【摘要】 混合量测下电力系统动态状态估计算法,目前主要假设量测信息在同一时间获得,且很少考虑量测异常值对状态估计的影响。针对以上不足,充分利用混合量测的特性,在分析监控及数据采集系统(SCADA)数据取用时间窗口的基础上,提出了混合量测下序贯抗差状态估计算法。该算法首先序贯入同一时间断面上的同步相量测量单元(PMU)量测值,然后依次序贯入陆续到来的SCADA量测值。初步融合完成后计算融合代价函数,如果超过设定阀值,则增广量测矩阵形成混合量测集,通过扩展卡尔曼滤波(EKF)进行抗差状态估计。该算法能够提高估计中心的利用率以及状态估计的实时性和精度,降低量测异常值对状态估计的影响。最后在IEEE14节点系统上进行了仿真,验证了该算法的有效性和优越性。更多还原

【Abstract】 Most power system dynamic state estimation algorithms under hybrid measurements assume that the information can be available at the same time and rarely consider the impact of bad data on state estimation. Aiming at the problems above and taking full advantage of hybrid measurements,a sequential robust dynamic state estimation is proposed based on the analysis of the SCADA estimation time window of the mixed- measure system. Firstly,the algorithm fusions the phasor measurement unit(PMU) measurements sampling at the same time and then the supervisory control and data acquisition(SCADA) measurements are taken into estimation in sequential of arrival time. Then the cost function is calculated. A set of SCADA / PMU measurements is formed and used for state estimation via extended Kalman filter(EKF) if the cost function exceeds a threshold The algorithm can make good use of utilization of the estimation center,reduce the impact of measurement outliers on the state estimation and improve state estimation real- time and accuracy. Finally,simulations on an IEEE 14- bus test system demonstrate the effectiveness and superiority of the proposed algorithm.更多还原