Detecting wind turbine wakes with nacelle lidars

D P Held,J Mann,A Larvol

doi:10.1088/1742-6596/854/1/012020

D P Held, J Mann + Show 1 more

Open Access

https://doi.org/10.1088/1742-6596/854/1/012020

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Abstract

Because the horizontal homogeneity assumption is violated in wakes flows, lidars face difficulties when reconstructing wind fields. Further, small-scale turbulence which is prevalent in wake flows causes Doppler spectrum widths to be broader than in the free stream. In this study the Doppler peak variance is used as a detection parameter for wakes. A one month long measurement campaign, where a continuous-wave lidar on a turbine has been exposed to multiple wake situations, is used to test the detection capabilities. The results show that it is possible to identify situation where a downstream turbine is in wake by comparing the peak widths. The used lidar is inexpensive and brings instalments on every turbine within economical reach. Thus, the information gathered by the lidars can be used for improved control at wind farm level.

Highlights

Within a wind farm wakes from neighbouring turbines cannot be avoided; optimal wind farm design can minimize these effects but never completely avoid them
Much effort has been put into wake models as wind farm design tools and their validation, while significant interest in measuring wake with the use of remote sensing devices originates from optimal wind farm operation using wake redirection
The top panel shows the difference in peak variance between beam 1 and 2

Summary

Introduction

Within a wind farm wakes from neighbouring turbines cannot be avoided; optimal wind farm design can minimize these effects but never completely avoid them. Much effort has been put into wake models as wind farm design tools (for an overview see [1]) and their validation, while significant interest in measuring wake with the use of remote sensing devices originates from optimal wind farm operation using wake redirection. Information of the detection can be applied as input to wind farm based yaw steering. A very straight-forward approach to detect wakes was introduced by [2] of merely using wind farm SCADA data. Based on turbine data a wake pattern for each turbine was measured and used to decide in real time whether a power reduction was due to a wake or an actual fault

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Results

Conclusion