Fleetwide Data-Enabled Reliability Improvement of Wind Turbines

Timothy Verstraeten, Ann Nowé, Jonathan Keller, Yi Guo, Shuangwen Sheng, Jan Helsen

Research output: Contribution to journalArticlepeer-review

24 Scopus Citations

Abstract

Wind farms are an indispensable driver toward renewable and nonpolluting energy resources. However, as ideal sites are limited, placement in remote and challenging locations results in higher logistics costs and lower average wind speeds. Therefore, it is critical to increase the reliability of the turbines to reduce maintenance costs. Robust implementation requires a thorough understanding of the loads subject to the turbine's control. Yet, such dynamically changing multidimensional loads are uncommon with other machinery, and generally underresearched. Therefore, a multitiered approach is proposed to investigate the load spectrum occurring in wind farms. Our approach relies on both fundamental research using controllable test rigs, as well as analyses of real-world loading conditions in high-frequency supervisory control and data acquisition data. A method is introduced to detect operational zones in wind farm data and link them with load distributions. Additionally, while focused research further investigates the load spectrum, a method is proposed that continuously optimizes the farm's control protocols without the need to fully understand the loads that occur. A case of gearbox failure is investigated based on a vast body of past experiments and suspect loads are identified. Starting from this evidence on the cause and effects of dynamic loads, the potential of our methods is shown by analyzing real-world farm loading conditions on a steady-state case of wake and developing a preventive row-based control protocol for a case of cascading emergency brakes induced by a storm.

Original languageAmerican English
Pages (from-to)428-437
Number of pages10
JournalRenewable and Sustainable Energy Reviews
Volume109
DOIs
StatePublished - 2019

Bibliographical note

Publisher Copyright:
© 2019 Elsevier Ltd

NREL Publication Number

  • NREL/JA-5000-73245

Keywords

  • Data-enabled load analysis
  • Failure avoidance
  • Wind turbine reliability

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