Blackbox Large-Signal Modeling of Grid-Connected DC-AC Electronic Power Converters

Galo Guarderas, Airan Frances, Dionisio Ramirez, Rafael Asensi and Javier Uceda
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Galo Guarderas: Centro de Electrónica Industrial, Universidad Politécnica de Madrid, 28006 Madrid, Spain
Airan Frances: Centro de Electrónica Industrial, Universidad Politécnica de Madrid, 28006 Madrid, Spain
Dionisio Ramirez: Centro de Electrónica Industrial, Universidad Politécnica de Madrid, 28006 Madrid, Spain
Rafael Asensi: Centro de Electrónica Industrial, Universidad Politécnica de Madrid, 28006 Madrid, Spain
Javier Uceda: Centro de Electrónica Industrial, Universidad Politécnica de Madrid, 28006 Madrid, Spain

Energies, 2019, vol. 12, issue 6, 1-22

Abstract: Modern electric power distribution systems are progressively integrating electronic power converters. However, the design of electronic-power-converter-based systems is not a straightforward task, as the interactions among the different converters can lead to dynamic degradation or instabilities. In addition, electric power distribution systems are expected to consist of commercial-off-the-shelf converters, which implies limited information about the dynamic behavior of the devices. Large-signal blackbox modeling approaches have been proposed in order to obtain accurate dynamic models of commercial converters that can be used for system-level analyses. However, most of the works are focused on DC-DC converters. In this work, a large-signal blackbox model is proposed to model grid-connected three-phase DC-AC converters. An experimental setup has been used to demonstrate the limitations of small-signal models and the capability of the proposed modeling approach to capture the dynamic behavior of the converter when large perturbations are applied. Finally, the automation of the model identification process is discussed.

Keywords: DC-AC power converters; nonlinear dynamical systems; system identification (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: 2019
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