Abstract
Bad data detection in the initial information on the electric power system state is one of the most topical problems in the state estimation problem solution. The paper presents a method for bad data detection which is based on the analysis of the retrospective and forecasting information on state variables. The retrospective information is understood as the values of measurements and estimates taken from the previous snapshot. The forecasting information is obtained as a result of dynamic state estimation which rests on the extended Kalman filter. The suggested method demonstrates satisfactory results at measurement testing in the context of low information redundancy.
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