摘要
分析了绝缘子工作气候环境如酸雨、雾、空气污染指数等以及绝缘子电压等级和型号等对绝缘子污秽物形成的影响。在此基础上,引入BP神经网络,研究基于BP神经网络和气象统计的绝缘子污闪预警方法,以绝缘子工作气候环境、电压等级、型号等11个参量为输入特征量,以污闪发生概率为输出特征量,设计相应的绝缘子污闪预警模型。试验结果表明,该方法有较好的预测能力,具有可行性。
Forecasting the pollution flashover situation of insulator has important guiding significance for the prevention of pollution flashover. The influence of working environment such as acid rain, fog, air pollution index as well as the voltage level and model of the insulator on insulator contamination grades is analyzed. Based on which, BP neural network technology is used to study the early warning method for insulator pollution flashover based on BP network and meteorological data, eleven parameters including work climate environment, voltage level and model of the insulator are taken as the input value, the probability of the pollution flashover is taken as the output value,and the warning model of the insulator pollution flashover is built. The test results show that the method proposed in the paper has good predictive ability and feasibility.
出处
《陕西电力》
2012年第11期8-11,33,共5页
Shanxi Electric Power
基金
国家自然科学基金资助项目(50677047)
湖北省自然科学基金资助项目(2010CDZ051)
湖北省教育厅基金资助项目(20092505)
关键词
绝缘子
污闪
气象
神经网络
insulator
pollution flashover
climate
neural network
