基于数据融合技术的电力系统鲁棒动态状态估计方法

Robust Dynamic State Estimation Method for Power Systems Based on Data Fusion

针对电力系统在连续多断面动态状态估计中,现有混合数据处理方法存在的两者相互影响以及两阶段处理方法存在的效率低下问题,基于数据融合理论,提出了基于数据融合技术的电力系统鲁棒动态状态估计方法。首先基于改进变点重复检测方法,将PMU和SCADA数据进行断面归一化处理。然后,在每一断面分别利用无迹变换与指数权估计方法相结合进行并行滤波处理,得到两组滤波结果,并基于数据融合技术将两组滤波结果进行状态融合以得到最终估计值。最后,基于IEEE-39节点标准系统对本文所提方法进行仿真,验证了本文所提方法不仅能够有效提高估计结果精度,且基于数据融合技术的并行滤波计算大大提高了计算效率。

Targeting the problems of the mutual influence of existing hybrid data processing methods and the low efficiency of two-stage processing methods in continuous multi-section dynamic state estimation(DSE)of power systems,this paper proposes a robust DSE method for power systems based on data fusion.Firstly,PMU and SCADA data are normalized in sections based on the improved change point repeated detection method.Then the unscented transform and exponential weight estimation method are combined to carry out parallel filtering processing to obtain the two sets of filtering results.Furthermore,the two sets of filtering results are fused to obtain the final estimated value based on the data fusion.Finally,the IEEE 39-node standard system is used to make a simulation of the proposed method.The results verifies that the proposed method can not only effectively improve the accuracy of estimation results but also greatly improve the computational efficiency of parallel filtering calculation based on the data fusion.