电子式电流互感器误差模型及误差状态预测方法

Error model and forecasting method for electronic current transformers

为及时发现电子式电流互感器误差状态的稳定性问题,保证电能贸易结算的公平性,有必要对电子式电流互感器的误差状态进行预测。文中建立了电子式电流互感器误差模型,将其误差表征为单输出变量和多输入变量的理论模型,确定了模型输入变量和输出变量。针对模型输入变量和输出变量之间无明确函数关系的问题,提出基于聚类径向基函数(RBF)神经网络的误差状态预测方法,针对变量单位和数量级不同的问题,采用Z-score标准化法对数据进行预处理,为了简化神经网络,采用k-means聚类算法对输入变量进行聚类分析。算例分析结果表明,比差预测误差的绝对值小于0.05%,角差预测误差的绝对值小于10’。该预测方法可提供电子式电流互感器误差状态的变化信息,防范电能贸易结算的风险。

Predicting the error of electronic current transformers is significance for tackling the long-term stability problem of electronic current transformers’ error in time and ensuring the validity of electric power trade. The error model for electronic current transformers is established,where the error of electronic transformers is taken as a theoretical model with one input and multiple outputs,and the input and output variables are determined. Since the relationship between the input and output variables is indistinct,a forecasting method for electronic transformers based on clustering RBF neural network is proposed. The data are pre-processed using Z-score normalization method to avoid the problem of different variable magnitude and unit. The input variables are analyzed by K-means clustering method to simplify the neural network. The numerical example suggests that the predicting error of ratio error is less than 0. 05% and the predicting error of phase error is less than 10’. The method provides an effective approach to analyzing the development state of electronic current transformers in operation and to managing the instruments actively,as a result,the risk of electric energy trade can be alleviated.