Linear state estimation and bad data detection for power systems with RTU and PMU measurements

Abstract

The reliability of state estimation is of vital importance for secure operation of a power system. The key features of any state estimator are its accuracy, robustness against bad data and the associated computational burden. In order to ensure full system observability and maximise accuracy, a state estimation algorithm needs to take into account all available measurements. A significant number of hybrid estimators that can process both the conventional measurements that have been used in transmission systems for decades, as well as the synchrophasor measurements, can be found in literature. However, these algorithms are predominantly non-linear, thus requiring iterative solving procedures. In this study, a novel linear algorithm is proposed for state estimation including bad data detection of power systems that are monitored both by conventional and synchrophasor measurements. The proposed estimator is based on the linear weighted least square framework. As a result, the computational burden is rendered extremely low since there is no need for an iterative solving procedure, and standardised post-processing tools for bad data detection can be used. To validate the accuracy and robustness of the proposed algorithm, an extensive number of test cases of different sizes are solved and the results are presented and discussed.