State Estimation for Active Distribution Networks Considering Bad Data in Measurements and Topology Parameters

SE is critical in ADNs integrating renewable DGs. Traditional SE methods face the challenges of increasing SE errors and decreased robustness due to the adverse impact of bad data in measurements and topology parameters. To address these issues, this paper proposes a robust SE method that considers bad data in measurements and topology parameters. First, a bad measurement data processing model is proposed to improve measurement and SE accuracy by generating high-precision pseudo-measurements through adaptive learning from historical data sequences to replace the bad measurement data in measurements. Second, a robust SE model combining network estimation and linear estimation is proposed, which enhances SE accuracy and robustness under bad data generated in measurements and topology parameters in ADNs. In a simulation, the proposed method’s effectiveness is verified on the modified IEEE 33-node system.