基于加权平均插值和容积卡尔曼滤波的配电网预测辅助状态估计

微型同步相量测量单元(micro synchronous phasor measurement unit, μPMU)与远程终端(remote terminal unit, RTU)为配电网提供了高精度量测数据。针对μPMU和RTU混合数据,文章采用数据填补和容积卡尔曼滤波技术,提出了配电网混合量测的预测辅助状态估计(forecasting aided state estimation, FASE)算法。首先,针对混合量测数据不同更新周期,综合考虑历史数据稳定性和线性插值的同步特性,采用平均插值实现数据的综合填补。然后,采用容积卡尔曼滤波器,构建状态预测、量测预测和滤波修正方程,提出混合数据的配电网预测辅助状态估计算法。最后,在IEEE 37节点系统验证了该算法的有效性。

A Forecasting Aided State Estimation for Distribution Network Based on Weighted Average Interpolation and Cubature Kalman Filter

The micro synchronous phasor measurement unit (μPMU) and remote terminal unit (RTU) provide high precision measurement data for distribution network. This paper presents a forecasting aided state estimation (FASE) method based on hybrid measurement data, which uses data imputation and the cubature Kalman filter to improve the synchronous and filtering profile of μPMU and RTU. Firstly, to improve the synchronous profile of RTU with the shorter updating period, the historical data and linear interpolation are considered comprehensively to realize the data imputation by weighted average interpolation. Then, the cubature Kalman filter is used to construct a state prediction equation, measurement prediction equation, and filter correction equation. A FASE for distribution network with mixed data is proposed. Finally, the validity of the proposed method is verified in the IEEE 37-node system.