Autoregressive Filters for the Identification and Replacement of Bad Data in Power System State Estimation

The quadratic cost function J(xˆ) and the normalized residuals rN are used conventionally for identifying the presence and location of bad measurements in power system state estimation. These are "post estimation" tests and therefore require the complete re-estimation of system states whenever bad data is identified. This paper presents a pre-estimation filter for detection and identification of gross measurement errors. The basic function of this filter is to compare the measured value of a system variable with its predicted value obtained using an autoregressive (AR) model. If the difference exceeds a pre-determined threshold, the measured value is discarded in favor of the predicted value. Each measurement is processed by an AR filter before being used in the state estimation. The performance of the AR filter is tested against that of the J(xˆ) and rN tests and the results are reported in this paper. The principal advantage of the AR filtering scheme is its speed in bad data identification. Furthermore, it can be used to complement other bad data processing methods.