考虑数据时延的电力系统两阶段动态状态估计方法  

Two-Stage Dynamic State Estimation Method Based For Power System Considering Data Delay

作　　者：柳翔林 向小民[1] 杨哲 沈丹青 何丽娜 陈汝科 杨凯帆 谢枭 LIU Xianglin;XIANG Xiaomin;YANG Zhe;SHEN Danqing;HE Lina;CHEN Ruke;YANG Kaifan;XIE Xiao(College of Electrical Engineering&New Energy,Three Gorges University,Yichang 443000,China;State Grid Hubei Zhongxiang Power Supply Company,Zhongxiang 431900,China)

机构地区：[1]三峡大学电气与新能源学院,湖北宜昌443000 [2]国网湖北钟祥市供电公司,湖北钟祥431900

出　　处：《智慧电力》2021年第4期75-81,共7页Smart Power

基　　金：国家自然科学基金资助项目(61876097)。

摘　　要：由于电力系统中SCADA数据和PMU数据采样频率不同,使得这两种数据存在时延。首先提出基于变点重复检测的PMU最佳缓冲长度计算方法,将SCADA数据和PMU数据统一到同一时间尺度下,然后将无迹变换与指数权函数抗差估计算法相结合,针对历史多数据断面进一步提出了两阶段无迹卡尔曼滤波鲁棒动态状态估计方法。该方法在每一断面内,首先用无迹变换和两参数指数平滑预测后的预测值与SCADA数据结合进行第一阶段滤波,然后再将滤波所得估计值与PMU数据结合进行第二阶段滤波。通过两阶段滤波,能够显著增大滤波过程中的量测冗余度,并且有效降低在混合数据滤波过程中量测精度较低的SCADA量测对精度较高的PMU量测的影响。基于IEEE-39节点标准系统对本文所提方法进行仿真,仿真结果表明,本文所提方法能够有效结合PMU数据和SCADA数据对电力系统进行动态状态估计计算,且估计精度高,鲁棒性好。Due to the different sampling frequencies of SCADA data and PMU data in power system,there is a problem of data delay.The SCADA data and PMU data are unified to the same time scale based on the method of PMU optimal buffer length,and then combines the unscented transformation with the exponential weight function robust algorithm to propose a two-stage unscented Kalman filter dynamic state estimation method.In each snapshot,firstly,the method combines the predicted value after unscented transformation and two-parameter exponential smoothing prediction with SCADA data for the first stage of filtering,and then combines the estimated value obtained by first stage with PMU data for the second stage of filtering.This method can obviously increase the measurement redundancy in the filtering process through two-stage filtering,and effectively reduce the impact of SCADA measurement with lower measurement accuracy on PMU measurement with higher measurement accuracy in the process of mixed data filtering.Based on the IEEE-39 buses standard system,the proposed method is simulated and analyzed.The simulation results show that the proposed method can effectively combine the PMU and SCADA data to calculate the dynamic state of the power system with high estimation accuracy and good robustness.

关 键 词：PMU和SCADA 数据时延 两阶段 无迹卡尔曼滤波 动态状态估计 

分 类 号：TM744[电气工程—电力系统及自动化]