Renewable Energy

Exploring Progress in Wind Resource Assessment: Meteodyn's Technological Advancements at WindEurope Technology Workshop 2023

Exploring Progress in Wind Resource Assessment: Meteodyn's Technological Advancements at WindEurope Technology Workshop 2023

Join us at the WindEurope Technology Workshop, taking place on June 1-2 at the Congress Center in Lyon, France.

Meteodyn, a leading player in the industry, will actively participate in this exciting event. Zixiao Jiang, our Technical Director from Meteodyn China will be speaking at the session on "Smart Methods for Resource Assessment", specifically presenting on ways to reduce uncertainty in Wind Farm Repowering.

zixiao-jiang-meteodyn-china minh-thang-do-meteodyn Antoine-Rozel

Zixiao Jiang

Technical Director,
Meteodyn China

Minh-Thang Do

Head of Energy Division,

Antoine Rozel

Research Engineer and
Climate Change Expert, Meteodyn

Minh-Thang Do, Head of Energy Division and Antoine Rozel, Research Engineer and Climate Change Expert at Meteodyn, will also attend the event as visitors.
Additionally, we will showcase a poster highlighting our research on the behavior of Lidar in complex terrains titled "Optimizing Lidar Placement through Analysis of Measurement Errors in Complex Terrain."

Advancing Wind Farm Repowering: Zixiao Jiang to Share Innovative Resource Assessment Methodologies

Zixiao Jiang, Technical Director of Meteodyn China, will be a speaker in the "Smart methods for resource assessment" session.

When: Thursday, June 1, 2023, from 16:30 to 18:00.
Where: Auditorium Lumière of the Congress Center in Lyon, France.

Discover below the abstract of his presentation titled "Wind resource assessment methodology for repowering wind farms: reducing uncertainty by model verification and calibration".


Wind resource assessment (WRA) for repowering wind farm should provide better accuracy than the pre-construction assessment, due to the presence of operating data. However, it is not obvious to incorporate SCADA data properly into a wind flow modelling process. The lack of an explicit technical guidance could make it difficult for the industry to carry out WRA for repowering project in an appropriate and standard way.

In response to these challenges, a specific WRA methodology for repowering wind farm has been proposed. The description focuses on two main aspects differing from traditional WRA:

Adjustment of nacelle wind speed of the existing wind turbines

Nacelle wind speed measured at each wind turbine locations provides a good spatial representation of the wind flow characteristic. It is often considered as a useful source to verify/calibrate wind flow modelling but is not suitable for direct use as input of wind flow model due to the disturbance from turbine blades. Moreover, the determination of site-specific nacelle transfer function is not always possible.

An alternative adjusting method is used instead. It consists of running a two-step adjustment:

  • The first adjustment is done with the help of the average power curve evaluated from all the turbines of the same classification in the wind farm, converting the original nacelle wind speed to “theoretical” wind speed.
  • The second adjustment is based on correlation with met mast measurement.

Calibration of wind flow modelling using multi-source data

The agreement between the model-predicted wind speed and the adjusted nacelle wind speed at each turbine is checked, and a tuning of wind flow modelling could be necessary. In addition to microscale model calibration, mesoscale simulation provides potential for improving the modelling accuracy through a proper mesoscale-microscale model-chain.

Several levels of modelling methods can be applied, requiring different modelling techniques and calibration process:

  • Microscale model only
  • Refined microscale model (finer resolution, larger domain, calibrated roughness length and atmospheric stability)
  • Combined use of mesoscale and microscale model
  • Combined use of calibrated mesoscale and microscale model

The calibration on mesoscale data is based on the comparison of predicted and measured (adjusted) wind speed at turbines.


The methodology was applied on a specific wind farm with 33 1.5 MW turbines. The mean wind speed estimation at turbine locations shows an RMS error of only 1%.

The methodology was presented in the "Guidance Document on Wind Resource Assessment Method of In-service Wind Farms" (REETC/TN010:2022) released by Renewable Energy Experts Technical Committee of China General Certification in October 2022, following a joint work by several organizations with different profiles in wind power industry.


An appropriate adjustment of nacelle wind speed provides reference data for flow modelling verification, allowing to check the model performance at each turbine location. Combined use of SCADA data and mesoscale data by relevant calibration process, allows to reduce significantly modelling uncertainty. It is considered as a promising way to reduce modelling uncertainty of the WRA for repowering wind farms.

A new poster released for the event, enhancing lidar performance and accuracy in complex terrain

We invite you to join us and explore our latest poster presentation (PO075) by Zixiao Jiang titled "Analysis of spatial distribution characteristic of lidar measurement errors in complex terrain to optimize lidar placement" during the WindEurope Technology Workshop.

This poster showcases a comprehensive methodology that addresses the challenges of measuring wind resources in complex terrains using ground-based lidar devices, through the utilization of microscale Computational Fluid Dynamics (CFD) modeling. Discover the abstract below.


Ground based lidar has been considered as a promising wind measurement technology in wind resource assessment and wind turbine power performance test. Thanks to doppler effect, lidar devices reconstruct wind flow filed by assuming that the flow is homogenous. However, this assumption is challenged in complex terrain due to inhomogeneity of the wind flow and especially flow curvature. In consequence, the measurement uncertainty is increased in complex terrain, and it has become one of the main constraints for using lidar more widely.

With the topographical data of a specific site, microscale CFD modeling is used to reproduce the inhomogeneity of the wind flow and use it to correct lidar measurement. The correction considers site-specific conditions and the specific wind flow reconstruction method used by the lidar device which could differ from one provider to another.

Then the correction technique for a given location is implemented to do batch processing on all the locations in an efficient way, for varying incident wind directions and at different heights. Thus, the spatial distribution of lidar error can be determined precisely.


On one hand, the study improves the understanding of lidar measurement error in complex terrain. On the other hand, the method can be generalized and used in any site with complex topography, to predict the measurement error at potential locations prior to the installation of lidar, and thus helps to optimize lidar placement from the perspective of quantifying and reducing uncertainty.


The spatial distribution of lidar measurement error varies with incident wind direction and height. Following characteristics have been identified:

  • Lidar trends to underestimate wind speed on the ridge. The most severe underestimation occurred when the wind direction is perpendicular to the ridge.
  • The spatial discrepancy of error is the most notable when the wind direction is perpendicular to the ridge, and less notable when the wind direction is parallel to the ridge.
  • The variation of lidar error at the heights 100m/150m/200m shows decreasing discrepancy as well as decreasing extreme value of error with increasing height. However, a finer study shows that the extreme value of error may occur at a particular height. This phenomenon is related to the geometry of lidar scanning cone, combined with the inhomogeneity of wind flow at different height (which is furtherly impacted by the topographical condition around the specific location). Thus, an accurate flow model is fundamental to have a good assessment of the error.
  • The main influencing factor on lidar measurement error is the flow curvature, causing difference of inflow angle between upstream and downstream measurement points.


Lidar measurement error has been evaluated from wind flow parameters obtained with CFD-based numerical simulation. By using this technique, it has become possible to carry out a full analysis on the spatial distribution characteristic of lidar error over a specific site. It also provides the possibility to run an optimization of lidar placement in a specific site.



Don't miss this opportunity to explore cutting-edge advancements and engage in insightful discussions. We eagerly await your presence at the WindEurope Technology Workshop!

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