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The Efficiency of the Kalman Filter in Nodal Redundancy

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  • Henrry Moyano

    (Faculty of Economic and Administrative Sciences, University of Cuenca, 12 Abril Ave., Cuenca 01017, Ecuador
    Electrical Engineering Department, Faculty of Physical and Mathematical Sciences, University of Chile, Santiago 8370458, Chile)

  • Luis Vargas

    (Electrical Engineering Department, Faculty of Physical and Mathematical Sciences, University of Chile, Santiago 8370458, Chile)

Abstract

The growing integration of distributed energy resources underscores the critical importance of having precise insights into the dynamics of an electrical power system (EPS). Consequently, an estimator must align with the EPS dynamics to enhance the overall reliability, safety, and system stability. This alignment ensures that operators can make informed decisions during system operations. An initial step in gaining insight into the system’s state involves examining its state vector, which is represented by voltage phasors. These results are derived through the application of a distributed state-estimation process in large-scale systems. This study delved into the effectiveness of Bayesian filters, with a particular emphasis on the extended Kalman filter (EKF) algorithm in the context of distributed state estimation. To analyze the outcomes, the nodal partitioning process was incorporated within the distributed state-estimation framework. The synergy between the EKF algorithm and the partitioning method was evaluated using the IEEE118 test system.

Suggested Citation

  • Henrry Moyano & Luis Vargas, 2024. "The Efficiency of the Kalman Filter in Nodal Redundancy," Energies, MDPI, vol. 17(9), pages 1-20, April.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:9:p:2131-:d:1386104
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