
【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。
[Abstract]:Based on the propagation characteristics of optical waveguide boundary and artificial neural network, an on-line pollution monitoring device is introduced, which can simultaneously monitor the equivalent salt density and grey density of insulator surface of transmission line. Based on the detailed analysis of the influence mechanism of the surface fouling layer on the total reflected beam, the design scheme of the optical waveguide and the light source is proposed. A large number of sample data were obtained through the calibration tests under different humidity, different pollution grades and different lime-salt ratios, and an artificial neural network model was established and trained accordingly. The test results and operation practice show that both the salt density and grey density can be monitored simultaneously by using the device and the embedded neural network model after training, and the precision of the test meets the requirements of relevant standards.
【作者单位】： 第二炮兵工程大学;西安金源电气股份有限公司;
【基金】：国家自然科学基金资助项目(61102170)~~
【分类号】：TM855.2
[Abstract]:Based on the propagation characteristics of optical waveguide boundary and artificial neural network, an on-line pollution monitoring device is introduced, which can simultaneously monitor the equivalent salt density and grey density of insulator surface of transmission line. Based on the detailed analysis of the influence mechanism of the surface fouling layer on the total reflected beam, the design scheme of the optical waveguide and the light source is proposed. A large number of sample data were obtained through the calibration tests under different humidity, different pollution grades and different lime-salt ratios, and an artificial neural network model was established and trained accordingly. The test results and operation practice show that both the salt density and grey density can be monitored simultaneously by using the device and the embedded neural network model after training, and the precision of the test meets the requirements of relevant standards.
【作者单位】： 第二炮兵工程大学;西安金源电气股份有限公司;
【基金】：国家自然科学基金资助项目(61102170)~~
【分类号】：TM855.2
【参考文献】
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