Identifying bad parameters of state estimation considering the WAMS measurements

Owing to the network parameters may alter with the changing power grid operation 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 state estimation, the parameter errors must be identified. Currently in the power system, there are two measurement passes, viz. the supervisory control and data acquisition system (SCADA) and wide area measurement system (WAMS). This paper aims at constructing Lagrange function by combining the SCADA measurement residuals with the introduced residuals of zero-injection node power flows and WAMS measurements, and to get the sensibility matrix with respect to the residuals and parameters errors based on the optimization theory, with the obtained sensibility matrix, the parameters errors can be identified. The proposed method not only can effectively overcome dimensionality curse of the augmented state estimation, but also utilize high precision measurements of WAMS. The efficiency of the proposed method has been proved on the standard test system.