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Abstract
A novel despiking method is presented for in-stationary wind lidar velocity measurements. A finite difference approach yields the upper and lower bounds for a valid velocity reading. The sole input to the algorithm is the velocity series and optionally a far- field reference to the temporal variation in the velocity. The new algorithm is benchmarked against common despiking algorithms using a dataset acquired by three synchronised lidars in the upstream area of a full-scale wind turbine rotor and an artificially created space-time series with controlled spike contamination. By accounting for variations in space and time, this approach yields improvements in spike detection for in-stationary lidar measurements of about 25% over other more established stationary methods. Furthermore it proofs to be robust even for large numbers of spikes.
Original language | English |
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Article number | 072017 |
Book series | Journal of Physics: Conference Series (Online) |
Volume | 753 |
Number of pages | 9 |
ISSN | 1742-6596 |
DOIs | |
Publication status | Published - 2016 |
Event | The Science of Making Torque from Wind 2016 - Technische Universität München (TUM), Munich, Germany Duration: 5 Oct 2016 → 7 Oct 2016 Conference number: 6 https://www.events.tum.de/?sub=29 |
Conference
Conference | The Science of Making Torque from Wind 2016 |
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Number | 6 |
Location | Technische Universität München (TUM) |
Country/Territory | Germany |
City | Munich |
Period | 05/10/2016 → 07/10/2016 |
Internet address |
Bibliographical note
Content from this work may be used under the terms of theCreative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Published under licence by IOP Publishing LtdFingerprint
Dive into the research topics of 'A finite difference approach to despiking in-stationary velocity data - tested on a triple-lidar'. Together they form a unique fingerprint.Projects
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UniTTe: Unified testing procedures for wind turbines through inflow characterisation using nacelle lidars
Wagner, R. (Project Manager), Pedersen, T. F. (Project Participant), Troldborg, N. L. (Project Participant), Meyer Forsting, A. R. (Project Participant), Bechmann, A. (Project Participant), Courtney, M. S. (Project Participant), Borraccino, A. (Project Participant), Vignaroli, A. (Project Participant), Natarajan, A. (Project Participant), Sathe, A. (Project Participant) & Dimitrov, N. K. (Project Participant)
01/01/2014 → 31/12/2017
Project: Research