Tuesday, 27 September 2016
14:30 - 16:00 LIDARs - the zapping competition
Resource assessment  
          

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:
Co-chair(s):

Mohsen Zendehbad ETH Zurich, Switzerland
Co-authors:
mohsen zendehbad (1) ^F ndaona chokani (1) reza s. abhari (1)
(1) eth zurich, zurich, Switzerland

Wind farm scale measurements using a mobile scanning LIDAR

Introduction

Turbine-to-turbine interactions in wind farms result in power losses, and can result in increased damages of individual wind turbines. However, to-date wind flows in full-scale wind farms are poorly documented. In previous work [1], the authors demonstrated the novel approach of using a single mobile scanning LiDAR to make three-dimensional velocity field measurements up to four rotor diameters downstream of a single multi-megawatt wind turbine. In the present work, we extend this novel approach to demonstrate three-dimensional velocity field measurements on the scale of a whole wind farm. This measurement approach contributes to filling the gap of full-scale data that are required for the further development of simulation tools that are used to predict atmospheric flows and wakes in wind farms. Moreover, these measurements provide further insight for the development of advanced control systems to mitigate turbine-to-turbine interactions.

Approach

We have developed a measurement strategy using our mobile scanning LiDAR, whereby the flowfield in a whole wind farm is scanned within an hour. The measurement approach is comprised of four steps;
a) Preprocessing: the network of available roads in the wind farm, and the positions, hub heights and diameters of wind turbines are incorporated into a geographical information system database.
b) Initialization measurements: during the subsequent initialization stage, the range of the LiDAR and vertical profiles of wind speed and direction are measured. Subsequently, based on the primary wind direction, optimum positions to measure the wind flowfield within the wind farm are determined using an optimization algorithm.
c) Main measurements: an automated routine guides the driver mobile system to each measurement position, determines the appropriate scan patterns at each location, performs the measurements, and stops, when sufficient measurements are done.
d) Post-processing: the measurements of the line-of-sight components of wind from the measurement positions are used to calculate axial and lateral components of wind speed upstream and downstream of each wind turbine in the whole wind farm.


Main body of abstract

Measurements are made at a 25.8MW wind farm with 9 wind turbines. Flowfield in wind farm is measured from 11 optimum positions during a period of 66 minutes. The method of the guide to expression of uncertainty in measurement shows that the uncertainty in measured wind speed components is generally less than 5%. The measured wakes of all 9 wind turbines are compared with computational fluid dynamic simulations of a simulation tool that uses novel approaches to model the wakes.

Conclusion

The first measurements of the three-dimensional wind velocity field on the scale of a whole wind farm, which are obtained using a mobile scanning LiDAR, will be presented in the full paper. These measurements allow for an assessment of the evolution of wakes, and also provide details about the inflow conditions of downstream turbines. Thus, this work contributes to filling the gap of full-scale data that are required to support the development of simulation tools and advanced wind farm control systems.
[1] Zendehbad, M., Chokani, N., Abhari, R. S., 2016, “Volumetric three-dimensional wind measurement using a single mobile-based LiDAR,” Journal of Solar Energy Engineering, 138, 011003.



Learning objectives
• Use of mobile LiDAR for wind farm-scale flow measurements
• Understanding of full-scale wind flowfield within wind farms
• Comparisons of simulations with full-scale wind farm measurements