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Delegates are invited to meet and discuss with the poster presenters in this topic directly after the session 'Optimising measurement strategies to maximise project value: Is the industry making false economies at the expense of project value?' taking place on Tuesday, 11 March 2014 at 11:15-12:45. The meet-the-authors will take place in the poster area.

Frank Flottemesch Ecofys, The Netherlands
Co-authors:
Lidewij van den Brink (1) F P Frank Flottemesch (1) Erik Holtslag (1)
(1) Ecofys, Utrecht, The Netherlands

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Abstract

Shorter lidar campaigns without seasonal bias

Introduction

The maturity of LiDAR technology means that it can now replace a traditional tall mast for on-site wind measurement campaigns. To overcome issues of seasonal bias, it is common practice to measure on-site for a period of 12-months. However, this campaign design does not take full advantage of the ease of re-deployment of a LiDAR.

Ecofys has designed a wind measurement campaign that samples the wind climate in all four seasons: 6 months of on-site measurements are divided into four periods. Coupled with an improved Measure-Correlate-Predict (MCP) methodology, this optimised wind measurement strategy can significantly reduce seasonal bias.


Approach

Despite advances in MCP, seasonality continues to affect long-term wind climate estimates. The calculated long-term wind speed is lower when using summer months, and higher using winter months.

Ecofys has considered new measurement strategies and analysed the campaign designs in terms of the uncertainty in the long-term wind climate. Finally, an improved MCP methodology has been developed and demonstrated, as a further opportunity to reduce seasonal bias.


Main body of abstract

An Ecofys study of long-term wind climates calculated using MCP with 3 months of measurements found ±10% variability dependent on the measurement period. Since the reference time series are typically not at hub height, they exhibit different atmospheric stability behaviour and the MCP will not correct for seasonal variation.

Ecofys has designed a wind measurement campaign consisting of 6 months of on-site measurements divided into four periods to sample the wind climate in all seasons. This campaign design is based on the different cost structure of LiDAR compared to a met mast. While installation is one of the largest costs for a mast, for LiDAR it is equipment hire. Thus, it can be preferable to deploy a LiDAR in shorter campaigns to capture the seasonal effects without measuring at the site all year.

Applying this campaign strategy to the same datasets as in the earlier study, the resultant estimates of long-term wind climate differ by only ±1% relative to a 12-month campaign, which represents a major reduction in seasonal bias.

To extend the results to the long-term, Ecofys has also developed an improved MCP method that aims to more accurately account for seasonal stability differences. The long-term reference measurement time series (usually at a height lower than measurements) is first corrected to hub height using site-specific stability. A standard MCP procedure is then applied to measurements and long-term reference at the same height. This new MCP method reduces much of the seasonal bias, as evidenced by improved monthly and diurnal patterns.

Conclusion

The design of a LiDAR campaign which samples the wind climate in all four seasons will avoid significant seasonal bias, while simultaneously reducing campaign costs by about 35% (compared to a 1-year campaign). And it also more time efficient, as the LiDAR is available for 6 months, allowing either a concurrent campaign at a second site or a number of other uses.


Learning objectives
This presentation will demonstrate two innovations that address the issue of seasonal bias in short-term measurements. This example illustrates how beneficial it can be to re-think wind measurement strategies in light of the latest technological and industry developments.