Dynamic State Estimation for Distribution Networks Based on Adaptive Set Membership Filter under Unknown but Bounded Noise Environments

To overcome the difficulty in tracking the operation state of distribution networks when the specific distribution of system noise and measurement errors is unknown and the measurement is insufficient, a dynamic state estimation (DSE) method based on adaptive set membership filter (SMF) is proposed in this paper. First, for the sampling period and measurement delay differences of various measurements, a multi-source data fusion strategy was proposed to achieve the synchronization of measurement data at the sampling moment. Subsequently, considering unknown but bounded noise, an ellipsoid-based DSE model was established, which unified the form of multi-source data through measurement transformation strategies and linearized the measurement function. Then, an adaptive SMF considering bad data detection was proposed to solve the proposed DSE model. The state variables at different moments were iteratively solved through three steps: time update, bad data adaptive detection, and measurement update. Finally, the effectiveness and robustness of the proposed method were verified based on the IEEE33-bus distribution system, the 118-bus test system, and the 34-bus real test system.