14:30 - 16:00 LIDARs - the zapping competition
Resource assessment
![Onshore Onshore](onshore-neg.png)
![Offshore Offshore](offshore-neg.png)
Room: Hall G2
In this highly interactive quick-fire session, participants will scan through 14 LIDAR-related presentations and vote to select the three contributions they would like to hear in full. Presentations will cover a wide range of possible LIDAR applications, both offshore and onshore, such as power-curve validation, resource assessment in complex terrain, turbulence intensity measurements, and more.
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Learning objectives
Get a wide overview of the latest research and field work involving LIDARs, both onshore and offshore.
This session will be chaired by:
![](../submit-an-abstract/thumb/ADMIN_15Courtney,_Mike_2015.jpg)
![](../submit-an-abstract/thumb/ADMIN_507Landberg_Lars.jpg)
![](../submit-an-abstract/thumb/ADMIN_506Ivanell_Stephan.jpg)
Presenter
![](../submit-an-abstract/thumb/320_1810184032.jpeg)
Co-authors:
Matthieu Boquet (1) F Raghu Krishnamurthy (1) Paul Mazoyer (1)
(1) LEOSPHERE, Paris, France
Presenter's biography
Biographies are supplied directly by presenters at WindEurope Summit 2016 and are published here uneditedMatthieu Boquet is graduated in Energy and Information Science in France and received a MSc. in Information and Signal Processing in Spain. Since he joined Leosphere in 2008, he has worked on the application of Lidars in the wind energy industry, in particular ground-based vertical profiler, Long-Range scanning Lidar and wind turbine nacelle-mounted Lidar. At Leosphere he is in charge of leading the scientific Research & Development of new functionalities and bringing the scientific support for customer specific projects.
Abstract
Turbulence intensity measurement techniques for pulsed LIDARs – the current status
Introduction
Turbulence Intensity (TI) measurements is the current challenge for increasing the acceptance of Lidars in site suitability and wind turbine loads assessment applications. The current status of the Lidars capacity to retrieve this wind information is subject to a series of questions: what is the effect of volume averaging? How far/close are the measurements from cup and sonic anemometers? In which environmental and site conditions the Lidars TI may be acceptable and in which it is not? How should the TI information be treated and evaluated?
Approach
Several approaches have been attempted so far to improve TI measurements, as recommended in an IEA expert report on Lidar TI measurements. These techniques may be site specific corrections (Sathe et al), generic raw data filtering (Guillemin et al), or the use of neural networks (Newman et al). In this paper we propose to review and evaluate pros and cons of those approaches for the pulsed Lidar Windcube.
Main body of abstract
Turbulence Intensity (TI) is an important wind data for the development of wind farms and wind turbines, in particular to assess the suitability of a wind turbine and choose its performance class for a specific site, and to calculate the equivalent loads. In general TI measurements from pulsed Lidars may be considered as sufficient in simple flat terrains for site suitability purposes, as shown in various studies. Still the exact status and acceptance remains a subject under discussion. While in complex terrains and forested areas the measurements are also questioned, and acceptance is often conditioned to the presence of a met mast.
Site specific correction using Reynolds Tensor derived from wind and Lidar modelling has been introduced by Sathe at a precedent EWEA conference. The wind model is based on the Mann model, and has been evaluated for homogeneous terrain conditions, ie. simple flat sites. The value of the method resides in taking into account the atmospheric stability to evaluate and correct Lidar TI, and therefore requires a measurement of the atmospheric stability.
Filtering techniques on the Lidar radial wind speed presented by Guillemin in 2015 brings a reduction of the measurement noise, which contributes to debias the Lidar TI data to some extent. The filtering has therefore improved the TI measurements, but the effect of complex topography and varying roughness around the Lidar is not fully taken into account.
Newer statistical techniques like learning algorithm based on neural networks have been recently developed by Newman. Those techniques are looking promising, however the understanding of their behavior and how repeatable they are remains a challenge.
Conclusion
We could evaluate most of these techniques on a variety of Windcube data sets and will propose to draw the pros and cons of them against defined success criteria.
Finally, we would like to encourage the further development of pulsed Lidar data processing techniques and will ask if a combination of methods could improve and increase the industry confidence in Lidar TI, such as ensemble forecast techniques have reduced the overall uncertainty of weather forecasting models?
Learning objectives
• The audience will get an overview of current status of pulsed Lidar TI measurements
• Several data processing techniques are evaluated against success criteria on Windcube data sets
• Pros and cons of these techniques will be given, following IEA Wind (task 32) expert report on TI measurements
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