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Dynamic State Estimation of a Synchronous Machine Applying the Extended Kalman Filter Technique

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Advances in Emerging Trends and Technologies (ICAETT 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1067))

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Abstract

The purpose of this work is to implement a dynamic state estimator (DSE) of a synchronous machine by applying the extended Kalman filter (EKF). This work is focused on the synchronous generator because it constitutes a fundamental component of the Electric Power Systems. The dynamic behavior of the generator in an electric grid is simulated in Matlab using the One Machine-Infinite Bus System (OMIB). On the other hand, two standard dynamic models of the synchronous generator are used to develop the dynamic state estimator: the Two-Axis fourth order model and the sixth order GENROU model. The EKF estimation algorithm is applied to this two dynamic models in order to design the estimator block, and the filter’s parameters are tuned by simulating the ensemble “OMIB System-Estimator”. Finally, the performance of the developed state estimators is evaluated and compared.

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Author information

Authors and Affiliations

  1. Escuela Politécnica Nacional, Quito, Ecuador

    Jorge Ninazunta & Silvana Gamboa

Authors
  1. Jorge Ninazunta
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  2. Silvana Gamboa
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Corresponding author

Correspondence to Jorge Ninazunta .

Editor information

Editors and Affiliations

  1. Eindhoven University of Technology, Eindhoven, Noord-Brabant, The Netherlands

    Miguel Botto-Tobar

  2. Universidad Técnica de Babahoyo, Babahoyo, Ecuador

    Joffre León-Acurio

  3. Universitat de Valencia, Valencia, Valencia, Spain

    Angela Díaz Cadena

  4. Centro de Investigación y Desarrollo Profesional (CIDEPRO), Babahoyo, Ecuador

    Práxedes Montiel Díaz

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Ninazunta, J., Gamboa, S. (2020). Dynamic State Estimation of a Synchronous Machine Applying the Extended Kalman Filter Technique. In: Botto-Tobar, M., León-Acurio, J., Díaz Cadena, A., Montiel Díaz, P. (eds) Advances in Emerging Trends and Technologies. ICAETT 2019. Advances in Intelligent Systems and Computing, vol 1067. Springer, Cham. https://doi.org/10.1007/978-3-030-32033-1_17

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