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Cited by 485

The DOI matches its Crossref record; 59 of the 59 references it cites were found in a registry or an index.

A Survey on State Estimation Techniques and Challenges in Smart Distribution Systems

Open access
Dehghanpour, Kaveh, Wang, Zhaoyu, Wang, Jianhui, Yuan, Yuxuan, Bu, Fankun·2019·IEEE Transactions on Smart Grid

Abstract

This paper presents a review of the literature on state estimation (SE) in power systems. While covering works related to SE in transmission systems, the main focus of this paper is distribution system SE (DSSE). The critical topics of DSSE, including mathematical problem formulation, application of pseudo-measurements, metering instrument placement, network topology issues, impacts of renewable penetration, and cyber-security are discussed. Both conventional and modern datadriven and probabilistic techniques have been reviewed. This paper can provide researchers and utility engineers with insights into the technical achievements, barriers, and future research directions of DSSE.

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Registry

Crossref · 10.1109/tsg.2018.2870600

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Metadata

Authors
Dehghanpour, K., Wang, Z., Wang, J., Yuan, Y., & Bu, F.
Venue
IEEE Transactions on Smart Grid
Published
2019-03
Volume
10
Issue
2
Pages
2312-2322
ISSN
  • 1949-3053 (ISSN-L)
  • 1949-3061
OpenAlex
W2890369708
Cited by
485
References
59
FWCI
15.90
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: A Survey on State Estimation Techniques and Challenges in Smart Distribution Systems. https://app.cochat.ai/papers/10.1109/tsg.2018.2870600
The paper
Dehghanpour, K., Wang, Z., Wang, J., Yuan, Y., & Bu, F. (2019). A Survey on State Estimation Techniques and Challenges in Smart Distribution Systems. IEEE Transactions on Smart Grid. https://doi.org/10.1109/tsg.2018.2870600

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.