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Optical fiber sensor system for monitoring leakage current of post insulators based on RBF neural network

机译:基于RBF神经网络的绝缘子泄漏电流监测光纤传感器系统。

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With the construction and operation of China's EHV and UHV power grid, the voltage level of transmission line has been increasing, and with the rapid development of industry and agriculture, and the increasing level of atmospheric contamination, pollution flashover has become a serious threat to the safe operation of the electric power system. Leakage current monitoring has been widely employed to estimate pollution level on high voltage insulators. Therefore, an optical fiber sensor system for monitoring leakage current of post insulators based on RBF neural network for pollution evaluation is introduced in this paper which contributes to design a practical used system for flashover predicting. This system is composed of a fiber optic sensor, humidity and temperature sensors, a signal processing module, a data collection module based on micro-controller, a wireless signal transmission module and power supply with solar panels and batteries. Measured data are analyzed by the RBF neutral work, which measures four parameters (amplitude, RMS, number of the impulse and relative humidity(RH)) reflecting the insulating property of an insulator and outputs the pollution level and risk level of pollution flashover. After enough of training, the outputs of the network show great agreement with the experiment results. And this system has been applied in actual operation.
机译:随着我国超高压,特高压电网的建设和运行,输电线路的电压水平不断提高,随着工农业的飞速发展,大气污染水平的日益提高,污染闪络已成为对我国的严重威胁。电力系统的安全运行。漏电流监控已被广泛用于估算高压绝缘子的污染水平。因此,本文介绍了一种基于RBF神经网络的后绝缘子漏电流监测光纤传感器系统,用于污染评估,有助于设计实用的闪络预测系统。该系统由光纤传感器,湿度和温度传感器,信号处理模块,基于微控制器的数据收集模块,无线信号传输模块以及带有太阳能电池板和电池的电源组成。通过RBF中性工作对测量数据进行分析,该测量工作测量反映绝缘子绝缘性能的四个参数(幅度,RMS,脉冲数和相对湿度(RH)),并输出污染水平和污染闪络的风险水平。经过足够的培训,网络输出与实验结果非常吻合。并且该系统已在实际运行中得到了应用。

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