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

By Kaveh Dehghanpour, Zhaoyu Wang, Jianhui Wang, Yuxuan Yuan, Fankun Bu. IEEE Transactions on Smart Grid, 2018

412 citations recorded by the source corpus.

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 data-driven 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.

Subfields: Control and Systems Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/479355b6b1236331305140eedb212e92ef67048f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. Mohammad Shahidehpour
2. Yuanzheng Li
3. Zhaoyang Dong
4. Xinhao Wei
5. Fahad Alismail

In Electrical and Electronic Engineering

1. Mohammad Shahidehpour
2. Zuyi Li
3. Rafal Weron
4. Antonio J. Conejo
5. Lei Wu
6. Amin Khodaei
7. Tao Li
8. Jianhui Wang

Citations in the corpus

• Detection of False Data Injection Attacks in Smart Grid: A Secure Federated Deep Learning Approach (2022, background): “To achieve this goal, a modern power system is developing into a type of CPSs that is deeply integrated by sensing, communication, computing, decision-making, and control [2].”
• Explainable Reinforcement Learning: A Survey (2020, neutral): “ment Learning (DRL), for the sake of simplicity, we will refer to both as just Reinforcement Learning going forward. Explainable Reinforcement Learning: A Survey 3 a CPS—e.g., Neural State Estimation [9]—to fully decentralized, but still rulegoverned systems—such as the Universal Smart Grid Agent [6”
• On the Secondary Control Architectures of AC Microgrids: An Overview (2020, neutral): “estimation approaches have been recommended [199]–[201].”