基于混合量测的电力系统动态状态估计

针对目前广域量测量无法单独进行状态估计的问题，引入部分SCADA功率量测与广域量测一起构成混合量测系统，提出了基于混合量测的动态状态估计算法。该算法采用扩展卡尔曼滤波算法实现状态预测与滤波，并能利用精度高和短期更新的广域量测数据去提高状态滤波效果。仿真分析表明，当广域量测在混合量测数据所占比例逐渐增加以及广域量测更新周期缩短后，状态预测和滤波结果精度均会有明显提高。

Mixed Measurement Based Dynamic State Estimation of Electric Power Systems

At present the phasor measurements of Wide-Area Measurement Systems are not sufficient for state estimation in most practical power systems. To slove this problem this paper presents a new dynamic state estimation algorithm which utilizes all the phasor measurements and partial SCADA measurements. This algorithm adopts Extended Kalman Filter EKF to perform state vector forecasting and filtering, and can utilize metered WAMS measurements of high accuracy and short-time update to improve estimated state variables. Numerical simulation shows that the accuracy of predicted and filtered state vector will be enhanced when the proportion of WAMS measurements increases or the update time of WAMS measurements is shorten in the mixed measurements.