基于WAMS/SCADA混合量测和离散粒子群优化算法的PMU优化配置

当前应用于状态估计的量测数据由广域测量系统(wide area measurement system, WAMS)和数据监控及采集系统(supervisory control and data acquisition,SCADA)采集, WAMS向量测量单元(phasor measurement unit,PMU)的优化配置问题成为研究的重点。本文在分析WAMS/SCADA混合量测数据成分、时间断面、精度、刷新频率4个方面差异的基础上,实现了混合量测数据的有效兼容,提出了一种基于无迹卡尔曼滤波(unscented kalman filter, UKF)动态状态估计和离散粒子群优化(discrete particle swarm optimization, DPSO)算法的PMU优化配置方案。采用该方案下的混合量测数据进行UKF动态状态估计,很好地提高了状态估计精度。在IEEE39节点系统上模拟日负荷变化验证了该PMU配置方案的有效性。

PMU Optimized Placement Based on Data Compatibility of WAMS/SCADA and Discrete Particle Swarm Optimization Algorithm

The data for state estimation are measured by wide area measurement system (WAMS) and supervisory control and data acquisition (SCADA) currently, and the optimal placement of phasor measurement unit (PMU) in WAMS system becomes key research aspect. In this paper, based on the analysis of differences on data composition, time section, accuracy and refresh rate for WAMS/SCADA hybrid data measuring, an effective compatible scheme is realized, and a PMU optimized placement strategy is proposed based on dynamic state estimation by unscented kalman filter (UKF) and discrete particle swarm optimization (DPSO) algorithm. By applying hybrid measurement data to estimate UKF dynamic state, precision of state estimation is improved greatly. The simulation results of daily load changes in IEEE 39-node system verify the validity of this PMU placement scheme.