Linear Quadratic Gaussian Integral Control for Secondary Voltage Regulation

Elio Chiodo, Pasquale Di Palma, Maurizio Fantauzzi, Davide Lauria, Fabio Mottola () and Domenico Villacci
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Elio Chiodo: Department of Industrial Engineering, University of Naples Federico II, 80125 Naples, Italy
Pasquale Di Palma: Department of Industrial Engineering, University of Naples Federico II, 80125 Naples, Italy
Maurizio Fantauzzi: Department of Industrial Engineering, University of Naples Federico II, 80125 Naples, Italy
Davide Lauria: Department of Industrial Engineering, University of Naples Federico II, 80125 Naples, Italy
Fabio Mottola: Department of Electrical Engineering and Information Technology, University of Naples Federico II, 80125 Naples, Italy
Domenico Villacci: Department of Industrial Engineering, University of Naples Federico II, 80125 Naples, Italy

Energies, 2024, vol. 18, issue 1, 1-18

Abstract: In this paper, the voltage regulation in power systems is addressed from the perspective of the modern paradigm of control logic supported by phasor measurement units. The information available from measurements is used to better adapt the regulation actions to the actual operation point of the system. The use of the online measurement data allows for identifying the sensitivity matrix and for improving the regulation performances with respect to the fast load variations that increasingly affect modern power systems. With the aim of estimating the sensitivity matrices, a preliminary action is necessary to reconstruct the phases of the network voltages, which are assumed not to be provided by the phasor measurement units. This allows for obtaining a model-free adaptive control method. It is then shown how the regulation problem can be formulated in terms of a linear quadratic Gaussian problem, properly considering the load modeling in terms of the stochastic Ornstein–Uhlenbeck process. This control strategy has the advantage of avoiding dangerous oscillations of power flows, as demonstrated through the results of some simulations on a classical test network. Particularly, the advantage of the proposed approach is shown in the presence of different levels of load disturbances.

Keywords: secondary voltage regulator; linear quadratic Gaussian integral regulator; Ornstein–Uhlenbeck process; PMU (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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