计及广域测量信息的状态估计错误参数识别与修正

因电网元件参数可能随工作环境变化而改变、遥信量在干扰下可能出现错误，需要对状态估计中的错误参数进行识别和修正。根据当前监控与数据采集（SCADA）系统和广域测量系统（WAMS）量测共存的状况，引入WAMS量测求得的功率残差和零注入节点功率残差，与SCADA量测残差一起构成拉格朗日函数，由优化理论得出参数误差与各残差的灵敏度关系，从而进一步识别和修正错误参数。所述方法不仅避免了增广参数估计维数高的问题，还利用了高精度的相量量测信息。在标准测试系统上的仿真结果验证了方法的有效性。

Recognizing and Correcting the Wrong Parameters in State Estimation Considering the WAMS Measurements

Since the network parameters may vary with the changing power gird operation conditions and the remote information may also contain errors under some contingency, there could be parameter errors in the state estimation process. In order to provide a correct operation state with the sate estimation, the parameter errors must be recognized and corrected. Currently in a power system there are two measurement passes, viz. the supervisory control and data acquisition (SCADA) system and the wide area measurement system (WAMS). This paper aims at constructing Lagrange function by combining the SCADA measurement residuals with the power flow residuals of zero-injection node and residuals from WAMS measurements, and to get the sensibility matrix with respect to the residuals and parameter errors based on the optimization theory. With the obtained sensibility matrix, the parameter errors can be recognized and corrected. The proposed method can effectively overcome high dimensionality problem of the augmented state estimation. It can also utilize high precision phasor measurements from phasor measurement units (PMU). The efficiency of the proposed method has been tested on a standard test system.