Dynamic State Estimation with Multi-level Data Recognition Considering New Energy Sources

The integration of new energy sources in modern power systems has resulted in increased volatility and challenges associated with measurement data. This paper proposes a novel dynamic state estimation method for power system analysis that incorporates multi-level data recognition considering new energy sources. The proposed approach is based on the robust Cubature Kalman Filter algorithm, which leverages the innovation vector to achieve Level 1 data recognition. Additionally, the proposed approach utilizes classification and recognition based on measurement data characteristics to further refine data recognition. The proposed method applies dynamically adjusting the noise scale factor based on recognition results to correct adversarial data and improve the accuracy of the estimation. Numerical simulation analysis carried out on the improved IEEE-57 node system demonstrated the feasibility and effectiveness of the proposed methodology.