Individual Pitch Control of A Clipper Wind Turbine for Blade In-plane Load Reduction

Shu Wang, Peter Seiler, Zongxuan Sun

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

Abstract

This paper proposes an H∞ individual pitch controller for the Clipper C96 2.5 MW wind turbine installed at University of Minnesota. Different with existing results aimed to mitigate the blade out-of-plane loads, the proposed controller focuses more on reducing the blade in-plane loads. This is because the in-plane loads are dominating parts on the blade roots and therefore more critical to the life time of blades. To better balance the load reduction performance of different components on the turbine, the proposed controller takes the sum of filtered measurements of both blade in-plane and out-of-plane moments as feedback signals. It is expected to mitigate part of the in-plane periodic loads. Meanwhile, the high pass filtered measurements of the out-of-plane moments allow the controller to still mitigate high order harmonic terms of the out-of-plane loads. Therefore, the load reduction performance on the rotor shaft and nacelle should be improved. Simulation results show that the proposed design achieved these objectives.

Original languageEnglish (US)
Title of host publication2018 Annual American Control Conference, ACC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1491-1496
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:
VII. ACKNOWLEDGMENTS This work was supported by Xcel Energy Renewable Energy Fund: No. RD4-13 Virtual Wind Simulator with Advanced Control & Aeroelastic Model for Improving the Operation of Wind Farms and National Science Foundation Grant No. NSF-CMMI-1254129 CAREER: Probabilistic Tools for High Reliability Monitoring and Control of Wind Farms.

Publisher Copyright:
© 2018 AACC.

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