Application of Kalman Filter to Bad-Data Detection in Power System

Abstract

Bad-data detection in pre-estimation can help to improve state estimation [Teeuwsen & Erlich, 2006]. Since transient and abnormal conditions may occur in a power system, measurements may be polluted by bad data to cause estimated errors. Hence, it is necessary to accurately detect the bad data. To ensure the reliability of the measured data, a practical state estimator should have an ability to detect and identify the bad data as well as to eliminate their effects on the estimation [Zhang & Lo, 1991]. Generally, bad-date detection is important to guarantee the reliability of the measured data. If one or more errors occur in power system measurements, the states of the estimated system may be biased and the safety of power supply may be potentially dangerous. To avoid this situation, several bad-data detection and identification schemes have been presented. For example, WLS (weighted least squares) was proposed in 1989 [EI-Keib et al., 1989]. The weighted sum of squares of the measurement residuals was chosen as the objective function to be minimized. But, WLS-based state estimators were only developed by using a linearized measurement function [Huang & Lin, 2003] with complicated computations. Then, linear programming (LP) was proposed to improve the identification method [Peterson & Girgis, 1988]. However, the LP estimator may fail to reject the bad data and it can be attributed to the existence of leverage in the power system model [Ali Abur, 1990]. Thus, Ali Abur had proposed hypothesis testing identification (HTI) to extend the case of the LP estimator. Nevertheless, it had caused computational burdens with taking the special properties of the LP estimation equations into account. Huang [Huang & Lin, 2003] proposed a changeable weighting matrix to identify the bad data but it only can apply for static state estimations. Nevertheless, Zhang had proposed recursive measurement error estimation identification (RMEEI) for bad-data identification [Zhang et al., 1992]. State variables, residuals and their parameters can be updated after removing a measurement from the suspected data set to the remaining data set by using a set of linear recursive equations. With splitting the raw measurements into some parts, a set of residual equations used by the traditional methods can only apply to linear systems and it may result the operation of calculation burden and complexity because each part consists of some measurements [Zhang et al., 1992]. Source: Kalman Filter, Book edited by: Vedran Kordic, ISBN 978-953-307-094-0, pp. 390, May 2010, INTECH, Croatia, downloaded from SCIYO.COM