The largest studentized residual test for bad data identification in state estimation of a power system

Power system state estimation is a reliable tool used in Energy Management System (EMS) to identify the existence state of the system during its operating hours. The results of this estimation are values for unknown state parameters of the power system. The presence of systematic errors can alter the results of state estimation. The chi-square and normalized residual tests are the common post estimation procedures usually used for detection and identification of gross errors in the estimation algorithm. These tests are based on two separate test statistics and are not so powerful for detection of smaller magnitudes of gross errors. In this paper, an implementation of largest studentized residual (LSR) test is presented that combines both the results of chi-square and normalized test for detection and identification of bad data. Based on LSR test, a comprehensive strategy is developed for detection and identification of multiple gross errors which may exist simultaneously in the data. A six-bus power system data is used for the application of LSR test for detecting and identifying the gross errors in the processed measurements. The reporting results are presented showing that the method is most powerful and effective for practical implementation in conventional procedures of the state estimation problem. © 2006-2015 Asian Research Publishing Network (ARPN).