Correction of LiDAR measurement error in complex terrain by CFD: Case study of the Yangyang pumped storage plant

Kim, Hyun‐Goo; Meißner, Cathérine

doi:10.1177/0309524x17709725

Cited by 8 publications

(5 citation statements)

References 3 publications

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“…For the ZephIR and the Leosphere WindCube lidar, lidar error estimation methods using the Reynolds-averaged Navier-Stokes (RANS) computational fluid dynamics (CFD) models WindSim and Meteodyn WT were developed and tested over the years (Harris et al, 2010;Meissner and Boquet, 2011;Bezault et al, 2012;Jokela et al, 2013;Kim and Meissner, 2017). The lidar error estimation approaches are comparable to that proposed by Bingöl et al (2009).…”

Section: Introductionmentioning

confidence: 99%

The five main influencing factors for lidar errors in complex terrain

Klaas-Witt

Emeis

2022

Wind Energ. Sci.

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Abstract. Lidars have become a valuable technology to assess the wind resource at hub height of modern wind turbines. However, because of the assumption of homogeneous flow in their wind vector reconstruction algorithms, common wind profile Doppler lidars suffer from errors at complex terrain sites. This study analyses the impact of the five main influencing factors for lidar measurement errors in complex terrain, i.e. orographic complexity, measurement height, surface roughness and forest, atmospheric stability, and half-cone opening angle, in a non-dimensional, model-based parameter study. In a novel approach, the lidar error ε is split up into a part εc, caused by flow curvature at the measurement points of the lidar, and a part εs, caused by the local speed-up effects between the measurement points. This approach allows for a systematic and complete interpretation of the influence of the half-cone opening angle φ of the lidar on the total lidar error ε. It also provides information about the uncertainty in simple lidar error estimations that are based on inflow and outflow angles at the measurement points. The model-based parameter study is limited to two-dimensional Gaussian hills with hill height H and hill half-width L. H/L and z/L, with z being the measurement height, are identified as the main scaling factors for the lidar error. Three flow models of different complexity are used to estimate the lidar errors. The outcome of the study provides various findings that enable an assessment of the applicability of these flow models. The study clearly shows that orographic complexity, roughness and forest characteristics, and atmospheric stability have a significant influence on lidar error estimation. Based on the error separation approach it furthermore allows for an in-depth analysis of the influence of reduced half-cone opening angles, explaining contradiction in the previously available literature. The choice and parameterization of flow models and the design of methods for lidar error estimation are found to be essential to achieve accurate results. The use of a Reynolds-averaged Navier–Stokes (RANS) computational fluid dynamics (CFD) model in conjunction with an appropriate forest model is highly recommended for lidar error estimations in complex terrain since forest (and roughness) tends to reduce the lidar error. If atmospheric stability variation at a measurement site plays a vital role, it should also be considered in the modelling. When planning a measurement campaign, an accurate estimation of the predicted lidar error should be carried out in advance to choose a reasonable measurement location. This will decrease measurement uncertainties and maximize the value of the measurement data.

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Section: Introductionmentioning

confidence: 99%

The five main influencing factors for lidar errors in complex terrain

Klaas-Witt

Emeis

2022

Wind Energ. Sci.

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“…Additionally, the study states that the lidar error is independent of height at this site (Foussekis, 2009). For the ZephIR and the Leosphere Windcube lidar, lidar error estimation methods using the Reynolds-averaged Navier-Stokes (RANS) computational fluid dynamics (CFD) models WindSim and Meteodyn WT were developed and tested over the years (Harris et al, 2010;Meissner and Boquet, 2011;Bezault et al, 2012;Jokela et al, 2013;Kim and Meissner, 2017). The lidar error estimation approaches are comparable to that proposed by Bingöl et al (2009).…”

Section: Introductionmentioning

confidence: 99%

The five main influencing factors on lidar errors in complex terrain

Klaas

Emeis

2021

Preprint

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Abstract. Light detection and ranging (notably Doppler lidar), has become a valuable technology to assess the wind resource at hub height of modern wind turbines. However, because of their measurement principle, common wind profile Doppler lidars suffer from errors at complex terrain sites. This study analyses the impact of the five main influencing factors at lidar measurement errors in complex terrain, i.e. orographic complexity, measurement height, surface roughness and forest, atmospheric stability and half-cone opening angle, in a non-dimensional, model-based parameter study. In a novel approach, the lidar error ε is split up into a part εc, caused by flow curvature at the measurement points of the lidar and a part εs, caused by the local speed-up effects between the measurement points. This approach, e.g., allows for a systematic and complete interpretation of the influence of the half-cone opening angle φ of the lidar. It also provides information about the uncertainty of simple lidar error estimations that are based on inflow and outflow angles at the measurement points. The model-based parameter study is limited to two-dimensional Gaussian hills with hill height H and hill half-width L. H/L and z/L, with z being the measurement height, are identified as the main scaling factors for the lidar error. Three flow models of different complexity are used to estimate the lidar errors. The outcome of the study provides manifold findings that enable an assessment of the applicability of these flow models. The study clearly shows that orographic complexity, roughness and forest characteristics, as well as atmospheric stability, have a significant influence on lidar error estimation. Based on the error separation approach it furthermore allows for an in-depth analysis of the influence of reduced half-cone opening angles. The choice and parameterization of flow models and the design of methods for lidar error estimation are found to be essential to achieve accurate results. The use of a RANS CFD model in conjunction with an appropriate forest model is highly recommended for lidar error estimations in complex terrain. If atmospheric stability variation at a measurement site plays a vital role, it should also be considered in the modelling. When planning a measurement campaign, an accurate estimation of the prospective lidar error should be carried out in advance to decrease measurement uncertainties and maximize the value.

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“…The accuracy of the WindCube LiDAR has been verified through a comparative verification with SODAR (SOnic Detection And Ranging) under various terrains such as plain, hilly terrain, and urban [13]. A measurement algorithm of the LiDAR and a correction method in complex terrain using computational fluid dynamics (CFD) are well described in Kim and Meissner (2017) [14].…”

Section: Lidar Measurement Campaignmentioning

confidence: 99%

“…The LiDAR was deployed between wind turbines #2 and #3 at the Shinan wind farm in order to measure the sea breeze, which is a main wind, without interruption of the terrain features (Figure 2). Due to the conical scanning setup of WindCube [14], the scanning plane at the hub height of the wind turbine (the yellow circle in Figure 2) might be interfered partially by the blade rotation (the white circle of #2). To prevent this, the Li-DAR is rotated 35° clockwise so that the scanning points (four yellow dots) do not overlap in the rotor plane.…”

Section: Lidar Measurement Campaignmentioning

confidence: 99%

Analysis of Wind Turbine Aging through Operation Data Calibrated by LiDAR Measurement

Kim

2021

Energies

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This study analyzed the performance decline of wind turbine with age using the SCADA (Supervisory Control And Data Acquisition) data and the short-term in situ LiDAR (Light Detection and Ranging) measurements taken at the Shinan wind farm located on the coast of Bigeumdo Island in the southwestern sea of South Korea. Existing methods have generally attempted to estimate performance aging through long-term trend analysis of a normalized capacity factor in which wind speed variability is calibrated. However, this study proposes a new method using SCADA data for wind farms whose total operation period is short (less than a decade). That is, the trend of power output deficit between predicted and actual power generation was analyzed in order to estimate performance aging, wherein a theoretically predicted level of power generation was calculated by substituting a free stream wind speed projecting to a wind turbine into its power curve. To calibrate a distorted wind speed measurement in a nacelle anemometer caused by the wake effect resulting from the rotation of wind-turbine blades and the shape of the nacelle, the free stream wind speed was measured using LiDAR remote sensing as the reference data; and the nacelle transfer function, which converts nacelle wind speed into free stream wind speed, was derived. A four-year analysis of the Shinan wind farm showed that the rate of performance aging of the wind turbines was estimated to be −0.52%p/year.

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Correction of LiDAR measurement error in complex terrain by CFD: Case study of the Yangyang pumped storage plant

Cited by 8 publications

References 3 publications

The five main influencing factors for lidar errors in complex terrain

The five main influencing factors for lidar errors in complex terrain

The five main influencing factors on lidar errors in complex terrain

Analysis of Wind Turbine Aging through Operation Data Calibrated by LiDAR Measurement

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