A Reduced-order Aggregated Model for Parallel Inverter Systems with Virtual Oscillator Control

M. M.S. Khan, Yashen Lin, Brian Johnson, Victor Purba, Mohit Sinha, Sairaj Dhople

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

This paper introduces a reduced-order aggregated model for parallel-connected inverters controlled with virtual oscillator control (VOC). The premise of VOC is to modulate inverter dynamics to emulate those of nonlinear oscillators with the goal of realizing a stable ac microgrid in the absence of communication, synchronous generation, or a stiff grid. To obtain a reduced-order model for a system of parallel-connected inverters with VOC, we first formulate a set of scaling laws that describe how the controller and filter parameters of a given inverter depend on its voltage and power rating. Subsequently, we show that N parallel inverters which adhere to this scaling law can be modeled with the same structure and hence the same computational burden of the model of a single inverter. The proposed aggregate model is experimentally validated on a system of three parallel inverters with heterogeneous power ratings.

Original languageEnglish (US)
Title of host publication2018 IEEE 19th Workshop on Control and Modeling for Power Electronics, COMPEL 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781538655412
DOIs
StatePublished - Sep 10 2018
Event19th IEEE Workshop on Control and Modeling for Power Electronics, COMPEL 2018 - Padova, Italy
Duration: Jun 25 2018Jun 28 2018

Publication series

Name2018 IEEE 19th Workshop on Control and Modeling for Power Electronics, COMPEL 2018

Other

Other19th IEEE Workshop on Control and Modeling for Power Electronics, COMPEL 2018
CountryItaly
CityPadova
Period6/25/186/28/18

Bibliographical note

Funding Information:
ACKNOWLEDGMENTS This work was supported in part by the: i) Alliance for Sustainable Energy, LLC, the Manager and Operator of the National Renewable Energy Laboratory for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided by U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Solar Energy Technologies Office; and ii) National Science Foundation through grants 1453921 and 1509277. The views expressed in the article do not necessarily represent the views of the DOE or the U.S. Government. The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paidup, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for U.S. Government purposes.

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