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Remote Sensing of Wind Energy

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A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (30 September 2016) | Viewed by 88145

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Prof. Dr. Charlotte Bay Hasager

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Guest Editor

Wind Energy Department, Technical University of Denmark, Frederiksborgvej 399, DK-4000 Roskilde, Denmark
Interests: marine boundary-layer meteorology; satellite remote sensing; offshore wind energy; wind farm wakes; land- and sea-surface roughness; wind resources; ground-based remote sensing; offshore wind resource assessment
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Dr. Alfredo Peña

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Guest Editor

DTU Wind Energy, Technical University of Denmark, 2800 Kongens Lyngby, Denmark
Interests: wind-power and boundary-layer meteorology; wind resources and siting; remote sensing; lidar; atmospheric turbulence; data analysis and interpretation; meso- and micro-scale modeling

Special Issue Information

Dear Colleagues,

Wind energy is the renewable energy source that contributes the most to the electricity generation worldwide. The need to further understand and efficiently use available wind resources is the key motivation for research in wind energy. The levelized cost of energy from wind power is competitive with that of the conventional energy sources at wind-favorable land sites, while efforts are made to lower the cost of wind energy at offshore, complex, and forested areas. The wake effect within and between wind farms and wind-power forecasting are areas with increasing importance because of the need to accurately predict wind power. There is, therefore, a need for reliable, robust, and accurate measurements and datasets to further improve our understanding of the physical conditions in which wind turbines and wind farms operate and for flow model evaluation.

Nowadays, remote sensing observations are used widely in wind energy applications. During the last couple of years, remote sensing technologies for wind have been improved, both in terms of accuracy and costs. Combined measurement infrastructures, such as that of WindScanner.eu, and new advancements for the measurement of atmospheric turbulence and the wind turbine power performance, turbine wakes, and for improvement of the turbine control, are being progressively achieved. Commercial acceptance of lidars, including floating/buoy lidars, for wind resource assessment, is also on-going. Based on airborne lidar, high-resolution land surface maps are retrieved in forested and complex terrain and provide new valuable inputs to micro- and meso-scale modeling. Surface roughness, terrain elevation, albedo, vegetation parameters, and land- and sea surface temperatures are assessed based on Earth Observation (EO) data and used as input for flow modeling of wind resources (wind atlas) and for the forecasting of wind power at short temporal scales. EO microwave data are used for offshore wind field mapping and applied for wind resource estimation, wind park wake effect, and long-term wind climate conditions.

We welcome submission on all aspects of remote sensing for wind energy application. This includes the above-mentioned topics and those listed below.

Lidar, sodar, radar, and other ground-based remote sensing
EO data from SAR, scatterometer and passive microwaves
EO-based surface roughness and terrain elevation
Remote sensing contribution to wind energy, wind resources, boundary-layer, and wind-power meteorology
Remote sensing in atmospheric turbulence and wind-flow modeling
Remote sensing for wake of wind turbines and wind farms
Remote sensing application in forecasting of winds and wind power
Remote sensing for control of wind turbines and wind farms
Theoretical and experimental issues within remote sensing for wind energy

We would like to invite you to submit articles about your recent research. Review articles covering one or more of these topics are also welcome.

Dr. Charlotte Bay Hasager
Dr. Alfredo Peña
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Published Papers (11 papers)

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Research

2116 KiB

Open AccessArticle

Characterization of Turbulence in Wind Turbine Wakes under Different Stability Conditions from Static Doppler LiDAR Measurements

by Valerie-Marie Kumer, Joachim Reuder and Rannveig Oftedal Eikill

Remote Sens. 2017, 9(3), 242; https://doi.org/10.3390/rs9030242 - 5 Mar 2017

Cited by 6 | Viewed by 6871

Abstract

Wake characteristics are of great importance for wind park performance and turbine loads. While wind tunnel experiments provided a solid base for the basic understanding of the structure and dynamics of wind turbine wakes, the consequent step forward to characterize wakes is full-scale measurements in real atmospheric boundary layer conditions under different stability regimes. Scanning Doppler LiDAR measurements have proven to be a flexible and useful tool for such measurements. However, their advantage of measuring spatial fluctuation is accompanied by the limited temporal resolution of individual sampling volumes within the scanned area. This study presents results from LiDAR Doppler Beam Swing (DBS) measurements and highlights the potential of information retrieved from a spectral analysis of wake measurements. Data originate from three Windcube v1 and sonic anemometers, collected during the Wind Turbine Wake Experiment–Wieringermeer. Despite the ongoing research on the reliability of turbulence retrievals based on DBS data, our results show wake peak frequencies consistent with sonic anemometer measurements. The energy spectra show rather distinct maxima during stable conditions, which broaden during unstable and neutral conditions. Investigations on the effect of blade pitch on downstream wind speed and turbulence intensity profiles indicate the potential for the development of stability-dependent wind farm control strategies. Full article

(This article belongs to the Special Issue Remote Sensing of Wind Energy)

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18096 KiB

Open AccessArticle

Wind Resource Assessment for High-Rise BIWT Using RS-NWP-CFD

by Hyun-Goo Kim, Wan-Ho Jeon and Dong-Hyeok Kim

Remote Sens. 2016, 8(12), 1019; https://doi.org/10.3390/rs8121019 - 13 Dec 2016

Cited by 10 | Viewed by 10222

Abstract

In this paper, a new wind resource assessment procedure for building-integrated wind turbines (BIWTs) is proposed. The objective is to integrate wind turbines at a 555 m high-rise building to be constructed at the center of Seoul, Korea. Wind resource assessment at a high altitude was performed using ground-based remote sensing (RS); numerical weather prediction (NWP) modeling that includes an urban canopy model was evaluated using the remote sensing measurements. Given the high correlation between the model and the measurements, we use the model to produce a long-term wind climate by correlating the model results with the measurements for the short period of the campaign. The wind flow over the high-rise building was simulated using computational fluid dynamics (CFD). The wind resource in Seoul—one of the metropolitan cities located inland and populated by a large number of skyscrapers—was very poor, which results in a wind turbine capacity factor of only 7%. A new standard procedure combining RS, NWP, and CFD is proposed for feasibility studies on high-rise BIWTs in the future. Full article

(This article belongs to the Special Issue Remote Sensing of Wind Energy)

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4653 KiB

Open AccessArticle

Wind Turbine Wake Characterization from Temporally Disjunct 3-D Measurements

by Paula Doubrawa, Rebecca J. Barthelmie, Hui Wang, S. C. Pryor and Matthew J. Churchfield

Remote Sens. 2016, 8(11), 939; https://doi.org/10.3390/rs8110939 - 10 Nov 2016

Cited by 19 | Viewed by 5545

Abstract

Scanning LiDARs can be used to obtain three-dimensional wind measurements in and beyond the atmospheric surface layer. In this work, metrics characterizing wind turbine wakes are derived from LiDAR observations and from large-eddy simulation (LES) data, which are used to recreate the LiDAR scanning geometry. The metrics are calculated for two-dimensional planes in the vertical and cross-stream directions at discrete distances downstream of a turbine under single-wake conditions. The simulation data are used to estimate the uncertainty when mean wake characteristics are quantified from scanning LiDAR measurements, which are temporally disjunct due to the time that the instrument takes to probe a large volume of air. Based on LES output, we determine that wind speeds sampled with the synthetic LiDAR are within 10% of the actual mean values and that the disjunct nature of the scan does not compromise the spatial variation of wind speeds within the planes. We propose scanning geometry density and coverage indices, which quantify the spatial distribution of the sampled points in the area of interest and are valuable to design LiDAR measurement campaigns for wake characterization. We find that scanning geometry coverage is important for estimates of the wake center, orientation and length scales, while density is more important when seeking to characterize the velocity deficit distribution. Full article

(This article belongs to the Special Issue Remote Sensing of Wind Energy)

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8315 KiB

Open AccessArticle

Generic Methodology for Field Calibration of Nacelle-Based Wind Lidars

by Antoine Borraccino, Michael Courtney and Rozenn Wagner

Remote Sens. 2016, 8(11), 907; https://doi.org/10.3390/rs8110907 - 2 Nov 2016

Cited by 13 | Viewed by 6485

Abstract

Nacelle-based Doppler wind lidars have shown promising capabilities to assess power performance, detect yaw misalignment or perform feed-forward control. The power curve application requires uncertainty assessment. Traceable measurements and uncertainties of nacelle-based wind lidars can be obtained through a methodology applicable to any type of existing and upcoming nacelle lidar technology. The generic methodology consists in calibrating all the inputs of the wind field reconstruction algorithms of a lidar. These inputs are the line-of-sight velocity and the beam position, provided by the geometry of the scanning trajectory and the lidar inclination. The line-of-sight velocity is calibrated in atmospheric conditions by comparing it to a reference quantity based on classic instrumentation such as cup anemometers and wind vanes. The generic methodology was tested on two commercially developed lidars, one continuous wave and one pulsed systems, and provides consistent calibration results: linear regressions show a difference of ∼0.5% between the lidar-measured and reference line-of-sight velocities. A comprehensive uncertainty procedure propagates the reference uncertainty to the lidar measurements. At a coverage factor of two, the estimated line-of-sight velocity uncertainty ranges from 3.2% at 3 m

\cdot

^{- 1}

to 1.9% at 16 m

\cdot

^{- 1}

. Most of the line-of-sight velocity uncertainty originates from the reference: the cup anemometer uncertainty accounts for ∼90% of the total uncertainty. The propagation of uncertainties to lidar-reconstructed wind characteristics can use analytical methods in simple cases, which we demonstrate through the example of a two-beam system. The newly developed calibration methodology allows robust evaluation of a nacelle lidar’s performance and uncertainties to be established. Calibrated nacelle lidars may consequently be further used for various wind turbine applications in confidence. Full article

(This article belongs to the Special Issue Remote Sensing of Wind Energy)

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4147 KiB

Open AccessArticle

Long-Range WindScanner System

by Nikola Vasiljević, Guillaume Lea, Michael Courtney, Jean-Pierre Cariou, Jakob Mann and Torben Mikkelsen

Remote Sens. 2016, 8(11), 896; https://doi.org/10.3390/rs8110896 - 29 Oct 2016

Cited by 60 | Viewed by 10717

Abstract

The technical aspects of a multi-Doppler LiDAR instrument, the long-range WindScanner system, are presented accompanied by an overview of the results from several field campaigns. The long-range WindScanner system consists of three spatially-separated, scanning coherent Doppler LiDARs and a remote master computer that coordinates them. The LiDARs were carefully engineered to perform user-defined and time-controlled scanning trajectories. Their wireless coordination via the master computer allows achieving and maintaining the LiDARs’ synchronization within ten milliseconds. The long-range WindScanner system measures the wind field by emitting and directing three laser beams to intersect, and then scanning the beam intersection over a region of interest. The long-range WindScanner system was developed to tackle the need for high-quality observations of wind fields on scales of modern wind turbine and wind farms. It has been in operation since 2013. Full article

(This article belongs to the Special Issue Remote Sensing of Wind Energy)

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15590 KiB

Open AccessArticle

The RUNE Experiment—A Database of Remote-Sensing Observations of Near-Shore Winds

by Rogier Floors, Alfredo Peña, Guillaume Lea, Nikola Vasiljević, Elliot Simon and Michael Courtney

Remote Sens. 2016, 8(11), 884; https://doi.org/10.3390/rs8110884 - 26 Oct 2016

Cited by 28 | Viewed by 7103

Abstract

We present a comprehensive database of near-shore wind observations that were carried out during the experimental campaign of the RUNE project. RUNE aims at reducing the uncertainty of the near-shore wind resource estimates from model outputs by using lidar, ocean, and satellite observations. Here, we concentrate on describing the lidar measurements. The campaign was conducted from November 2015 to February 2016 on the west coast of Denmark and comprises measurements from eight lidars, an ocean buoy and three types of satellites. The wind speed was estimated based on measurements from a scanning lidar performing PPIs, two scanning lidars performing dual synchronized scans, and five vertical profiling lidars, of which one was operating offshore on a floating platform. The availability of measurements is highest for the profiling lidars, followed by the lidar performing PPIs, those performing the dual setup, and the lidar buoy. Analysis of the lidar measurements reveals good agreement between the estimated 10-min wind speeds, although the instruments used different scanning strategies and measured different volumes in the atmosphere. The campaign is characterized by strong westerlies with occasional storms. Full article

(This article belongs to the Special Issue Remote Sensing of Wind Energy)

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2527 KiB

Open AccessArticle

A Methodology for the Reconstruction of 2D Horizontal Wind Fields of Wind Turbine Wakes Based on Dual-Doppler Lidar Measurements

by Marijn F. Van Dooren, Davide Trabucchi and Martin Kühn

Remote Sens. 2016, 8(10), 809; https://doi.org/10.3390/rs8100809 - 29 Sep 2016

Cited by 31 | Viewed by 7792

Abstract

Dual-Doppler lidar is a powerful remote sensing technique that can accurately measure horizontal wind speeds and enable the reconstruction of two-dimensional wind fields based on measurements from two separate lidars. Previous research has provided a framework of dual-Doppler algorithms for processing both radar and lidar measurements, but their application to wake measurements has not been addressed in detail yet. The objective of this paper is to reconstruct two-dimensional wind fields of wind turbine wakes and assess the performance of dual-Doppler lidar scanning strategies, using the newly developed Multiple-Lidar Wind Field Evaluation Algorithm (MuLiWEA). This processes non-synchronous dual-Doppler lidar measurements and solves the horizontal wind field with a set of linear equations, also considering the mass continuity equation. MuLiWEA was applied on simulated measurements of a simulated wind turbine wake, with two typical dual-Doppler lidar measurement scenarios. The results showed inaccuracies caused by the inhomogeneous spatial distribution of the measurements in all directions, related to the ground-based scanning of a wind field at wind turbine hub height. Additionally, MuLiWEA was applied on a real dual-Doppler lidar measurement scenario in the German offshore wind farm “alpha ventus”. It was concluded that the performance of both simulated and real lidar measurement scenarios in combination with MuLiWEA is promising. Although the accuracy of the reconstructed wind fields is compromised by the practical limitations of an offshore dual-Doppler lidar measurement setup, the performance shows sufficient accuracy to serve as a basis for 10 min average steady wake model validation. Full article

(This article belongs to the Special Issue Remote Sensing of Wind Energy)

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4599 KiB

Open AccessArticle

A Case Study of Land-Surface-Temperature Impact from Large-Scale Deployment of Wind Farms in China from Guazhou

by Rui Chang, Rong Zhu and Peng Guo

Remote Sens. 2016, 8(10), 790; https://doi.org/10.3390/rs8100790 - 23 Sep 2016

Cited by 30 | Viewed by 7141

Abstract

The wind industry in China has experienced a rapid expansion of capacity after 2009, especially in northwestern China, where the China’s first 10 GW-level wind power project is located. Based on the analysis from Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) data for period of 2005–2012, the potential LST impacts from the large-scale wind farms in northwestern China’s Guazhou are investigated in this paper. It shows the noticeable nighttime warming trends on LST over the wind farm areas relative to the nearby non-wind-farm regions in Guazhou and that the nighttime LST warming is strongest in summer (0.51 °C/8 years), followed by autumn (0.48 °C/8 years) and weakest in winter (0.38 °C/8 years) with no warming trend observed in spring. Meanwhile, the quantitative comparison results firstly indicate that the nighttime LST warming from wind farm areas are less than those from the urban areas in this work. Full article

(This article belongs to the Special Issue Remote Sensing of Wind Energy)

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6170 KiB

Open AccessArticle

An Inter-Comparison Study of Multi- and DBS Lidar Measurements in Complex Terrain

by Lukas Pauscher, Nikola Vasiljevic, Doron Callies, Guillaume Lea, Jakob Mann, Tobias Klaas, Julian Hieronimus, Julia Gottschall, Annedore Schwesig, Martin Kühn and Michael Courtney

Remote Sens. 2016, 8(9), 782; https://doi.org/10.3390/rs8090782 - 21 Sep 2016

Cited by 51 | Viewed by 9413

Abstract

Wind measurements using classical profiling lidars suffer from systematic measurement errors in complex terrain. Moreover, their ability to measure turbulence quantities is unsatisfactory for wind-energy applications. This paper presents results from a measurement campaign during which multiple WindScanners were focused on one point next to a reference mast in complex terrain. This multi-lidar (ML) technique is also compared to a profiling lidar using the Doppler beam swinging (DBS) method. First- and second-order statistics of the radial wind velocities from the individual instruments and the horizontal wind components of several ML combinations are analysed in comparison to sonic anemometry and DBS measurements. The results for the wind speed show significantly reduced scatter and directional error for the ML method in comparison to the DBS lidar. The analysis of the second-order statistics also reveals a significantly better correlation for the ML technique than for the DBS lidar, when compared to the sonic. However, the probe volume averaging of the lidars leads to an attenuation of the turbulence at high wave numbers. Also the configuration (i.e., angles) of the WindScanners in the ML method seems to be more important for turbulence measurements. In summary, the results clearly show the advantages of the ML technique in complex terrain and indicate that it has the potential to achieve significantly higher accuracy in measuring turbulence quantities for wind-energy applications than classical profiling lidars. Full article

(This article belongs to the Special Issue Remote Sensing of Wind Energy)

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2880 KiB

Open AccessArticle

Quarter-Century Offshore Winds from SSM/I and WRF in the North Sea and South China Sea

by Charlotte Bay Hasager, Poul Astrup, Rong Zhu, Rui Chang, Merete Badger and Andrea N. Hahmann

Remote Sens. 2016, 8(9), 769; https://doi.org/10.3390/rs8090769 - 20 Sep 2016

Cited by 13 | Viewed by 7589

Abstract

We study the wind climate and its long-term variability in the North Sea and South China Sea, areas relevant for offshore wind energy development, using satellite-based wind data, because very few reliable long-term in-situ sea surface wind observations are available. The Special Sensor Microwave Imager (SSM/I) ocean winds extrapolated from 10 m to 100 m using the Charnock relationship and the logarithmic profile method are compared to Weather Research and Forecasting (WRF) model results in both seas and to in-situ observations in the North Sea. The mean wind speed from SSM/I and WRF differ only by 0.1 m/s at Fino1 in the North Sea, while west of Hainan in the South China Sea the difference is 1.0 m/s. Linear regression between SSM/I and WRF winds at 100 m show correlation coefficients squared of 0.75 and 0.67, standard deviation of 1.67 m/s and 1.41 m/s, and mean difference of −0.12 m/s and 0.83 m/s for Fino1 and Hainan, respectively. The WRF-derived winds overestimate the values in the South China Sea. The inter-annual wind speed variability is estimated as 4.6% and 4.4% based on SSM/I at Fino1 and Hainan, respectively. We find significant changes in the seasonal wind pattern at Fino1 with springtime winds arriving one month earlier from 1988 to 2013 and higher winds in June; no yearly trend in wind speed is observed in the two seas. Full article

(This article belongs to the Special Issue Remote Sensing of Wind Energy)

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6028 KiB

Open AccessArticle

Assessing the Severity of Wind Gusts with Lidar

by René Bos, Ashim Giyanani and Wim Bierbooms

Remote Sens. 2016, 8(9), 758; https://doi.org/10.3390/rs8090758 - 14 Sep 2016

Cited by 18 | Viewed by 6506

Abstract

Lidars have gained a lot of popularity in the field of wind energy, partly because of their potential to be used for wind turbine control. By scanning the oncoming wind field, any threats such as gusts can be detected early and high loads can be avoided by taking preventive actions. Unfortunately, lidars suffer from some inherent weaknesses that hinder measuring gusts; e.g., the averaging of high-frequency fluctuations and only measuring along the line of sight). This paper proposes a method to construct a useful signal from a lidar by fitting a homogeneous Gaussian velocity field to a set of scattered measurements. The output signal, an along-wind force, acts as a measure for the damaging potential of an oncoming gust and is shown to agree with the rotor-effective wind speed (a similar control input, but derived directly from the wind turbine’s shaft torque). Low data availability and the disadvantage of not knowing the velocity between the lidar beams is translated into uncertainty and integrated in the output signal. This allows a designer to establish a control strategy based on risk, with the ultimate goal to reduce the extreme loads during operation. Full article

(This article belongs to the Special Issue Remote Sensing of Wind Energy)

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