Robust Matrix Completion State Estimation in Distribution Systems
This work addresses state estimation challenges in low-observability distribution systems, offering a more robust solution for grid operators, but it is incremental as it builds on existing matrix completion approaches.
The paper tackles the problem of state estimation in distribution systems with insufficient measurements by proposing a robust matrix completion method that improves robustness to bad data without requiring full observability or a separate bad data detection process. The method was evaluated on the IEEE 33-node system, showing improved performance and robustness compared to existing methods like WLS-LNR and MCSE.
Due to the insufficient measurements in the distribution system state estimation (DSSE), full observability and redundant measurements are difficult to achieve without using the pseudo measurements. The matrix completion state estimation (MCSE) combines the matrix completion and power system model to estimate voltage by exploring the low-rank characteristics of the matrix. This paper proposes a robust matrix completion state estimation (RMCSE) to estimate the voltage in a distribution system under a low-observability condition. Tradition state estimation weighted least squares (WLS) method requires full observability to calculate the states and needs redundant measurements to proceed a bad data detection. The proposed method improves the robustness of the MCSE to bad data by minimizing the rank of the matrix and measurements residual with different weights. It can estimate the system state in a low-observability system and has robust estimates without the bad data detection process in the face of multiple bad data. The method is numerically evaluated on the IEEE 33-node radial distribution system. The estimation performance and robustness of RMCSE are compared with the WLS with the largest normalized residual bad data identification (WLS-LNR), and the MCSE.