Robust Matrix Completion State Estimation in Distribution Systems: Preprint

Rui Yang, Yingchen Zhang, Andrey Bernstein, Bo Liu, Hongyu Wu

Research output: Contribution to conferencePaper


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.
Original languageAmerican English
Number of pages8
StatePublished - 2019
Event2019 IEEE Power and Energy Society General Meeting (IEEE PES GM) - Atlanta, Georgia
Duration: 4 Aug 20198 Aug 2019


Conference2019 IEEE Power and Energy Society General Meeting (IEEE PES GM)
CityAtlanta, Georgia

Bibliographical note

See NREL/CP-5D00-76226 for paper as published in IEEE proceedings

NREL Publication Number

  • NREL/CP-5D00-74530


  • distribution system state estimation
  • low observability
  • matrix completion
  • robustness


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