# Can LiDARs Replace Meteorological Masts in Wind Energy?

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## Abstract

**:**

## 1. Introduction

## 2. Test Site and Measurement Setup

## 3. Comparison of LiDAR Measurement with Sonic and Cup Anemometer

#### 3.1. Mean Wind Speed and Turbulence Intensity

#### 3.2. Peak Wind Speed

#### 3.3. Effect of Terrain

## 4. Wind Resource, Power and Load Comparison

## 5. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## Abbreviations

ABL | Atmospheric Boundary Layer |

CFD | Computational Fluid Dynamics |

CNR | Carrier-to-Noise Ratio |

CW | Continuous Wave |

DBS | Doppler Beam Swinging |

DEL | Damage Equivalent Loads |

DLC | Design Load Case |

IEC | International Electrotechnical Commission |

NTM | Normal Turbulence Model |

Probability Density Function | |

RIX | Ruggedness Index |

RMSE | Root-Mean-Square Error |

VAD | Velocity Azimuth Display |

## References

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**Figure 2.**(

**a**) Terrain complexity of the region around the test site; (

**b**) Ruggedness index of the site.

**Figure 4.**Data availability for the period of one year from April 2018 through March 2019. (

**a**) Cup anemometer; (

**b**) Sonic anemometer; (

**c**) V2 profiling LiDAR.

**Figure 5.**Comparison of 10-min average horizontal wind speeds at 40 m height. (

**a**) Sonic anemometer and cup anemometer; (

**b**) Sonic anemometer and V2 profiling LiDAR.

**Figure 6.**Comparison of standard deviation at 40 m height. (

**a**) Sonic anemometer and cup anemometer; (

**b**) Sonic anemometer and V2 profiling LiDAR.

**Figure 7.**Turbulence intensity as a function of mean wind speed at 40 m height. (

**a**) Sonic anemometer; (

**b**) Cup anemometer; (

**c**) V2 profiling LiDAR; (

**d**) Bin-averaged turbulence intensities; (

**e**) Number of 10-min average data per bin. Lines in (

**a**–

**c**) represent the 90th percentile of the standard deviation for the wind speed bin. Bin size is set to 1 m/s of mean wind speed.

**Figure 8.**Comparison of the original sonic anemometer data collected at a sampling frequency of 10 Hz and the sonic anemometer data re-sampled at 1 Hz sampling frequency. The measurement height is 40 m. (

**a**) 10-min average horizontal wind speeds and (

**b**) standard deviation.

**Figure 9.**Comparison of measurements at 57 m height. (

**a**) 10-min average horizontal wind speed; (

**b**) Standard deviation.

**Figure 10.**Turbulence intensity as a function of mean wind speed at 57 m height. (

**a**) Sonic anemometer; (

**b**) V2 profiling LiDAR; (

**c**) bin-averaged turbulence intensities; (

**d**) turbulence intensity for the 90th percentile of the standard deviation for the wind speed bin; (

**e**) number of 10-min average data per bin. Lines in (

**a**,

**b**) represent the 90th percentile of the standard deviation for the wind speed bin. Bin size is set to 1 m/s of mean wind speed.

**Figure 11.**Time series of maximum wind speed in every 10-min time slot at 57 m height. The two zoom-in figures show the maximum wind speeds for a 24 h period; (

**top left**) is for 2018-10-01 and (

**top right**) is for 2019-02-01.

**Figure 12.**Probability density function (PDF) of the maximum wind speed in every 10-min time slot at 57 m height. Bin size for PDF is 1 m/s of peak wind sped.

**Figure 13.**Gust factor ($G=\widehat{V}/\overline{V}$) as a function of mean wind speed at 57 m height. (

**a**) Sonic anemometer; (

**b**) V2 profiling LiDAR. The lines indicate the 95th percentile of the measured data.

**Figure 14.**Wind rose showing wind speed and wind direction as measured at 57 m height. (

**a**) Sonic anemometer; (

**b**) V2 LiDAR.

**Figure 15.**Comparison of measurements at 57 m height. (

**a**) 10-min average horizontal wind speed for wind direction between ${60}^{\circ}$ and ${180}^{\circ}$ (non-complex terrain); (

**b**) 10-min average horizontal wind speed for wind direction between ${270}^{\circ}$ and ${330}^{\circ}$ (moderately complex terrain); (

**c**) Standard deviation for wind direction between ${60}^{\circ}$ and ${180}^{\circ}$; (

**d**) Standard deviation for wind direction between ${270}^{\circ}$ and ${330}^{\circ}$.

**Figure 16.**Velocity distribution at 57 m height. (

**a**) Histogram of frequency of occurrence of 10-min average wind speed; (

**b**) Weibull distribution of the PDF obtained from the measured data.

**Figure 17.**Power distribution of the site for the NREL 5-MW wind turbine. (

**a**) Histogram of frequency of occurrence of power values estimated from the measured wind speed; (

**b**) Wind turbine power duration curve.

**Figure 18.**Comparison of DELs computed using the distribution of wind speed and turbulence intensity of the sonic anemometer and the LiDAR measurements. (

**a**) DELs for the blade root bending moment; (

**b**) DELs for the tower base moment; (

**c**) Lifetime DELs (vertical line separates the blade root and the tower base moments).

**Table 1.**Overview of wind speed and wind direction sensors installed at the FREA test site. For orientation, true north is set to ${0}^{\circ}$ and the meteorological convention is followed.

Sensor | Heights [m] | Orientations [${}^{\circ}$] | Sampling Frequency [Hz] |
---|---|---|---|

Sonic anemometers | 40, 57 | ${198}^{\circ}$, ${17}^{\circ}$ | 10.0 |

SONIC CORPORATION SAT900 | |||

Cup anemometers | 10, 20, 30, 40, 50 | ${21}^{\circ}$, ${20}^{\circ}$, ${19}^{\circ}$, ${18}^{\circ}$, ${16}^{\circ}$ | 1.0 |

Thies Clima (4.3351.10.141) | |||

Wind vanes | 10, 30, 50 | ${201}^{\circ}$, ${199}^{\circ}$, ${196}^{\circ}$ | 1.0 |

Thies Clima (4.3150.10.141) | |||

V2 LiDAR | 40, 50, 57, | ${0}^{\circ}$ | ≈1.0 |

Windcube WLS7-724 | 60 to 220 at 20 m interval |

Tool | Description | Set-Up and Parameters |
---|---|---|

Wind speed: 3–23 m/s at wind step of 2 m/s, | ||

General input | 24 m/s | |

Total number of cases: $2\times 12=24$ | ||

Wind type: NTM | ||

Turbulence model: Kaimal | ||

TurbSim | Turbulent wind field generation | Number of grid points (Vertical × Horizontal): $31\times 31$ |

Grid height × Grid width: 145 m × 145 m | ||

Different random seed used for each simulation | ||

OpenFAST | Aero-servo-elastic simulation | Simulation time (T): 630 s |

of a wind turbine | Discard first 30 s of data | |

Time step ($dt$): 0.005 s | ||

Equivalent load frequency: 1 Hz | ||

MLife | Fatigue load analysis | Number of equivalent cycles: $2.98\times {10}^{8}$ |

Analysis corresponds to 20 year lifetime |

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Goit, J.P.; Shimada, S.; Kogaki, T. Can LiDARs Replace Meteorological Masts in Wind Energy? *Energies* **2019**, *12*, 3680.
https://doi.org/10.3390/en12193680

**AMA Style**

Goit JP, Shimada S, Kogaki T. Can LiDARs Replace Meteorological Masts in Wind Energy? *Energies*. 2019; 12(19):3680.
https://doi.org/10.3390/en12193680

**Chicago/Turabian Style**

Goit, Jay Prakash, Susumu Shimada, and Tetsuya Kogaki. 2019. "Can LiDARs Replace Meteorological Masts in Wind Energy?" *Energies* 12, no. 19: 3680.
https://doi.org/10.3390/en12193680