Abstract
The global rise in offshore wind farms underscores the need to cut costs and optimise energy production. As turbines increase in size and wind farms become more concentrated, mitigating downstream wake effects is crucial for operational efficiency. LiDAR technology, offering advantages like eliminating the need for meteorology masts, has been extensively discussed in the literature. However, it indirectly measures wind parameters, relying on assumptions and embedded algorithms. Wind field reconstruction (WFR) methods empower users with more control over LiDAR measurements, allowing tailored flow assumptions and parameter estimation. Using LiDAR data from two sequential campaigns at a wind farm, our research analyses LiDAR performance validated with SCADA measurements and applies WFR for wind field parameters estimation. Comparative analyses of wind parameters from different sources, particularly downstream turbines, demonstrate the robustness of WFR. The reconstructed wind field is compared with SCADA data for a comprehensive assessment.
Original language | English |
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Title of host publication | Journal of Physics: Conference Series |
Publisher | IOP Publishing |
Pages | 012014 |
Number of pages | 10 |
Volume | 2875 |
Edition | 1 |
DOIs | |
Publication status | Published - 2024 |
Event | EERA DeepWind Conference 2024 - Norway, Trondheim Duration: 17 Jan 2024 → … https://www.deepwind.no/ |
Publication series
Name | Journal of Physics: Conference Series |
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ISSN (Print) | 1742-6588 |
Conference
Conference | EERA DeepWind Conference 2024 |
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City | Trondheim |
Period | 17/01/24 → … |
Internet address |
Bibliographical note
Funding Information:This research was supported by funding from the MaDurOS program from VLAIO (Flemish Agency for Innovation and Entrepreneurship) and SIM (Strategic Initiative Materials) through project SBO SEAFD and the intercluster SIM-Blue Cluster ICON Rainbow. The authors, moreover, acknowledge the support via the Flemish Government under the \u201COnderzoeksprogramma Artifici\u00EBle Intelligentie (AI) Vlaanderen\u201D program.
Publisher Copyright:
© 2024 Institute of Physics Publishing. All rights reserved.