Abductive identification of bad data: methodology and field test

This study describes a solution to discriminate the original sources of bad data which are concerned by the substation maintainers. Bad data caused by defects of monitoring system, such as mal‐setting of monitoring devices or time skew, are concerned here. These bad data can be identified mostly in electric power control centre by bad data identification (BDI) methods including robust state estimation; however, these methods can hardly explain the relevant original sources of bad data in substations. Here, the authors propose a data‐driven method named abductive identification (AbI) to discriminate the original sources of bad data already identified by BDI methods. In the AbI method, feature patterns of bad data, based on their residuals, are investigated. A feature pattern selection method for bad data is then proposed, in which both the absolute residual and relative residual are considered. Based on this feature selection approach, a Bayesian classifier is developed to identify the original sources of bad data. A two‐level architecture for the AbI approach is introduced to deal with diverse bad data patterns of wide‐area networks. Simulation and field test results demonstrate the applicability of the proposed method.