基于时序建模的光纤电流互感器随机噪声卡尔曼滤波方法

针对光纤电流互感器(FOCT)随机噪声特性及其对继电保护、电能计量等间隔层设备的影响,建立FOCT随机误差的时序模型,并采用滤波方法有效提高了FOCT测量精确度。首先,预处理和统计检验FOCT原始数据,获取数据随机特征;根据赤池信息准则(AIC)准则选择时间序列模型的阶次,求出模型系数建立FOCT随机误差的ARMA(2,1)模型,并检验其适用性;采用卡尔曼滤波方法对FOCT输出数据进行滤波处理。总方差分析结果表明:建立的FOCT时序模型经卡尔曼滤波后,随机噪声幅值明显减小,方差值降低了两个数量级,各项随机噪声的误差系数均下降一个数量级,采用的时序建模和卡尔曼滤波方法能有效减小FOCT的随机噪声,提高电流信息的测量精确度。

Kalman filter offiber optical current transducer's stochastic noise based on time series model

Due to the effects on the devices like relay protection and power metering,created by stochastic error characteristic of fiber optic current transducer (FOCT),modeling online and filtering real-time can effectively improve measurement accuracy.At first,pretreating and inspecting statistically the FOCT data is essential to characterize the stochastic error of FOCT.Then,set order for the time series model by Akaike information criterion (AIC) rule and acquire model coefficients to establish ARMA(2,1) model.Next,test the applicability of the established model.Finally,Kalman filter is adopted to process the FOCT data.Simulation results of total variance demonstrate that stochastic error is obviously decreased after Kalman filtering based on ARMA (2,1) model.Besides,variance is reduced by two orders,and every coefficient of stochastic error is reduced by one order.The filter method based on time series model does reduce stochastic noise of FOCT,and increase measurement accuracy.