Wind turbine load analysis of a full range LPV controller
Bernabé Ibáñez,
F.A. Inthamoussou and
Hernán De Battista
Renewable Energy, 2020, vol. 145, issue C, 2741-2753
Abstract:
A wind turbine load analysis featuring both fatigue and extreme loads for a full range Linear Parameter Varying (LPV) controller is presented in this paper. The National Renewable Energy Laboratory (NREL) 5 MW reference wind turbine is simulated with realistic profiles of turbulent wind, according to the IEC 61400-1 regulation, using several NREL's tools, with a total of 354 numerical simulations. Classical techniques has been applied, like the Rain-flow counting (RFC) algorithm, and the Palmgren-Miner Rule with Goodman's correction to estimate the wind turbine components damage rate. The mechanical loads under consideration are the tower and blades main moments. The results show a lifetime equivalent load reduction with the LPV controller in comparison with the classical Gain Scheduling Proportional Integral (GSPI), for all analyzed loads, similar to those achieved by other publications in the literature. This improvement is achieved using the same online measurements and information as the used by the GSPI controller.
Keywords: Wind turbine; Fatigue analysis; Mechanical loads (search for similar items in EconPapers)
Date: 2020
References: Add references at CitEc
Citations: View citations in EconPapers (1)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960148119312030
Full text for ScienceDirect subscribers only
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:145:y:2020:i:c:p:2741-2753
DOI: 10.1016/j.renene.2019.08.016
Access Statistics for this article
Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides
More articles in Renewable Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().