动态传输下基于改进卡尔曼滤波的电力计量计费数据状态估计

State Estimation of Electricity Metering and Charging Data Under Dynamic Transmission Based on Improved Kalman Filter

电力计量计费数据是电力营销业务公平公正实施的重要基础,具有数量总量大、通信方式多样等特点。为更加合理地利用通信资源并提升数据的可靠性,提出了一种动态传输下的改进扩展卡尔曼滤波方法用于电力计量计费数据的动态状态估计。首先,利用动态传输策略有选择地将区域电力计量计费数据传输到用户用电信息采集平台。然后,提出了一种改进扩展卡尔曼滤波方法,对电力计量计费数据进行动态状态估计,该算法利用不确定项表示线性化误差,在保证状态估计精度的基础上提高了计算速度。最后,用标准的IEEE-33配电网用户电表数据案例验证了该算法的可行性。

Electricity metering and charging data is an important basis for the fair implementation of the power marketing business,and it is characterized by characteristics such as a large quantity and various communication modes.To make use of the communication resources more reasonably and improve the data reliability,an improved extended Kalman filter(EKF)method under dynamic transmission is proposed for the dynamic state estimation of electricity metering and charging data.First,a dynamic transmission strategy is used to selectively transmit the regional electricity metering and charging data to the user electricity information acquisition platform.Then,the EKF method is put forward,which utilizes an uncertainty term to represent the linearization error and improves the computational speed on the basis of guaranteeing the state estimation accuracy.Finally,the user electricity meter data from a standard IEEE 33-bus distribution network system is used to verify the feasibility of the proposed method.