基于双重人工神经网络的XP-70绝缘子串污闪概率模型的建立

Risk Forecasting of Contaminated Insulator Flashover Using Double Artificial Neural Networks

针对高压电网运行中绝缘子污闪风险概率评估的困难,建立了XP?70(9片串)绝缘子的基于双重人工神经网络的污闪概率模型。污闪概率模型比较准确地反映了环境因素、等值附盐密度(ESDD)、污秽闪络电压水平及污秽闪络概率之间的非线性关系,建立了包括估计绝缘子的自然积污污秽度、预测绝缘子污秽闪络电压值及根据前两个模型的输出结果计算出该绝缘子污闪的风险概率大小的三个子模型。进行了针对三个子模型的一系列试验,试验结果表明,该预测模型的预测结果基本满足工程需要,具有实用价值。

To solve the problem of the flashover forecasting of contaminated insulator, a flashover forecasting model of contaminated insulator based on double artificial neural networks （ANNs） is proposed in the paper. The forecasting model consists of three artificial neural networks that reflect relationship of environment, equivalent salt deposit density （ESDD）, flashover voltage, and flashover probability. The first is used to estimate the ESDD of insulator and the second to forecast the flashover voltage of the insulator in complex ambient conditions, and then the third is employed to calculate the probability of the flashover. A series of artificial pollution tests show that the results of the forecasting model is acceptable.