An Enhanced State Estimator for Bad Data Detection using PMU Measurements

Abstract

The deployment of phasor measurement units (PMUs) by power electric utilities to enhance the operating capabilities of state estimators is an emerging trend around the world. In the literature, several publications introduced PMU measurements into traditional state estimators to boost their estimation accuracy. One of the essential functions of a state estimator that could potentially benefit from PMU technology is bad data detection and identification. Bad data are gross errors coming from flawed measurement devices and has the potential to affect the estimation results leading to incorrect information of the system status. Therefore, state estimators are required to be equipped with advanced bad data detection techniques. One of the most commonly used bad data detection techniques is the largest normalized residual test (LNRT). However, it is known to fail to certain measurements known as critical measurements. In this thesis, a state estimator (SE) based on weighted least squares (WLS) was developed and evaluated against Iterative Kalman Filter (IEKF). For bad data detection, largest normalized residual test (LNRT) was integrated into the WLS estimator. All simulations were conducted using the IEEE 14 Bus test system simulated in Matlab. From the simulation results, utilizing a fixed residual threshold was demonstrated to be less sensitive to detect bad data. Adequate threshold value was found to be dependent on network conditions and measuring redundancy. Furthermore, simulation results demonstrated the utilization of strategically placed PMU measurements into LNRT could eliminate the issues associated with critical measurements. The improved LNRT could detect and identify any bad data irrespective of its location. In summary, the enhanced technique improved the bad data detection capability and corresponding state estimation accuracy.

Date of Award	Aug 2015
Original language	American English
Supervisor	Jimmy Peng (Supervisor)