Enhancing performance of state estimation algorithms with FACTS

The integration of Flexible Alternating Current Transmission Systems (FACTS) devices in power grids can enhance operational capabilities. However, such components must be properly incorporated in real-time power system analysis tools, such as State Estimator (SE), for fulfilling their purpose. Despi...

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Published inElectric power systems research Vol. 246; p. 111641
Main Authors de Melo, Vitor H.P., Hebling, Gustavo M., Massignan, Julio A.D., Jr, João B.A. London
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.09.2025
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ISSN0378-7796
DOI10.1016/j.epsr.2025.111641

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Abstract The integration of Flexible Alternating Current Transmission Systems (FACTS) devices in power grids can enhance operational capabilities. However, such components must be properly incorporated in real-time power system analysis tools, such as State Estimator (SE), for fulfilling their purpose. Despite some good theoretical results, most solutions to including FACTS in SEs make structural changes in the original problem, which may incur lower convergence rate, starting point reliance, divergence, and harmed Gross Errors (GE) processing capabilities. This paper addresses such limitations and provides two solutions to SE in presence of FACTS. The first adds information on those devices’ state variables for enhancing SE’s convergence and accuracy while allowing GE processing, whereas the second proposes Levenberg–Marquardt (LM) method for avoiding divergence in case those variables are not monitored. The paper also provides simulation results on IEEE 14 and 118 bus test systems, showing the feasibility of the solutions. •Including FACTS devices in State Estimation may hinder the application’s reliability.•Convergence Instabilities introduced in the optimization problems by the FACTS variables.•Gross Errors processing is harmed when FACTS are included using only traditional measurements.•Information about the FACTS control parameters must be integrated to support state estimation.•Use of the Levenberg–Marquardt method in state estimation to ensure convergence reliability.
AbstractList The integration of Flexible Alternating Current Transmission Systems (FACTS) devices in power grids can enhance operational capabilities. However, such components must be properly incorporated in real-time power system analysis tools, such as State Estimator (SE), for fulfilling their purpose. Despite some good theoretical results, most solutions to including FACTS in SEs make structural changes in the original problem, which may incur lower convergence rate, starting point reliance, divergence, and harmed Gross Errors (GE) processing capabilities. This paper addresses such limitations and provides two solutions to SE in presence of FACTS. The first adds information on those devices’ state variables for enhancing SE’s convergence and accuracy while allowing GE processing, whereas the second proposes Levenberg–Marquardt (LM) method for avoiding divergence in case those variables are not monitored. The paper also provides simulation results on IEEE 14 and 118 bus test systems, showing the feasibility of the solutions. •Including FACTS devices in State Estimation may hinder the application’s reliability.•Convergence Instabilities introduced in the optimization problems by the FACTS variables.•Gross Errors processing is harmed when FACTS are included using only traditional measurements.•Information about the FACTS control parameters must be integrated to support state estimation.•Use of the Levenberg–Marquardt method in state estimation to ensure convergence reliability.
ArticleNumber 111641
Author de Melo, Vitor H.P.
Jr, João B.A. London
Massignan, Julio A.D.
Hebling, Gustavo M.
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Keywords Convergence robustness
Gross Errors
State estimation
Levenberg–Marquardt
FACTS
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Snippet The integration of Flexible Alternating Current Transmission Systems (FACTS) devices in power grids can enhance operational capabilities. However, such...
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StartPage 111641
SubjectTerms Convergence robustness
FACTS
Gross Errors
Levenberg–Marquardt
State estimation
Title Enhancing performance of state estimation algorithms with FACTS
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