Distribution system state estimation: an overview of recent developments

Gang Wang, Georgios B. Giannakis, Jie Chen, Jian Sun

Research output: Contribution to journalReview articlepeer-review

22 Scopus citations


In the envisioned smart grid, high penetration of uncertain renewables, unpredictable participation of (industrial) customers, and purposeful manipulation of smart meter readings, all highlight the need for accurate, fast, and robust power system state estimation (PSSE). Nonetheless, most real-time data available in the current and upcoming transmission/distribution systems are nonlinear in power system states (i.e., nodal voltage phasors). Scalable approaches to dealing with PSSE tasks undergo a paradigm shift toward addressing the unique modeling and computational challenges associated with those nonlinear measurements. In this study, we provide a contemporary overview of PSSE and describe the current state of the art in the nonlinear weighted least-squares and least-absolutevalue PSSE. To benchmark the performance of unbiased estimators, the Cramér-Rao lower bound is developed. Accounting for cyber attacks, new corruption models are introduced, and robust PSSE approaches are outlined as well. Finally, distribution system state estimation is discussed along with its current challenges. Simulation tests corroborate the effectiveness of the developed algorithms as well as the practical merits of the theory.

Original languageEnglish (US)
Pages (from-to)4-17
Number of pages14
JournalFrontiers of Information Technology and Electronic Engineering
Issue number1
StatePublished - Jan 1 2019

Bibliographical note

Funding Information:
* Wang G and Giannakis GB were supported by the National Natural Science Foundation of China (NSFC) (Nos. 1514056, 1505970, and 1711471). Chen J and Sun J were supported by the NSFC (Nos. 61621063 and 61522303), the NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informa-tization (No. 61720106011), the Projects of Major International (Regional) Joint Research Program NSFC (No. 61720106011), and the Program for Changjiang Scholars and Innovative Research Team in University (No. IRT1208) ORCID: Gang WANG, http://orcid.org/0000-0002-7266-2412 ©c Zhejiang University and Springer-Verlag GmbH Germany, part of Springer Nature 2019


  • Bad data detection
  • Composite optimization
  • Cramér-Rao bound
  • Cyber attack
  • Feasible point pursuit
  • Proximal linear algorithm
  • Semidefinite relaxation
  • State estimation
  • TP311

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