A linear recursive bad data identification method with real-time application to power system state estimation

A fast and efficient algorithm, the recursive measurement error estimation identification (RMEEI) method, for bad data (BD) analysis is further developed. By using a set of linear recursive formulas, state variables, residuals, and their variances are updated after the removal of a measurement from a suspected data set to the remaining data set, or in the reverse direction. Neither a re-estimation nor a residual sensitivity matrix are needed in the identification process, which increases the computational speed greatly. Digital tests have been done to compare the RMEEI method with other conventional BD identification methods in terms of identification performance and computational speed. The real-time operational experience of the RMEEI method in the northeast China power system is outlined