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A Survey on Hybrid SCADA/WAMS State Estimation Methodologies in Electric Power Transmission Systems

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  • Orestis Darmis

    (School of Electrical and Computer Engineering, National Technical University of Athens (NTUA), 15780 Athens, Greece)

  • George Korres

    (School of Electrical and Computer Engineering, National Technical University of Athens (NTUA), 15780 Athens, Greece)

Abstract

State estimation (SE) is an essential tool of energy management systems (EMS), providing power system operators with an overall grasp of the actual power system operating conditions and aiding them in sustaining reliable and secure operation of the grid. In modern transmission sectors, two main measurement systems are deployed, namely the supervisory control and data acquisition (SCADA) and the wide area monitoring systems (WAMS). The multiple advantages of augmenting conventional SCADA-based SE algorithms with synchrophasor measurements from WAMS are already well-established; thus, an abundance of different methodologies has been reported in the field of hybrid SE (HSE). Under this premise, this paper provides a thorough literature review of novel HSE methods in transmission systems and proposes a classification based on the scope and mathematical modeling of each method. Following a brief introduction to the concept of SE based on WAMS and SCADA measurements, an insight into the main challenges emerging in HSE implementations is provided. Various HSE methods which overcome these challenges are reviewed, for both static and dynamic SE implementations. In conclusion, the research trends in the area of HSE are summarized, and the main findings of this literature review are discussed.

Suggested Citation

  • Orestis Darmis & George Korres, 2023. "A Survey on Hybrid SCADA/WAMS State Estimation Methodologies in Electric Power Transmission Systems," Energies, MDPI, vol. 16(2), pages 1-20, January.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:2:p:618-:d:1025243
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    References listed on IDEAS

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    1. Md Jakir Hossain & Mia Naeini, 2022. "Multi-Area Distributed State Estimation in Smart Grids Using Data-Driven Kalman Filters," Energies, MDPI, vol. 15(19), pages 1-17, September.
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