基于AMI全量测点分区的配电网动态状态估计方法

针对传统配电网三相不平衡动态状态估计存在计算速度较慢且估计精度较低的问题，提出了一种基于高级量测体系(AMI)全量测点分区的配电网的动态状态估计方法。以AMI全量测点作为配电网分区节点，提出综合3个指标的分区目标函数对配电网进行分区，可对子区域进行完全解耦，缩小系统规模；并通过所提数据融合框架进行多尺度量测数据的融合，以远程终端单元量测周期为基准，融合量测周期较长的AMI量测数据，对非AMI量测时刻的系统状态进行跟随。提出一种基于子区域数据融合方法的高精度集合卡尔曼滤波算法，采用协方差膨胀法改进滤波发散的问题。算例仿真结果表明所提方法有效地提高了配电网动态状态估计的计算速度和估计精度。

Dynamic state estimation method of distribution network based on partition of AMI total measurement points

Aiming at the problems of slow calculation speed and low estimation accuracy of traditional three-phase unbalance dynamic state estimation of distribution network, a dynamic state estimation method based on partition of advanced metering infrastructure(AMI) total measurement points is proposed. Taking the AMI total measurement points as the partition nodes of distribution network, the partition objective function integrating three indexes is put forward to partition the distribution network, which can completely decouple the sub-regions and reduce the system scale. The multi-scale measurement data is fused through the proposed data fusion framework. Based on the measurement cycle of the remote terminal unit, the AMI measurement data with a long measurement cycle is fused and the system state at non-AMI measurement time is followed. A high-precision ensemble Kalman filtering algorithm based on sub-region data fusion is proposed and the covariance expansion method is used to improve the divergence problem of filter. The simulative results show that the proposed method can effectively improve the calculation speed and estimation accuracy of distribution network dynamic state estimation.