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Online Data-Driven Intelligent Control of Microgrids Using Koopman Operator Learning

Author

Listed:
  • Vladimir Toro

    (Department of Electronics Engineering, Universidad Santo Tomás, Cra. 9 No. 51-11, Bogotá 110311, Colombia)

  • Duvan Tellez-Castro

    (Systems Engineering, Universidad Distrital Francisco José de Caldas, Carrera 8 No. 40-62, Bogotá 110321, Colombia)

  • Eduardo Mojica-Nava

    (Department of Electrical and Electronics Engineering, Universidad Nacional de Colombia, Cra. 30 No. 45-03, Bogotá 111321, Colombia)

Abstract

This paper presents a voltage controller for an alternating current microgrid, where the nonlinear optimization problem of voltage regulation is transformed into a linear one by employing a linear predictor based on an online extended dynamic mode decomposition algorithm. This approach enables an online finite-dimensional representation of the Koopman operator. The voltage regulator operates online by updating the state matrix with past and current measurements. The system dynamics are updated in real time using the most recent data pair, with a regularization term included to prevent ill-posedness. Furthermore, this paper proposes an online data-driven control scheme for voltage regulation in a microgrid, which leverages model predictive control to handle transmission line faults and load variations, while ensuring conditions for convergence and stability. The main results are validated by simulation in a 14-node IEEE testbed microgrid.

Suggested Citation

  • Vladimir Toro & Duvan Tellez-Castro & Eduardo Mojica-Nava, 2025. "Online Data-Driven Intelligent Control of Microgrids Using Koopman Operator Learning," Sustainability, MDPI, vol. 17(24), pages 1-19, December.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:24:p:11114-:d:1815862
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