Time-Varying Robustness Margins for Wind Turbines

Sanjana Vijayshankar, Peter J Seiler Jr

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

1 Scopus citations

Abstract

This paper focuses on robustness analysis for wind turbine control systems. The dynamics of a wind turbine are nonlinear and time-varying due to several effects including blade rotation, wind shear, tower shadowing, and varying wind conditions. Thus classical gain/phase/delay margins, computed using frequency domain concepts, are insufficient for turbine control systems. The robustness analysis in this paper is instead performed in two steps. First, the turbine dynamics are linearized at either constant wind conditions or along a fixed (hub height) wind speed trajectory. This yields a linear time-varying (LTV) model for the turbine dynamics. Next, disk margins are computed using existing results for finite-horizon LTV systems. These disk margins account for uncertainty at the blade pitch and/or generator torque inputs. This method is applied to assess the margins for a 2.5 MW Clipper Liberty turbine operated by the University of Minnesota. The turbine model and source control law used in the analysis was provided by Clipper. These results provide additional insight into the robustness of the existing turbine control law.

Original languageEnglish (US)
Title of host publication2018 Annual American Control Conference, ACC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1503-1508
Number of pages6
ISBN (Print)9781538654286
DOIs
StatePublished - Aug 9 2018
Event2018 Annual American Control Conference, ACC 2018 - Milwauke, United States
Duration: Jun 27 2018Jun 29 2018

Publication series

NameProceedings of the American Control Conference
Volume2018-June
ISSN (Print)0743-1619

Other

Other2018 Annual American Control Conference, ACC 2018
Country/TerritoryUnited States
CityMilwauke
Period6/27/186/29/18

Bibliographical note

Funding Information:
ACKNOWLEDGMENT This work was supported by the National Science Foundation under Grant No. NSF-CMMI-1254129 entitled CAREER: Probabilistic Tools for High Reliability Monitoring and Control of Wind Farms.

Publisher Copyright:
© 2018 AACC.

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