针对当前应用于状态估计的广域测量系统(wide area measurement system, WAMS)和数据监控及采集系统(supervisory control and data acquisition,SCADA)数据频率兼容问题,在分析WAMS/SCADA混合量测数据差异的基础上,提出了一种可行的解决混合量测数据频率兼容的方案。本文基于数据挖掘理念和Vondrak分区插值算法,对SCADA节点依据数据相关度划分插值区域,各分区内采用同一PMU节点的最优平滑系数进行Vondrk插值,得到WAMS 测量时刻的SCADA拟量测数据,应用于无迹卡尔曼滤波(Unscented Kalman Filter,UKF)动态状态估计。该方案不仅可以增补SCADA拟量测数据,提高量测数据和状态估计精度,有效控制系统负荷快速变化时的估计误差,还可以实现系统故障前后全网母线电压波动的可观测。通过在IEEE 118节点系统上模拟日负荷变化和故障过程的仿真分析,验证了该频率兼容方案的有效性。
英文摘要:
ABSTRACT: In allusion to the current data frequency compatibility of hybrid measurements combined of supervisory control and data acquisition (SCADA) and wide area measurement system (WAMS) applied in state estimation, based on the analysis of hybrid measurements data differences, a feasible solution for the frequency compatibility of hybrid measurements data was proposed. This article based on data mining and Vondrak interpolation algorithm, divided interpolation area according to the data correlation of SCADA nodes, and each partition used the same optimal smoothing factor of phasor measurement unit (PMU) node to do Vondrak interpolation. We can get SCADA virtual measurement data corresponding to WAMS measurement time, applied to dynamic state estimation based on Unscented Kalman Filter (UKF). The method can not only augment SCADA virtual measurement data, improve the accuracy of measurement data and state estimation, control the estimation error effectively when load in system changes rapidly，but also realizes observation of all bus voltages before and after faults. The simulation results of daily load changes and failure process in Institute of Electrical and Electronics Engineers (IEEE) 118 node systems show the validity of data frequency compatibility method.