用于电力系统状态估计的WAMS/SCADA混合量测数据融合方法  

Data Fusion Method of WAMS/SCADA Hybrid Measurements in Power System State Estimation

作　　者：李从善[1] 刘天琪[1] 李兴源[1] 吴星[1] 

机构地区：[1]四川大学电气信息学院,成都610065

出　　处：《高电压技术》2013年第11期2686-2691,共6页High Voltage Engineering

基　　金：国家自然科学基金(51037003)~~

摘　　要：为了充分利用相量测量单元(PMU),并与数据监控及采集系统(SCADA)相结合以提高状态估计精度,详细分析了广域测量系统(WAMS)和SCADA这2套系统数据存在的4种差异(数据成分、传输延时、刷新频率、数据精度)。给出了2套数据在线性、非线性以及混合非线性3种估计模型下的数据成分处理方法;提出了全球定位系统(GPS)对时与时延校正相结合的方法以提高数据断面一致性;采用分段曲线拟合方法来填补PMU上传时刻SCADA数据的空缺,建立了多时间标尺混合量测预处理数据集,它可以用于多种时间尺度下的状态估计。在4节点测试系统潮流基础上叠加随机误差,形成量测数据,采用加权最小二乘估计算法,验证了该方法的有效性。In order to make full use of phasor measurement units （PMU）, and to combine the units with supervisory control and data acquisition （SCADA） measurements for improving the precision of state estimations, we analyzed four major differences （i. e. in data components, transmission delay, refresh rate, precision of data） between wide area measurement systems （WAMS） and SCADA. Then we presented a method of data component processing in three kinds of estimation model including linear, nonlinear and mixed nonlinear, as well as a combined method of global positioning system （GPS） synchronization and delay correction for improving the consistency of data section. The subsection curve fitting method was used to fill the vacancy of SCADA data when PMU are uploading, and a multiple time-scale data set was established for state estimations in multiple time scales. Based on simulations of a four-node test system with random errors considered, we obtained measurement data for further tests, and adopted a weighted least squares estimation algorithm to verify the proposed methods.

关 键 词：状态估计 PMU WAMS SCADA GPS对时 时延校正 分段曲线拟合 多时间标尺 

分 类 号：TM73[电气工程—电力系统及自动化] TP202[自动化与计算机技术—检测技术与自动化装置]