Matrix Completion Using Alternating Minimization for Distribution System State Estimation

Yajing Liu, April Sagan, Andrey Bernstein, Rui Yang, Xinyang Zhou, Yingchen Zhang

Research output: Contribution to conferencePaperpeer-review

8 Scopus Citations


This paper examines the problem of state estimation in power distribution systems under low-observability conditions. The recently proposed constrained matrix completion method which combines the standard matrix completion method and power flow constraints has been shown to be effective in estimating voltage phasors under low-observability conditions using single-snapshot information. However, the method requires solving a semidefinite programming (SDP) problem, which becomes computationally infeasible for large systems and if multiple-snapshot (time-series) information is used. This paper proposes an efficient algorithm to solve the constrained matrix completion problem with time-series data. This algorithm is based on reformulating the matrix completion problem as a bilinear (non-convex) optimization problem, and applying the alternating minimization algorithm to solve this problem. This paper proves the summable convergence of the proposed algorithm, and demonstrates its efficacy and scalability via IEEE 123-bus system and a real utility feeder system. This paper also explores the value of adding more data from the history in terms of computation time and estimation accuracy.


Conference2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2020
Country/TerritoryUnited States

Bibliographical note

See NREL/CP-5D00-74961 for preprint

NREL Publication Number

  • NREL/CP-5D00-79140


  • alternating minimization
  • distribution system state estimation
  • matrix completion


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