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.
Distribution Network Restoration Decision Considering Risk of State Estimation Error in Restoration Process
Time-varying Virtual Inertia and State Estimation of Grid-following Converter Based on Maximum Correntropy Criterion and Polynomial Chaos Kalman Filter
Related Author
ZHANG Yanjun
GE Yanfeng
GAO Kai
ZHOU Suquan
WANG Zhiqiang
LIU Wenxia
FAN Yongfeng
LIU Nian
Related Institution
Liaoning Electric Power Company Limited
Harbin Institute of Technology
North China Electric Power University
Henan Electric Power Companies
College of Electronics and Information Engineering, Tongji University