Field Measurements of Wind Characteristics Using Lidar on a Wind Farm with Downwind Turbines Installed in a Complex Terrain Region

energies

Article Field Measurements of Wind Characteristics Using LiDAR on a Wind Farm with Downwind Turbines Installed in a Complex Terrain Region

Tetsuya Kogaki 1,*, Kenichi Sakurai 1, Susumu Shimada 1 , Hirokazu Kawabata 1, Yusuke Otake 2, Katsutoshi Kondo 2 and Emi Fujita 2 1 Renewable Energy Research Center, National Institute of Advanced Industrial Science and Technology (AIST), Koriyama, Fukushima 963-0298, Japan; [email protected] (K.S.); [email protected] (S.S.); [email protected] (H.K.) 2 Hitachi Ltd., Chiyoda-ku, Tokyo 100-8280, Japan; [email protected] (Y.O.); [email protected] (K.K.); [email protected] (E.F.) * Correspondence: [email protected]; Tel.: +81-29-861-7251

Received: 27 August 2020; Accepted: 25 September 2020; Published: 2 October 2020

Abstract: Downwind turbines have favorable characteristics such as effective energy capture in up-flow wind conditions over complex terrains. They also have reduced risk of severe accidents in the event of disruptions to electrical networks during strong storms due to the free-yaw effect of downwind turbines. These favorable characteristics have been confirmed by wind-towing tank experiments and computational fluid dynamics (CFD) simulations. However, these advantages have not been fully demonstrated in field experiments on actual wind farms. In this study—although the final objective was to demonstrate the potential advantages of downwind turbines through field experiments—field measurements were performed using a vertical-profiling light detection and ranging (LiDAR) system on a wind farm with downwind turbines installed in complex terrains. To deduce the horizontal wind speed, vertical-profiling LiDARs assume that the flow of air is uniform in space and time. However, in complex terrains and/or in wind farms where terrain and/or wind turbines cause flow distortion or disturbances in time and space, this assumption is not valid, resulting in erroneous wind speed estimates. The magnitude of this error was evaluated by comparing LiDAR measurements with those obtained using a cup anemometer mounted on a meteorological mast and detailed analysis of line-of-sight wind speeds. A factor that expresses the nonuniformity of wind speed in the horizontal measurement plane of vertical-profiling LiDAR is proposed to estimate the errors in wind speed. The possibility of measuring and evaluating various wind characteristics such as flow inclination angles, turbulence intensities, wind shear and wind veer, which are important for wind turbine design and for wind farm operation is demonstrated. However, additional evidence of actual field measurements on wind farms in areas with complex terrains is required in order to obtain more universal and objective evaluations.

Keywords: light detection and ranging; complex terrains; wind speed; turbulence intensity; ﬂow inclination angle; wind shear and veer

1. Introduction Downwind turbines have the advantages of being able to generate power efficiently—even under up-flow wind conditions in complex terrains. They are also at reduced risk of collapse due to the free-yaw effect—even when electrical network failures occur during storms. Downwind turbines are therefore expected to offer a high degree of safety and reliability, even under the severe wind conditions that are frequently observed in Japan [1,2]. While the advantages of downwind turbines

Energies 2020, 13, 5135; doi:10.3390/en13195135 www.mdpi.com/journal/energies Energies 2020, 13, 5135 2 of 24 have been demonstrated through towing-tank experiments and computational fluid dynamics (CFD) simulations [3], they have not been fully verified on actual wind farms. The ultimate goal of the present study was to demonstrate the characteristics of downwind turbines in complex terrains. To begin with, we therefore conducted field measurements on a wind farm with downwind turbines installed over complex terrains. Specifically, we used a vertical-profiling light detection and ranging (LiDAR) system to evaluate the wind conditions around the downwind turbines. The vertical-profiling LiDAR uses a conical scanning pattern directed skyward to evaluate wind speed. The assumption with these systems is that there is spatial and temporal uniformity in the wind on the altitude measurement plane. However, since wind conditions are rarely uniform, vertical-profiling LiDAR is considered to be more prone to measurement errors in complex terrains. For example, a study conducted using a vertical-profiling LiDAR in complex terrains in Japan [4] found considerable systematic and random errors in wind speed measurements when compared to measurements performed using a cup anemometer installed on a meteorological mast. In complex European terrains, which is generally considered to be less complex than that in Japan, measurement results obtained using continuous wave and pulsed LiDAR systems have been compared to those obtained with a cup anemometer and ultrasonic anemometer installed on a meteorological mast [5]. The results of that study revealed a strong correlation between both the types of LiDAR and the cup and ultrasonic anemometer and low scatter in horizontal wind speed, despite a systematic error of approximately 6% (i.e., the LiDAR results underestimated wind speed). Using CFD simulations and engineering models, attempts have been made to analyze [6,7] and correct [8,9] errors caused by the surrounding topography in vertical-profiling LiDAR measurements. Vogstad et al. [8] reported that by introducing a correction based on appropriate CFD simulations, the uncertainty associated with LiDAR measurements performed in areas with complex terrains could be reduced to about 2.5%, which is comparable to measurements obtained using a cup anemometer. In a comprehensive technical review on remote sensing technology, Pena Diaz et al. [10] concluded that, in the near future, LiDARs could replace meteorological masts, even in areas with complex terrains, if pre-measurement simulations are conducted to determine the optimal placement of the LiDAR followed by simulation-based corrections after the LiDAR measurements are obtained. A relatively long-term study (approximately one year) conducted by Goit et al. [11] showed good agreement in average wind speed and turbulence intensity between LiDAR and ultrasonic anemometer measurements, suggesting that LiDARs could replace meteorological masts to assess wind resources, annual energy production and loads. It is conceivable that the errors associated with LiDAR measurements conducted in complex terrains can be decreased by reducing the cone angle of the conical laser beams directed, as this would reduce the area (assumed to be uniform) of the altitude measurement plane. Indeed, there are currently vertical-profiling LiDARs with such functionality (complex terrain mode). However, it has been confirmed, both theoretically [10] and experimentally [5], that the error in horizontal wind speed measurements obtained by LiDARs is independent of the cone angle. Attempts have been made to measure and evaluate complex flow phenomena by wind turbine wakes or by complex terrains using scanning LiDARs [12–14]. A wake of a continuously yawing wind turbine in a large wind farm was detected by a single scanning LiDAR with plan position indicator (PPI) scans in flat terrains [12] and even in moderately complex coastal terrains [13]. In the famous Perdigão experiment, three sets of scanning lidar with different scan strategies such as arc [15], range height indicator (RHI) and velocity azimuth display (VAD) were operated to scan the wake of the wind turbine in highly complex terrains [14]. Vertical and horizontal profiles of a wake were derived by each scan strategy in that study, however integration data from independently operated three systems indicated some differences in wake center position and it is concluded that to identify common methods for further wake metrics such as wake width and wind speed deficit is the next steps. Energies 2020, 13, 5135 3 of 24

Honrubia et al. [16] used a LiDAR to measure vertical changes in wind speed (wind shear) in relatively complex terrains in Europe and advocated the usefulness of LiDAR for inflow wind-shear measurements when evaluating wind turbine performance. However, as noted above, although there is a possibility that a vertical-profiling LiDAR can be moderately useful in mild complex terrains, few studies have been conducted in highly complicated terrains, such as that typically found in Japan. It is therefore not clear how reliable and accurate LiDARs are and for what purposes they can be used, in wind farms over highly complex terrains. In this study, we therefore evaluated the accuracy and reliability of LiDAR wind measurements conducted in complex terrains, taking turbine wakes into account. In addition, we performed detailed analyses of the raw data obtained for line-of-sight (LOS) wind speeds. We also analyzed the wind characteristics of complex terrain sites, such as the flow inclination and turbulence intensity in different wind directions and investigated the relationship between the wind characteristics and topography in complex terrains with the future aim of evaluating of downwind turbine characteristics in such environments.

2. Test Site and Measurement Setup In this study, we performed measurements on a wind farm in Japan. The wind farm employed Hitachi HTW 2.0-80 downwind turbines that had a hub height of 78 m and turbine diameters of 80 m. We installed a vertical-profiling LiDAR (DIABREZZA_W, Mitsubishi Electric Corporation) system between two wind turbines on the wind farm (a location hereinafter referred to as the “position between turbines”). The main LiDAR specifications used in this study are shown in Table1. As shown in Figure1, laser beams were directed skyward in five LOS directions ranging from LOS0 (vertical) to LOS4, at approximately at 0.4 s intervals. Using this system, we simultaneously measured the wind speed at 20 different altitudes ranging from 50 m to 126 m above ground level (AGL) at 4-m intervals. The half opening angle of the scanning cone is 30◦ (Figure1a). A method to retrieve horizontal and vertical wind speeds from LOS data is generally used one for DBS (Doppler beam swinging) scan mode of LiDAR measurements [17]. In calibration tests conducted at well-known European test sites, such as ECN (the Energy Research Center in The Netherlands) and at DTU (the Technical University of Denmark), the wind speed and direction measurements obtained using the DIABREZZA_W have been demonstrated to be in good agreement with the results obtained using cup anemometers and wind vanes installed on meteorological masts [18].

Table 1. Main specification of a vertical-profiling LiDAR, DIABREZZA_W,Mitsubishi Electric Corporation.

Aspect Characteristics Observation range 40 m–250 m AGL Scanning pattern Digital switching Scanning direction 0◦, 90◦, 180◦, 270◦, Vertical Range resolution Selectable from 20 m (selected in this study), 25 m, 30 m Measurable wind speed range 0 m/s–60 m/s Data update rate 2 s or less Doppler velocity range 30 m/s–+30 m/s − Doppler velocity accuracy error 0.1 m/s or less ± Laser wavelength [µm] 1.55 µm single frequency Environmental conditions 20 C–+ 40 C, 0–100% RH − ◦ ◦

Figure2 shows the locations of the LiDAR and two turbines and Figure3 shows topographic profiles of the terrain in the north–south, west–east, northeast–southwest and northwest–southeast directions. The profiles show that the site is a highly complex terrain with a horizontal length of approximately 1000 m and an elevation difference of more than 300 m. The elevation of the measurement location (position between turbines) was 488 m above mean sea level (AMSL). The measurement Energies 2020, 13, 5135 4 of 25

South (LOS3) West Vertical (LOS4) East (LOS0) (LOS2) 30 (deg.) Energies 2020, 13, 5135 30 [deg.] 4 of 24 North period spanned approximately(LOS1) three months, from October 1 to December 26, 2017, excluding the periodEnergies 2020 from, 13 November, 5135 7 to 12 when measurements were not taken due to system maintenance.4 of 25

South (LOS3) West Vertical (LOS4) East (LOS0) (LOS2) 30 (deg.) 30N [deg.] North (LOS1)

(a) (b)

Figure 1. Vertical-profiling LiDAR (DIABREZZA_W, Mitsubishi Electric Corporation) used in this study. (a) Emission directionsN of laser; (b) measurement heights above ground.

Figure 2 shows the locations of the LiDAR and two turbines and Figure 3 shows topographic profiles of the terrain in the north–south, west–east, northeast–southwest and northwest–southeast directions. The profiles show that the site is a highly complex terrain with a horizontal length of approximately 1000 m and an elevation difference of more than 300 m. The elevation of the measurement location (position between turbines) was 488 m above mean sea level (AMSL). The (a) (b) measurement period spanned approximately three months, from October 1 to December 26, 2017, excludingFigure the 1.1. Vertical-proﬁlingVertical-profilingperiod from November LiDAR (DIABREZZA_W, 7 to 12 when measurements Mitsubishi Electric were Corporation) not taken useddue into thissystem maintenance.study.study. ((aa)) EmissionEmission directionsdirections ofof laser;laser; ((bb)) measurementmeasurement heightsheights aboveabove ground.ground.

Figure 2 shows the locations of the LiDAR and two turbines and Figure 3 shows topographic profiles of the terrain in the north–south, west–east, northeast–southwest and northwest–southeast directions. The profiles show that the site is a highly complex terrain with a horizontal length of approximately 1000 m and an elevation difference of more than 300 m. The elevation of the measurement location (position between turbines) was 488 m above mean sea level (AMSL). The measurement period spanned approximately three months, from October 1 to December 26, 2017, excluding the period from November 7 to 12 when measurements were not taken due to system maintenance.

Figure 2. Horizontal (bottom) (bottom) and and side side (top) (top) layout layoutss of of the the LiDAR LiDAR and and both both wind wind turbines. turbines.

Figure 2. Horizontal (bottom) and side (top) layouts of the LiDAR and both wind turbines. Energies 2020, 13, 5135 5 of 24 Energies 2020, 13, 5135 5 of 25

Energies 2020, 13, 5135 5 of 25

FigureFigure 3. Cardinal3. Cardinal direction direction profilesprofiles of the the terrain terrain ar aroundound the the measurement measurement site site(Origin: (Origin: LiDAR LiDAR installed between two wind turbines on the wind farm. Altitude data obtained from the Geospatial installedFigure between 3. Cardinal two direction wind turbines profiles on of thethe windterrain farm. around Altitude the measurement data obtained site (Origin: from the LiDAR Geospatial Information Authority of Japan. Informationinstalled Authoritybetween two of wind Japan. turbines on the wind farm. Altitude data obtained from the Geospatial Information Authority of Japan. TheThe LiDAR LiDAR was was then then moved moved toto a location where where a ameteorological meteorological mast mast was wasinstalled installed near the near the windwind farm Thefarm (hereinafter LiDAR (hereinafter was then referred referred moved toto astoas a thethe location “mast"mast whereposi position”),tion"), a meteorological to tofurther further evaluate mast evaluate was the installedreliability the reliability near of thethe of the vertical-profiling LiDAR measurements obtained in complex terrains. Figure 4 shows the schematic vertical-profilingwind farm (hereinafter LiDAR measurementsreferred to as the obtained "mast posi intion"), complex to further terrains. evaluate Figure the4 shows reliability the of schematic the configuration of the sensors installed on the meteorological mast and a photo of the installed LiDAR, configurationvertical-profiling of the LiDAR sensors measurements installed on theobtained meteorological in complex mastterrains. and Figure a photo 4 shows of the the installed schematic LiDAR, which was positioned 5 m north of the meteorological mast. In addition to cup anemometers and whichconfiguration was positioned of the sensors 5 m north installed of the on meteorologicalthe meteorological mast. mast and In addition a photo of to the cup installed anemometers LiDAR, and windwhich vanes, was positionedan ultrasonic 5 m anemometer/thermometer north of the meteorolog ical(SAT-900, mast. Insonic addition Corporation) to cup anemometerswas installed atand a wind vanes, an ultrasonic anemometer/thermometer (SAT-900, sonic Corporation) was installed at a heightwind vanes, of 53 man AGL. ultrasonic The topographicanemometer/thermometer profiles in the (SAT-900,north–south, sonic west–east, Corporation) northeast–southwest, was installed at a height of 53 m AGL. The topographic profiles in the north–south, west–east, northeast–southwest, andheight northwest–southeast of 53 m AGL. The cardinal topographic directions profiles in Figure in the 5north–south, show that the west–east, mast position, northeast–southwest, like the position andbetweenand northwest–southeast northwest–southeast turbines, was in cardinal cardinala highly directions direcomplexctions terrain in Figure Figure with 55 show showa horizontal that that the the lengthmast mast position, of position, about like 1000 likethe m position the and position a betweendifferencebetween turbines, turbines, in elevation was was in ofin a approximatelya highly highly complexcomplex 300 terrain m. The with withelevation a ahorizontal horizontal of the lengthmeasurement length of about of aboutlocation 1000 1000m (mastand m a and a differenceposition)difference in was elevationin elevation431 m AMSL. of of approximately approximately The measurement 300300 m.period The The coveredelevation elevation roughly of ofthe the measurementone measurement month, from location January location (mast 1– (mast position)31,position) 2018, was excludingwas 431 431 m AMSL.m theAMSL. period The The measurementfrom measurement January period19–period26, when covered measurements roughly roughly one one month,were month, not from taken from January 1–31due to1– January maintenance. 2018,31, excluding 2018, excluding the period the fromperiod January from January 19–26, when 19–26, measurements when measurements were not were taken not due taken to maintenance.due to maintenance.

Meteorological mast

LiDAR

(a) (b)

Figure 4. LiDAR installed (a)near the meteorological mast position. (a) Outline of measurements;(b) (b) on- site installation configuration. FigureFigure 4. 4.LiDAR LiDAR installedinstalled near near the the meteorological meteorological mast position. mast position. (a) Outline (a )of Outline measurements; of measurements; (b) on- (b) on-sitesite installation installation configuration. conﬁguration. Energies 2020, 13, 5135 6 of 24 Energies 2020, 13, 5135 6 of 25

FigureFigure 5. Cardinal directiondirection proﬁles profiles of theof the terrain terrain around around the measurement the measurement site (Origin: site (Origin: LiDAR installed LiDAR installedat the meteorological at the meteorological mast position. mast Altitudeposition. data Altitude were obtaineddata were from obtained the Geospatial from the Information Geospatial InformationAuthority of Authority Japan. of Japan.

AlthoughAlthough thethe twotwo LiDAR LiDAR measurement measurement locations location differeds differed with respect with torespect topographical to topographical conditions, conditions,the horizontal the distance horizontal between distance the locations between was th onlye locations about 1430 was m andonly their about topographic 1430 m complexityand their topographicand vegetation complexity were similar. and vegetation were similar. 3. Results and Discussion 3. Results and Discussion 3.1. Comparison with Meteorological Mast Measurements 3.1. Comparison with Meteorological Mast Measurements A measurement method simultaneously utilizing a meteorological mast that is lower than the measurementA measurement height wasmethod used simultaneously in Japan as a means utilizing of improving a meteorological the accuracy mast and that reliability is lower of than LiDAR the measurementmeasurement height in complex was terrainsused in [4Japan]. This as correction a means methodof improving was shown the accuracy to provide and the reliability same degree of LiDARof data measurement consistency as in when complex measurements terrains [4]. are This performed correction on flatmethod terrains. was However,shown to provide since this the method same degreecould notof data be used consistency at the present as when site, measurements the reliability ar ofe performed the vertical-profiling on flat terrains. LiDAR However, measurements since this in methodcomplex could terrains not was be evaluated used at bythe simultaneous present site, measurements the reliability using of athe meteorological vertical-profiling mast installedLiDAR measurementsat a location close in tocomplex the wind terrains farm, as describedwas evalua above.ted by simultaneous measurements using a meteorologicalFigure6 shows mast installed the data-acquisition at a location close rate to of the the wind LiDAR farm, over as described time at above. the mast position, for representativeFigure 6 shows heights the data-acquisition of 50 m (minimum rate of measurement the LiDAR over height), time 78 at m the (turbine mast position, hub height), for representative118 m (maximum heights turbine of 50 rotor m (minimum height) and measurem 126 m (maximument height), measurement 78 m (turbi height).ne hub Theheight), data with118 m a (maximumsignal-to-noise turbine ratio rotor (SNR) height) of 7 dB an ord more126 m is (maximum treated as valid measurement data, while height). the low The SNR data data with were a omitted signal- to-noisein the LiDAR ratio (SNR) used of in 7 this dB study.or more Although is treated the as valid data-acquisition data, while the rate low over SNR the data entire were measurement omitted in theperiod LiDAR was used at least in this 90% study. at 50 m Although AGL, there the were data-acq threeuisition time periods rate over where the theentire data-acquisition measurement rate period fell wasbelow at least 80%, 90% at the at 50 higher m AGL, heights there of were 118 mthree and time 126 periods m AGL. where This decrease the data-acq mayuisition have been rate attributedfell below 80%,to greater at the concentrationshigher heights of of 118 water m and vapor 126 near m AGL. the ground This decrease surface may due have to fog been or the attributed high elevation to greater of concentrationsthe measurement of location,water vapor which ne mayar the have ground interfered surface with due laser to transmission. fog or the high In this elevation study, we of used the measurement10-min datasets location, with minimum which may data-acquisition have interfered rateswith oflaser 80% transmission. and excluded In allthis of study, the datasets we used with 10- mindata-acquisition datasets with rates minimum that were data-acquisition too low for statistical rates of 80% reliability. and excluded all of the datasets with data- acquisition rates that were too low for statistical reliability. EnergiesEnergies2020 2020,,13 13,, 5135 5135 77 ofof 2425 Energies 2020, 13, 5135 7 of 25

FigureFigure 6.6. Data-acquisition raterate ofof thethe LiDARLiDAR atat thethe mastmast positionposition overover time.time. Figure 6. Data-acquisition rate of the LiDAR at the mast position over time. FigureFigure7 7 shows shows a a comparison comparison of of horizontal horizontal wind wind speed speed measurements measurements obtained obtained withwith thethe cup cup Figure 7 shows a comparison of horizontal wind speed measurements obtained with the cup anemometer,anemometer, thethe LiDARLiDAR andand thethe ultrasonicultrasonic anemometer.anemometer. DespiteDespite the high complexity of the terrain, anemometer, the LiDAR and the ultrasonic anemometer. Despite the high complexity of the terrain, thethe correlationcorrelation betweenbetween thethe windwind speedspeed measuredmeasured byby thethe LiDARLiDAR andand thethe cup anemometer isis relatively the correlation between the wind speed measured2 by the LiDAR and the cup anemometer is relatively good,good, with a coeffici coefficientent of of determination determination RR2 ofof 0.989, 0.989, which which was was comparable comparable to or to better or better than, than, the good, with a coefficient of determination R2 of 0.989, which was comparable to or better than, the thecorrelation correlation obtained obtained between between the the ultrasonic ultrasonic and and cup cup anemometers, anemometers, both both of of which which provide pointpoint correlation obtained between the ultrasonic and cup anemometers, both of which provide point measurements.measurements. However, However, these these differences differences may may be bedue due to differences to differences in the in LiDAR the LiDAR equipment equipment used, measurements. However, these differences may be due to differences in the LiDAR equipment used, used,terrain terrain conditions, conditions, and/or and surrounding/or surrounding conditions, conditions, compared compared to previous to previous measurements measurements [4]. The [4]. terrain conditions, and/or surrounding conditions, compared to previous measurements [4]. The TheLiDAR LiDAR slightly slightly underestimates underestimates the wind the windspeed speedcompared compared with the with cup theanemometer, cup anemometer, with a gradient with a LiDAR slightly underestimates the wind speed compared with the cup anemometer, with a gradient gradientof 0.956 and of 0.956 it is anddifficult it is ditoffi specifycult to the specify reason the fo reasonr underestimation for underestimation by means by meansof dataof acquired data acquired in this of 0.956 and it is difficult to specify the reason for underestimation by means of data acquired in this instudy, this study,while whileit is predicted it is predicted that thatthe thehorizontal horizontal separation separation distance distance between between the the LiDAR LiDAR and thethe study, while it is predicted that the horizontal separation distance between the LiDAR and the meteorologicalmeteorological mastmast (5(5 m)m) andand thethe didifferencefference betweenbetween volumevolume averageaverage measurementmeasurement byby thethe LiDARLiDAR meteorological mast (5 m) and the difference between volume average measurement by the LiDAR andand pointpoint measurementmeasurement byby thethe cupcup anemometeranemometer aaffectffect thethe di difference.fference. and point measurement by the cup anemometer affect the difference.

(a) (b) (a) (b) FigureFigure 7.7. ComparisonsComparisons of of horizontal horizontal wind wind speed speed measured measured by by the the LiDAR, LiDAR, cup cup anemometer anemometer (CUP) (CUP) and Figure 7. Comparisons of horizontal wind speed measured by the LiDAR, cup anemometer (CUP) ultrasonicand ultrasonic anemometer anemometer (sonic) (son atic) the at mast the mast position. position. (a) sonic (a) sonic and CUPand CUP measurements; measurements; (b) LiDAR (b) LiDAR and and ultrasonic anemometer (sonic) at the mast position. (a) sonic and CUP measurements; (b) LiDAR CUPand CUP measurements. measurements. Measurement Measurement height height (h): h (h=):56 h = m 56 (CUP), m (CUP),h = 53h =m 53 (sonic), m (sonic),h = h54 = m54 (LiDAR).m (LiDAR). and CUP measurements. Measurement height (h): h = 56 m (CUP), h = 53 m (sonic), h = 54 m (LiDAR). Energies 2020, 13, 5135 88 of of 25 24

A comparison of the vertical wind speed and flow inclination angle measured with the LiDAR and theA comparison ultrasonic anemometer of the vertical are wind shown speed in Figures and flow 8 inclinationand 9, respectively. angle measured The slope with of thethe LiDARregression and linethe ultrasonicis 0.899 for anemometer the vertical arewind shown speed, in indicati Figuresng8 and that9, the respectively. LiDAR underestimated The slope of the the regression vertical wind line speedis 0.899 compared for the vertical to the wind ultrasonic speed, indicatinganemometer. that theThis LiDAR disparity underestimated may be due the to vertical the difference wind speed in measurementcompared to the principles; ultrasonic the anemometer. ultrasonic Thisanemom disparityeter employs may be due point to themeasurements, difference in measurementwhereas the LiDARprinciples; measures the ultrasonic the average anemometer wind speed employs value point for a measurements,certain control whereasvolume in the the LiDAR sky. However, measures thisthe averageeffect was wind not speed considered value to for be a certainsignificant control in this volume study, in because the sky. the However, vertical this wind eff ectspeed was was not measuredconsidered directly to be significant using the in LiDAR this study, only becausein the LOS0 the verticaldirection wind (Figure speed 1). was It should measured also directlybe noted using that thethe vertical LiDAR onlywind in speed the LOS0 measured direction with (Figure the ultrason1). It shouldic anemometer also be noted is not that necessarily the vertical correct, wind because speed themeasured measured with vertical the ultrasonic wind speed anemometer is affected is by not th necessarilye anemometer correct, housing because and the arms measured that support vertical thewind sensor. speed As is a anffected example, by the involving anemometer a large housing difference and the in arms the thatobserved support measurements, the sensor. As we an example,focused oninvolving the measurement a large diff dataerence collected in the observedon January measurements, 28, 2018, which we includes focused the on two the measurementsets of plot points data showncollected in onFigure 28 January 9. The 2018, time whichhistory includes of the the10 min two setsaverage of plot horizontal points shown and vertical in Figure wind9. The speeds time measuredhistory of on the the 10 same min averageday are shown horizontal in Figure and vertical10. Although wind the speeds differences measured in horizontal on the same wind day speed are obtainedshown in using Figure the 10 LiDAR. Although and the ultrasonic differences anemomet in horizontaler are wind small, speed the differ obtainedence using in vertical the LiDAR wind speedand the was ultrasonic large from anemometer 13:00 to are15:0 small,0 on thethe disamefference day. in According vertical wind to speedrecords was obtained large from from 13:00 an Automatedto 15:00 on theMeteorological same day. According Data Acquisition to records System obtained (AMeDAS) from an station Automated nearby, Meteorological the precipitation Data duringAcquisition the same System period (AMeDAS) on this day station was nearby, 0.0 mm, the but precipitation the weather during observed the sameon that period day was on thiscloudy day andwas 0.0snowy. mm, Therefore, but the weather it is observedpossible that on that an dayamount was cloudyof snowfall and snowy.that was Therefore, less than it isthe possible AMeDAS that precipitationan amount of detection snowfall thatlimit was (0.5 less mm) than may the AMeDAShave occurred, precipitation and this detection may limithave (0.5affected mm) maythe measurements.have occurred, andImportantly, this may havethe data-acquisition affected the measurements. rate of the LiDAR Importantly, over the data-acquisitionsame time period rate was of 100%,the LiDAR and there over thewas same no influence time period of statistical was 100%, error and theredue to was a low no influencedata-acquisition of statistical rate. errorThe 10 due min to averagea low data-acquisition horizontal wind rate. speed The over 10 min the averagesame time horizontal period ranges wind speed from over1.35 theto 3.42 same m/s, time which period is negligibleranges from for 1.35 the to design 3.42 m and/s, which operation is negligible of wind for turbines the design on wind and operationfarms. Consequently, of wind turbines as shown on wind in Figurefarms. Consequently,11, we only compared as shown the in Figureflow inclination 11, we only angle compared measured the flow by the inclination LiDAR and angle the measured ultrasonic by anemometerthe LiDAR and for thethe ultrasonicdatasets where anemometer the horizontal for the wi datasetsnd speed where was at the least horizontal 4 m/s. Using wind this speed minimum was at horizontalleast 4 m/s. wind Using speed this minimumof 4 m/s, which horizontal corresponds wind speed to the of turbine 4 m/s, cut-in which wind corresponds speed, the to thecorrelation turbine improved,cut-in wind as speed, shown the by correlation the slope of improved, the regression as shown line by of the0.997 slope and of the the coefficient regression of line determination of 0.997 and 2 (theR2) coeof 0.851.fficient of determination (R ) of 0.851.

Figure 8. ComparisonComparison of of vertical vertical wind wind speed speed measured measured by by the the LiDAR LiDAR and and the the ul ultrasonictrasonic anemometer anemometer (sonic) at the mast position. Measurement Measurement height ( h): hh == 5353 m m (sonic), (sonic), hh == 5454 m m (LiDAR). (LiDAR). Energies 2020, 13, 5135 9 of 24

Energies 2020, 13, 5135 9 of 25 Energies 2020, 13, 5135 9 of 25

Figure 9.FigureFigureComparison 9.9. ComparisonComparison of ﬂow ofof inclination flowflow inclinationinclination angle measuredangleangle memeasuredasured by the byby LiDAR thethe LiDARLiDAR and theandand ultrasonic thethe ultrasonicultrasonic anemometer (sonic) atanemometeranemometer the mast position. (sonic)(sonic) atat thethe Measurement mastmast position.position. Measurement heightMeasurement (h): h heightheight= 53 ( m(hh):): (sonic), hh == 5353 mm (sonic),h(sonic),= 54 hh m == 54 (LiDAR).54 mm (LiDAR).(LiDAR).

((aa)) ((bb)) Figure 10. Time history of 10 min average horizontal and vertical wind speed measured by the LiDAR Figure 10.FigureTime 10. history Time history of 10 of min 10 min average average horizontal horizontal an andd vertical vertical wind wind speed speed measured measured by the LiDAR by the LiDAR andand ultrasonicultrasonic anemometeranemometer (son(sonic)ic) atat thethe mastmast position.position. ((aa)) HorizontalHorizontal windwind speed;speed; ((bb)) verticalvertical windwind and ultrasonic anemometer (sonic) at the mast position. (a) Horizontal wind speed; (b) vertical wind speed.speed. MeasurementMeasurement heightheight ((hh):): hh == 5656 mm (CUP),(CUP), hh == 5353 mm (sonic),(sonic), hh == 5454 mm (LiDAR).(LiDAR). speed.Energies Measurement 2020, 13, 5135 height (h): h = 56 m (CUP), h = 53 m (sonic), h = 54 m (LiDAR). 10 of 25

Figure 11.FigureComparison 11. Comparison of ﬂow of inclination flow inclination angle measuredangle measured by the by LiDAR the LiDAR and theand ultrasonic the ultrasonic anemometer anemometer (sonic) at the mast position. Measurement height (h): h = 53 m (sonic), h = 54 m (LiDAR). (sonic) at the mast position. Measurement height (h): h = 53 m (sonic), h = 54 m (LiDAR). Horizontal Horizontal wind speeds below 4 m/s omitted. wind speeds below 4 m/s omitted. Figure 12 shows the results of the turbulence intensity measurements obtained by the LiDAR, the ultrasonic anemometer and the cup anemometer. Turbulence intensity (I) is calculated by 𝜎 𝐼 , 𝑉 (1)

1 𝜎 𝑉 𝑉 , (2) 𝑁

1 1 𝑉 𝑉𝑡𝑑𝑡 𝑉 , (3) 𝑇 𝑁

where 𝑉 is the 10 min average horizontal wind speed, V(t) and Vi are the instantaneous horizontal velocity in the continuous and discrete form, T is the average time period (10 min in this study), and NT is the total number of data in the 10 min time period and σ is the turbulence standard deviation of the horizontal wind speed. The turbulence intensity results measured with the cup anemometer (90% quantiles, Figure 12d) at wind speeds ranging from 3 to 15 m/s indicate intensities corresponding to turbulence category A (Iref = 0.16; Iref is the expected turbulence intensity at the hub height with a 10 min average wind speed of 15 m/s) or even higher intensities exceeding turbulence category A+ (Iref = 0.18), according to the IEC 61400-1 Ed. 4 (2019) [19]. These high turbulence intensities indicate that the measurement site is located in highly complex terrains. The turbulence intensities measured with the LiDAR are typically greater than those measured with the cup anemometer and less than those measured with the ultrasonic anemometer. A previous study [5], which also used a pulsed LiDAR (Leosphere Windcube, Vaisala Oyj) system, found that the LiDAR tended to slightly overestimate turbulence intensities compared with a cup anemometer. Further, when the laser beam emission angle of the Windcube was changed from the standard 30° to 15° (complex terrain mode), in addition to using a continuous-wave LiDAR (ZX Lidars (formerly ZephIR Lidar), the turbulence intensities of both LiDARs were underestimated when compared to the cup anemometer [5]. Nonetheless, in the present study, the difference in the 90% quantiles of the turbulence intensities estimated using the LiDAR and the cup anemometer (Figure 12d) were less than the width of the turbulence categories in the IEC 61400-1 Ed. 4 (2019) [19]. In addition, the LiDAR measurements in this study were on the conservative side, because the LiDAR tended to slightly overestimate the Energies 2020, 13, 5135 10 of 24

Figure 12 shows the results of the turbulence intensity measurements obtained by the LiDAR, the ultrasonic anemometer and the cup anemometer. Turbulence intensity (I) is calculated by σ I = , (1) V uv tu Nt+T 1 X 2 σ = Vi V , (2) NT − i=Nt

Z + Nt+T 1 t T 1 X V = V(t)dt = Vi, (3) T t NT i=Nt where V is the 10 min average horizontal wind speed, V(t) and Vi are the instantaneous horizontal velocity in the continuous and discrete form, T is the average time period (10 min in this study), and NT is the total number of data in the 10 min time period and σ is the turbulence standard deviation of the horizontal wind speed. The turbulence intensity results measured with the cup anemometer (90% quantiles, Figure 12d) at wind speeds ranging from 3 to 15 m/s indicate intensities corresponding to turbulence category A (Iref = 0.16; Iref is the expected turbulence intensity at the hub height with a 10 min average wind speed of 15 m/s) or even higher intensities exceeding turbulence category + A (Iref = 0.18), according to the IEC 61400-1 Ed. 4 (2019) [19]. These high turbulence intensities indicate that the measurement site is located in highly complex terrains. The turbulence intensities measured with the LiDAR are typically greater than those measured with the cup anemometer and less than those measured with the ultrasonic anemometer. A previous study [5], which also used a pulsed LiDAR (Leosphere Windcube, Vaisala Oyj) system, found that the LiDAR tended to slightly overestimate turbulence intensities compared with a cup anemometer. Further, when the laser beam emission angle of the Windcube was changed from the standard 30◦ to 15◦ (complex terrain mode), in addition to using a continuous-wave LiDAR (ZX Lidars (formerly ZephIR Lidar), the turbulence intensities of both LiDARs were underestimated when compared to the cup anemometer [5]. Nonetheless, in the present study, the difference in the 90% quantiles of the turbulence intensities estimated using the LiDAR and the cup anemometer (Figure 12d) were less than the width of the turbulence categories in the IEC 61400-1 Ed. 4 (2019) [19]. In addition, the LiDAR measurements in this study were on the conservative side, because the LiDAR tended to slightly overestimate the turbulence intensity compared to the cup anemometer. These results suggest that the LiDAR can be used to evaluate turbulence intensity, to some extent, even in complex terrains. In addition, the results of the present study show that the LiDAR measurements of wind characteristics generally show good agreement with those measured using a conventional cup anemometer and ultrasonic anemometer. In the measurement conditions encountered in this study, we believe that the vertical-profiling LiDAR is capable of sufficiently evaluating wind characteristics. However, it should be noted that the obtained results are specific to the current measurement conditions (such as the positioning of the LiDAR in complex terrains and the type of LiDAR equipment used). In order to draw universal conclusions, it will be necessary to undertake more measurements and demonstration cases in areas with complex terrains.

3.2. Measurements on the Wind Farm

3.2.1. Outline Figure 13 shows the changes in the data-acquisition rate in LiDAR measurements over time at the position between turbines for representative heights of 50 m (minimum measurement height), 78 m (turbine hub height), 118 m (maximum turbine rotor height) and 126 m (maximum measurement height) AGL. As in the case of the measurements at the mast position (Figure6), the data-acquisition Energies 2020, 13, 5135 11 of 24 rate is highest closer to the ground. However, here there were cases in which the data-acquisition rate Energies 2020, 13, 5135 11 of 25 was extremely low, i.e., 50% or less, for all heights. In October 2017, the measurement site experienced turbulenceheavy rain intensity as Typhoons compared No. 21 to (Lan) the cup and anemomet No. 22 (Saola)er. These struck results Japan. suggest Presumably, that the this LiDAR heavy can rain be usedoverwhelmed to evaluate the turbulence capability intensity, of the wiper to some on the extent, LiDAR even measurement in complex window,terrains. leaving the window obscured by raindrops and resulting in a marked reduction in the data-acquisition rate.

(a) (b)

Figure 12. TurbulenceTurbulence intensities measured using the LiDAR, the cup anemometer (CUP) and the ultrasonic anemometer (sonic) at the mast position. ( (aa)) LiDAR; LiDAR; ( (bb)) sonic; sonic; ( (cc)) CUP; CUP; ( (dd)) 90% 90% quantiles. quantiles. Measurement height (h): h = 5656 m m (CUP), (CUP), hh == 5353 m m (sonic), (sonic), hh == 5454 m m (LiDAR). (LiDAR).

3.2.2.In Influence addition, of the Turbine results Blade of Interferencethe present onstudy LiDAR show Measurements that the LiDAR measurements of wind characteristicsLaser beams generally emitted byshow the LiDARgood agreement may be blocked with by those the turbine measured blades, using resulting a conventional in measurement cup anemometerfailure and degradation and ultrasonic in the anemometer. data-acquisition In the rate.meas Moreover,urement conditions there was encountered a concern that in this the spatialstudy, weuniformity believe that in the the wind vertical-profili speed, whichng isLiDAR assumed is capable to be uniform of sufficiently by the LiDAR, evaluating may wind not were characteristics. maintained However,due to wakes it should on the downstreambe noted that side the of obtained the turbine results and theare stagnationspecific to etheffect current on the upstreammeasurement side conditionsof the turbine. (such Together, as the thesepositioning factors of may the have LiDAR adversely in complex affected terrains the accuracy and the of type the wind of LiDAR speed equipmentestimates of used). the LiDAR. In order To to address draw theseuniversal issues, conclu thesions, study examinedit will be necessary whether theto undertake data-acquisition more measurements and demonstration cases in areas with complex terrains.

Energies 2020, 13, 5135 12 of 25

3.2. Measurements on the Wind Farm

3.2.1. Outline Figure 13 shows the changes in the data-acquisition rate in LiDAR measurements over time at the position between turbines for representative heights of 50 m (minimum measurement height), 78 Energiesm2020 (turbine, 13, 5135 hub height), 118 m (maximum turbine rotor height) and 126 m (maximum measurement12 of 24 height) AGL. As in the case of the measurements at the mast position (Figure 6), the data-acquisition rate is highest closer to the ground. However, here there were cases in which the data-acquisition rate andrate thewas wind extremely speed low, measurement i.e., 50% or accuracyless, for al werel heights. adversely In October aﬀected 201 by7, the interference measurement between site the LiDARexperienced and the wind heavy turbine rain as blades.Typhoons Speciﬁcally, No. 21 (Lan) a detailedand No. 22 analysis (Saola) of struck the LOS Japan. wind Presumably, speeds measured this by theheavy LiDAR rain was overwhelmed performed. the capability of the wiper on the LiDAR measurement window, leaving the window obscured by raindrops and resulting in a marked reduction in the data-acquisition rate.

FigureFigure 13. 13.Data-acquisition Data-acquisition rate rate ofof the LiDAR at at the the position position between between turbines. turbines.

Figure3.2.2. Influence 14 shows of Turbine the results Blade of In aterference geometric on analysis LiDAR Measurements of the possibility of interference between the LOS fromLaser the LiDARbeams andemitted the by wind the turbineLiDAR bladesmay be for bl eachocked of by the the 16 turbine wind directionblades, resulting sectors. in Figure assumesmeasurement that each failure turbine and faces degradation the wind in directionthe data-acquisition ( 11.25◦). rate. The Moreover, results of there the geometricwas a concern analysis ± indicatethat thatthe spatial the blades uniformity may havein the interfered wind speed, with which the is LiDAR’s assumed LOSto be inuniform the wind by the direction LiDAR, sectorsmay of

117◦ Energiestonot 148 were ◦2020and maintained, 13, 297 5135◦ to 328due◦ to. wakes on the downstream side of the turbine and the stagnation13 effect of 25 on the upstream side of the turbine. Together, these factors may have adversely affected the accuracy of the wind speed estimates of the LiDAR. To address these issues, the study examined whether the data-acquisition rate and the wind speed measurement accuracy were adversely affected by interference between the LiDAR and the wind turbine blades. Specifically, a detailed analysis of the LOS wind speeds measured by the LiDAR was performed. Figure 14 shows the results of a geometric analysis of the possibility of interference between the LOS from the LiDAR and the wind turbine blades for each of the 16 wind direction sectors. Figure assumes that each turbine faces the wind direction (±11.25°). The results of the geometric analysis indicate that the blades may have interfered with the LiDAR’s LOS in the wind direction sectors of 117° to 148° and 297° to 328°.

FigureFigure 14. Interference 14. Interference of of LOS LOS with with wind wind turbineturbine blades blades every every sixteen sixteen wind wind directions. directions. Wind Wind turbines turbines are assumedare assumed to face to face the the wind wind direction direction ( (±11.25°).11.25 ). ± ◦ Furthermore, in and around these wind direction sectors, even if the turbine blades do not directly interfere with laser beams, they may pass near enough to the LOS to induce extreme changes in vertical wind speeds around the control volume. To investigate this possibility, Figure 15 compares a case in which the instantaneous vertical wind speed is directly measured in the LOS0 direction (Figure 1), with cases where it is calculated from LOS1 and LOS3 and from LOS2 and LOS4. The vertical wind speeds calculated from LOS1 and LOS3 and from LOS2 and LOS4, do not coincide exactly with the vertical wind speed measured directly from LOS0; also, the data show some variation, which may be caused by deviation in the horizontal position for evaluating vertical wind speed. Moreover, because the pulsed LiDAR used in this study emits laser beams intermittently by switching between the five directions (LOS0 to LOS4) every 0.4 sec, there is a sampling time lag of about 1 to 2 s between the directly measured and estimated vertical wind speeds; this time lag may also be a cause of the variation found in Figure 15. In any case, the calculated values of vertical wind speed in Figure 15 show no excessive up-flow or down-flow which would be suggestive of blade interference. Therefore, we consider that the turbine blades do not have a significant adverse effect on the LiDAR beams. Energies 2020, 13, 5135 13 of 24

Furthermore, in and around these wind direction sectors, even if the turbine blades do not directly interfere with laser beams, they may pass near enough to the LOS to induce extreme changes in vertical wind speeds around the control volume. To investigate this possibility, Figure 15 compares a case in which the instantaneous vertical wind speed is directly measured in the LOS0 direction (Figure1), with cases where it is calculated from LOS1 and LOS3 and from LOS2 and LOS4. The vertical wind speeds calculated from LOS1 and LOS3 and from LOS2 and LOS4, do not coincide exactly with the vertical wind speed measured directly from LOS0; also, the data show some variation, which may be caused by deviation in the horizontal position for evaluating vertical wind speed. Moreover, because the pulsed LiDAR used in this study emits laser beams intermittently by switching between the five directions (LOS0 to LOS4) every 0.4 sec, there is a sampling time lag of about 1 to 2 s between the directly measured and estimated vertical wind speeds; this time lag may also be a cause of the variation found in Figure 15. In any case, the calculated values of vertical wind speed in Figure 15 show no excessive up-flow or down-flow which would be suggestive of blade interference. Therefore, we consider that the turbine blades do not have a significant adverse effect on the LiDAR beams. Energies 2020, 13, 5135 14 of 25

(a) (b)

FigureFigure 15. 15.Comparison Comparison between between directly directly measured measured values values and and estimated estimated values values of instantaneousof instantaneous verticalvertical wind wind speeds speeds at theat the position position between between turbines. turbines. (a) ( Directlya) Directly measured measured values values from from LOS0 LOS0 and and estimatedestimated values values from from LOS1 LOS1 and and LOS3; LOS3; (b) ( directlyb) directly measured measured values values from from LOS0 LOS0 and and estimated estimated values values fromfrom LOS2 LOS2 and and LOS4. LOS4. Measurement Measurement height height (h): (hh):= h78 = 78 m. m.

AsAs shown shown in Figurein Figure 16, 16, the the horizontal horizontal wind wind speed speed within within the planethe plane formed formed by LOS1 by LOS1 and LOS2and LOS2 is denotedis denoted as 12 asu. 12 Thoseu. Those within within the otherthe other planes planes are analogouslyare analogously denoted denoted as 23asu 23, 34u,u 34andu and 41u 41. Whenu. When thethe horizontal horizontal wind wind speed speed is uniformis uniform within within the the horizontal horizontal plane plane at theat the height height above above ground ground where where measurementsmeasurements are are taken, taken, 12 u12andu and 34 u34(andu (and 23 u23andu and 41 u41) shouldu) should be be exactly exactly the the same. same. When When there there is ais a significantsignificant di ffdifferenceerence in eitherin either comparison, comparison, the the assumption assumption of of spatial spatial uniformity uniformity in in the the wind wind in in the the LiDARLiDAR measurement measurement plane plane does does not hold.not hold. The The difference difference may bemay explained be explained by the by eff theects effects of wakes of wakes and stagnationand stagnation caused caused by the turbines,by the turb as wellines, asas airflowwell asdistortion airflow distortion due to the due complexity to the complexity of the terrain. of the Figureterrain. 17 showsFigure the17 nonuniformityshows the nonuniformity in the wind in speed the wind within speed the horizontal within the planes, horizontal as evaluated planes, as throughevaluated a comparison through a of comparison 12u and 34u of(10 12 minu and averages 34u (10 in min each averages case). For in comparison, each case). measurementsFor comparison, obtainedmeasurements on the relatively obtained flat on terrain the relatively at the demonstration flat terrain at field the atdemonstration the Fukushima field Renewable at the Fukushima Energy InstituteRenewable of the Energy National Institute Institute of the of National Advanced Institute Industrial of Advanced Science and Industrial Technology Science (FREA) and Technology are also shown.(FREA) The are measurement also shown. method The measurement and terrain conditions method and are similarterrain toconditions those obtained are similar in a previous to those investigationobtained in bya previous Goit et al. investigation [11]. Comparison by Goit of et the al. 12[11].u–34 Comparisonu relationship of the in the12u–34 mastu relationship position and in at the 2 themast flatter position FREA siteand shows at the similar flatter data FREA variation site (theshows coe ffisimilarcient of data determination variation R(thebeing coefficient 0.993 or of higherdetermination in both cases), R2 being indicating 0.993 thator higher nonuniformity in both cases), in the indicating horizontal that wind nonuniformity speed within in the the horizontal horizontal wind speed within the horizontal plane does not have a prominent effect. The variation at the position between turbines is slightly larger, with a lower R2 value of 0.987 compared with 0.993, but this variation is not marked. However, the data variation at the position between turbines becomes noticeably larger at a measurement height of h = 78 m (turbine hub height), which has an R2 value of 0.968 compared with 0.987, indicating greater nonuniformity in the horizontal wind speed at that height. The same pattern is observed in the comparison between 23u and 41u, which suggests that the turbine rotors have an effect on the nonuniformity in the horizontal wind speed. Energies 2020, 13, 5135 14 of 24 plane does not have a prominent effect. The variation at the position between turbines is slightly larger, with a lower R2 value of 0.987 compared with 0.993, but this variation is not marked. However, the data variation at the position between turbines becomes noticeably larger at a measurement height of h = 78 m (turbine hub height), which has an R2 value of 0.968 compared with 0.987, indicating greater nonuniformity in the horizontal wind speed at that height. The same pattern is observed in the comparison between 23u and 41u, which suggests that the turbine rotors have an effect on the nonuniformityEnergies 2020, 13, 5135 in the horizontal wind speed. 15 of 25

Energies 2020, 13, 5135 15 of 25

Figure 16. Definition of 12u, 23u, 34u and 41u. FigureFigure 16.16. DeﬁnitionDefinition ofof 1212uu,, 2323uu,, 3434uu andand 4141uu..

(a) (b)

Figure 17. Cont. (a) (b) Energies 2020, 13, 5135 15 of 24 Energies 2020, 13, 5135 16 of 25

FigureFigure 17. Comparison 17. Comparison between between 12 12uuand and 34 34uu.(. (a) Position between between turbines. turbines. Measurement Measurement height height (h): (h): h = 78h m;= 78 ( bm;) mast(b) mast position. position.h =h =54 54 m; m; ( c(c)) Position Position between turbines. turbines. h =h 54= 54m; m;(d) (exampled) example of result of result at at moderatelymoderately ﬂat terrainflat terrain (FREA, (FREA,h = h =54 54 m). m).

AlthoughAlthough the ditheﬀerence difference between between 12u and 12u 34 uand(and 34 betweenu (and between 23u and 1423uu) indicatesand 14u) nonuniformityindicates nonuniformity in the horizontal wind speed, the magnitude of the difference cannot be used as a in the horizontal wind speed, the magnitude of the diﬀerence cannot be used as a universal index, as it universal index, as it depends on the wind direction. For a more universal index of the spatial dependsuniformity on the in wind wind direction. speed that For is independent a more universal of the indexwind direction, of the spatial we consider uniformity that inthe wind vector speed that iscomprised independent of (12u of–34 theu) windand (41 direction,u–23u) represents we consider the nonuniformity that the vector of wind comprised speed within of (12 au –34givenu) and (41u–23horizontalu) represents plane. This the nonuniformityvector, referred to of as wind the nonuniformity speed within wind a given velocity horizontal vector plane.(UNU), is This given vector, referredby: to as the nonuniformity wind velocity vector (UNU), is given by:

|𝑼| q12𝑢 34𝑢 23𝑢 41𝑢 , (4) 2 2 UNU = (12u 34u) + (23u 41u) , (4) where, |𝑼| indicates the |magnitude| of spatial− nonuniformity− in the wind speed (hereinafter, SNWS) within the horizontal plane. Figure 18 and 19 shows the quantity obtained by normalizing where, UNU indicates the magnitude of spatial nonuniformity in the wind speed (hereinafter, SNWS) |𝑼|| by| the horizontal wind speed at the corresponding time (hereinafter referred to as "normalized within the horizontal plane. Figures 18 and 19 shows the quantity obtained by normalizing UNU by |𝑼|"), plotted against the horizontal wind speed and wind direction, respectively. The higher| the | the horizontal wind speed at the corresponding time (hereinafter referred to as “normalized UNU ”), wind speed, the lower the normalized |𝑼| and thus the lower the effect of nonuniformity, which| | plottedleads against to a smaller the horizontal error in the windwind speed speed evaluated and wind with direction, the assumption respectively. of spatial The uniformity higher in the the wind speed,wind the speed lower in the vertical-profiling normalized U LiNUDARand measurements thus the lower (Figures the e18).ffect In of comparing nonuniformity, the three which positions leads to | | a smaller(the position error in between the wind turbines, speed the evaluated mast position with an thed the assumption FREA site), of the spatial SNWS uniformityis slightly greater in the at wind speedthe in mast vertical-profiling position and the LiDAR position measurements between turbines (Figure than 18at ).the In FREA comparing site, which the is three on the positions flattest (the positionterrains. between turbines, the mast position and the FREA site), the SNWS is slightly greater at the mast position and the position between turbines than at the FREA site, which is on the flattest terrains. For different wind directions, the variation in the bin average of normalized UNU at the FREA | | site and the mast position is generally small, whereas the bin average at the position between turbines is generally large. This may be partly due to the increased SNWS due to the presence of the turbines (Figure 19a,b). While the dependence of the SNWS on the wind direction is small at the FREA site (the wind direction bin average of normalized UNU is about 0.1), there are instances of large SNWS | | depending on the wind direction at the turbine position and the mast position. This may be due to the complexity of the terrain on the upstream side of the measurement location or the presence of the turbines. To eliminate as much as possible of the potential effect of differences in the wind speed range in Figure 19a,b, UNU values obtained for different wind speed ranges (4–8 m/s, 8–12 m/s and 12 m/s or | | more) are plotted against the wind direction in Figure 20. The normalized UNU values, which tend to | | increase slightly with the wind speed, are generally on the order of 0.5 m/s at both the mast position (denoted by squares) and the FREA site (denoted by triangles). At the position between turbines, Energies 2020, 13, 5135 16 of 24

the SNWS is generally greater, with normalized UNU values of around 1.0 (m/s); this may reﬂect the | | presence of the turbines (wakes or stagnation on the upstream side of the turbine rotor). EnergiesEnergies 20202020,, 1313,, 51355135 1717 ofof 2525

((aa)) ((bb))

((cc))

FigureFigureFigure 18. Magnitude 18.18. MagnitudeMagnitude of ofof the thethe nonuniformity nonuniformitynonuniformity windwind velocityvelocity vectorvector vector plottedplotted plotted againstagainst against thethe horizontal thehorizontal horizontal windwind wind speed.speed.speed. Measurement MeasurementMeasurement height heightheight (h ():(hh):):h hh= == 54 5454 m.m.m. ( (aa)) PositionPosition betweenbetween between turbines;turbines; turbines; ((bb)) (mastmastb) mast position;position; position; ((cc)) FREAFREA (c) FREA (ﬂat terrain).(flat(flat terrain).terrain).

((aa)) ((bb))

Figure 19. Cont. Energies 2020, 13, 5135 18 of 25

Energies 2020, 13, 5135 17 of 24 (c) Energies 2020, 13, 5135 18 of 25 Figure 19. Magnitude of the nonuniformity wind velocity vector plotted against the wind direction. Measurement height: h = 54 m. (a) Position between turbines; (b) mast position; (c) FREA (flat terrain).

For different wind directions, the variation in the bin average of normalized |𝑼| at the FREA site and the mast position is generally small, whereas the bin average at the position between turbines is generally large. This may be partly due to the increased SNWS due to the presence of the turbines (Figures 19a–19b). While the dependence of the SNWS on the wind direction is small at the FREA site

(the wind direction bin average of normalized |𝑼| is about 0.1), there are instances of large SNWS depending on the wind direction at the turbine position and the mast position. This may be due to the complexity of the terrain on the upstream side of the measurement location or the presence of the turbines. To eliminate as much as possible of the potential effect of differences in the wind speed range in Figures 19a and 19b, |𝑼| values obtained for different wind speed ranges (4–8 m/s, 8–12 m/s and 12 m/s or more) are plotted against the wind direction in Figure 20. The normalized |𝑼| values, which tend to increase slightly with the wind speed, are generally on the order of 0.5 m/s at both the mast position (denoted by squares) and the(c) FREA site (denoted by triangles). At the position betweenFigure turbines,Figure 19. Magnitude 19. Magnitudethe SNWS of theof isthe nonuniformity gene nonuniformityrally greater, windwind velocitywith normalized vector vector plotted plotted |𝑼 against against| values the thewind of wind direction.around direction. 1.0 (m/s); this mayMeasurement reflectMeasurement the height: presence height:h = h54 = of 54 m. them. (a () aturbines Position) Position between between(wakes turbines; turbines;or stagnation (b (b) mast) mast onposition; position; the upstream (c) FREA (c) FREA (flat side (ﬂatterrain). of terrain). the turbine rotor). For different wind directions, the variation in the bin average of normalized |𝑼| at the FREA site and the mast position is generally small, whereas the bin average at the position between turbines is generally large. This may be partly due to the increased SNWS due to the presence of the turbines (Figures 19a–19b). While the dependence of the SNWS on the wind direction is small at the FREA site

range in Figures 19a and 19b, |𝑼| values obtained for different wind speed ranges (4–8 m/s, 8–12 m/s and 12 m/s or more) are plotted against the wind direction in Figure 20. The normalized |𝑼| values, which tend to increase slightly with the wind speed, are generally on the order of 0.5 m/s at both the mast position (denoted by squares) and the FREA site (denoted by triangles). At the position

between turbines, the SNWS is generally greater, with normalized |𝑼| values of around 1.0 (m/s); this may reflect the presence of the turbines (wakes or stagnation on the upstream side of the turbine rotor).

FigureFigure 20.20. Magnitude of the nonuniformitynonuniformity wind velocity vector for didifferentﬀerent windwind speedspeed rangesranges plottedplotted againstagainst thethe windwind direction.direction.

3.2.3. Wind Characteristics at the Measurement Location Figure 21 shows the turbulence intensity at the position between turbines, measured and evaluated by the LiDAR system. The LiDAR wind speed measurement and evaluation principle indicates that the measurement accuracy of turbulence intensity is insufficient, and actual demonstration also indicated insufficient accuracy for quantitative evaluation [4]. It is therefore necessary to limit our discussion here to a relative evaluation. Compared with the turbulence category curve defined in IEC 61400-1 Ed. 4 (2019) [19] and the turbulence intensity at the mast position in Figure 12, the turbulence intensity tends to be large over the entire wind-speed range. This can be attributed not only to topographical factors, but also to the wakes caused by nearby turbines. Figure 20. Magnitude of the nonuniformity wind velocity vector for different wind speed ranges plotted against the wind direction. Energies 2020, 13, 5135 19 of 25

3.2.3. Wind Characteristics at the Measurement Location EnergiesFigure 2020 21, 13shows, 5135 the turbulence intensity at the position between turbines, measured19 of 25 and evaluated by the LiDAR system. The LiDAR wind speed measurement and evaluation principle 3.2.3. Wind Characteristics at the Measurement Location indicates that the measurement accuracy of turbulence intensity is insufficient, and actual demonstrationFigure also21 shows indicated the turbulence insufficient intensity accuracy at thefo r positionquantitative between evaluation turbines, [4].measured It is thereforeand necessaryevaluated to limit by theour LiDAR discussion system. here The to aLiDAR relative wind evaluation. speed measurement Compared withand theevaluation turbulence principle category indicates that the measurement accuracy of turbulence intensity is insufficient, and actual curve defined in IEC 61400-1 Ed. 4 (2019) [19] and the turbulence intensity at the mast position in demonstration also indicated insufficient accuracy for quantitative evaluation [4]. It is therefore Figure 12, the turbulence intensity tends to be large over the entire wind-speed range. This can be necessary to limit our discussion here to a relative evaluation. Compared with the turbulence category attributedcurve definednot only in to IEC topographical 61400-1 Ed. 4factors, (2019) [19]but alsoand theto the turbulence wakes causedintensity by at nearby the mast turbines. position in

FigureEnergies 202012, ,the13, 5135turbulence intensity tends to be large over the entire wind-speed range. This can18 of be 24 attributed not only to topographical factors, but also to the wakes caused by nearby turbines.

Figure 21. Turbulence intensity measured at the position between turbines. Measurement height: h = Figure 21. Turbulence intensity measured at the position between turbines. Measurement height: h = 78 m.Figure 21. Turbulence intensity measured at the position between turbines. Measurement height: h = 7878 m. m.

FiguresFiguresFigures 22 and 2222 and and23 23show 23 show show the the theflow flow flow inclination inclination inclination angle angle andand and turbulence turbulence turbulence intensity, intensity, intensity, respectively, respectively, respectively, for the forfor the 16 wind16the wind 16directions wind directions directions (wind (wind direction (wind direction direction bin bin averages), averages), bin averages), for thethe for average average the average wind wind windspeed speed speed ranges ranges ranges of 4–8 of of 4–8m/s, 4–8 m/s, 8–12 m/s, 8–12 m/s m/sand8–12 and12 m/ s m/s12 and m/s or 12 more.or m/ smore. or more.While While While the the flow theflow flow inclinationinclination inclination angleangle angle is is isabout about about 6° 66° and◦ andand the the the turbulence turbulence turbulence intensity intensity intensity aboutaboutabout 24% 24% 24%in the in in the theprevailing prevailing prevailing north- north- north-northwestnorthwestnorthwest windwind wind direction direction (337.5 (337.5 + 11.25°),+11.25 11.25°),◦), flow flow flow inclination inclination inclination angles angles angles exceedingexceedingexceeding 10° 1010°and◦ and turbulence turbulenceturbulence intensities intensities intensities exceeding exceedinexceeding 30% 30%30% are are are found found found in somein in some some wind wind directions.wind directions. directions. These These data These data datareflectreflect reflect the the influence theinfluence influence of of the ofthe complex the complex complex terrain. terrain. terrain.

Figure 22. Flow inclination angle for 16 wind directions measured at the position between turbines. Measurement height: h = 78 m. FigureFigure 22. Flow 22. Flow inclination inclination angle angle for for 16 16 wind wind directions directions measured at at the the position position between between turbines. turbines. Measurement height: h = 78 m. Measurement height: h = 78 m. Figures 24 and 25 show the ratios of the three components of the turbulence standard deviation σ2/σ1 and σ3/σ1 (referred to as “turbulence structure” in IEC 61400-1 Ed. 4 (2019) [19]), respectively, plotted against the wind direction for the average wind speed range of 4 to less than 8 m/s. Turbulence standard deviations (σi, i = 1, 2, 3. 1: longitudinal direction, 2: lateral direction. 3: upward direction) are standard deviations of the longitudinal, lateral or upward component of the turbulent wind velocity. The values of σ2/σ1 = 1.0 and σ3/σ1 = 0.7 are postulated for sites with high terrain complexity (Category Energies 2020, 13, 5135 20 of 25

Energies 2020, 13, 5135 19 of 24

H) in IEC 61400-1 Ed. 4 (2019) [19]. While the σ2/σ1 is greater than unity for some wind directions, further detailed investigations are required, as the present results may have been affected by various factors such as the wakes caused by nearby turbines. On the other hand, σ3/σ1 is as low as about 0.7 or less, for almost all directions. As the measurement location was situated on a ridge, it is conceivable that the upward turbulence standard deviation σ3 was reduced by the flow contraction effect on theEnergies ridge. 2020, 13, 5135 20 of 25

Figure 23. Turbulence intensity for 16 wind directions measured at the position between turbines.

Figures 24 and 25 show the ratios of the three components of the turbulence standard deviation σ2/σ1 and σ3/σ1 (referred to as “turbulence structure” in IEC 61400-1 Ed. 4 (2019) [19]), respectively, plotted against the wind direction for the average wind speed range of 4 to less than 8 m/s. Turbulence standard deviations (σi, i = 1, 2, 3. 1: longitudinal direction, 2: lateral direction. 3: upward direction) are standard deviations of the longitudinal, lateral or upward component of the turbulent wind velocity. The values of σ2/σ1=1.0 and σ3/σ1=0.7 are postulated for sites with high terrain complexity (Category H) in IEC 61400-1 Ed. 4 (2019) [19]. While the σ2/σ1 is greater than unity for some wind directions, further detailed investigations are required, as the present results may have been affected by various factors such as the wakes caused by nearby turbines. On the other hand, σ3/σ1 is as low as about 0.7 or less, for almost all directions. As the measurement location was situated σ on a ridge,Figure it 23. is Turbulenceconceivable intensity that th fore upward 16 wind directionsturbulence measuredmeasured standard atat thethedeviation positionposition betweenbetween3 was reduced turbines.turbines. by the flow contraction effect on the ridge. Figures 24 and 25 show the ratios of the three components of the turbulence standard deviation σ2/σ1 and σ3/σ1 (referred to as “turbulence structure” in IEC 61400-1 Ed. 4 (2019) [19]), respectively, plotted against the wind direction for the average wind speed range of 4 to less than 8 m/s. Turbulence standard deviations (σi, i = 1, 2, 3. 1: longitudinal direction, 2: lateral direction. 3: upward direction) are standard deviations of the longitudinal, lateral or upward component of the turbulent wind velocity. The values of σ2/σ1=1.0 and σ3/σ1=0.7 are postulated for sites with high terrain complexity (Category H) in IEC 61400-1 Ed. 4 (2019) [19]. While the σ2/σ1 is greater than unity for some wind directions, further detailed investigations are required, as the present results may have been affected by various factors such as the wakes caused by nearby turbines. On the other hand, σ3/σ1 is as low as about 0.7 or less, for almost all directions. As the measurement location was situated on a ridge, it is conceivable that the upward turbulence standard deviation σ3 was reduced by the flow contraction effect on the ridge.

Figure 24. Turbulence standard deviation ratio σ22/σ1 plotted against wind di directionrection (average wind speed: 4–8 4–8 m/s; m/s; measurement location: positi positionon between turbines; measurement height: h = 7878 m). m).

Figure 24. Turbulence standard deviation ratio σ2/σ1 plotted against wind direction (average wind speed: 4–8 m/s; measurement location: position between turbines; measurement height: h = 78 m). Energies 2020, 13, 5135 20 of 24 Energies 2020, 13, 5135 21 of 25

Figure 25. Turbulence standard deviationdeviation ratio σσ33/σ1 plotted against wind directiondirection (average wind speed: 4–8 m/s; m/s; measurement location: positi positionon between turbines; measurement height: h = 7878 m). m).

Figure 2626 shows shows the the wind wind direction direction bin-averaged bin-averag wind-sheared wind-shear profiles profiles for different for winddifferent directions wind anddirections four hub-height and four hub-height 10 min average 10 min wind average speed wi ranges.nd speed Wind ranges. speed Wind is normalized speed is normalized by hub-height by windhub- speedheight inwind Figure speed 26. in No Figure profiles 26. mean No profiles that there mean were that not there enough were 10 not min enough average 10 wind-shear min average samples wind- inshear the samples wind direction in the wind bins fordirection the wind bins speed for the ranges. wind speed While ranges. the diff Whileerence the in wind-sheardifference in profiles wind- betweenshear profiles different between wind different speed ranges wind inspeed each ranges wind direction in each wind (Figure direction 26b–d) (Figure is small, 26b–d) it diff ersis small, among it thediffers diff erentamong wind the directions. different Thewind wind directions. speed is increasedThe wind with speed increasing is increased measurement with increasing height at wind direction 270 11.25 (Figure 26a). Conversely, the wind-shear profiles for 292.5 11.25 , 315 measurement height◦ ± at wind◦ direction 270°±11.25° (Figure 26a). Conversely, the wind-shear◦ ± profiles◦ ◦ 11.25 and 337.5 11.25 (Figure 26b–d) are more vertical and concave-shaped, with a maximum ±for 292.5°±11.25°,◦ 315°±11.25°◦ ± ◦ and 337.5°±11.25° (Figure 26b–d) are more vertical and concave-shaped, windwith a speed maximum deficit wind at approximately speed deficit 10at mapproximatel above the rotory 10 hubm above height. the Althoughrotor hub itheight. is likely Although that these it concave-shapedis likely that these wind-shear concave-shaped profiles wind-shear are caused prof by theiles nearby are caused wind by turbine the nearby wakes wind (Figures turbine2 and wakes 14), it(Figure is also 2 suspectedand Figure that 14), theit is flowalso suspected over the ridgethat th ise acceleratedflow over the near ridge the is terrainaccelerated surface near by the the terrain flow contractionsurface by the eff ectflow on contraction the ridge. effect on the ridge. Figure 27 shows the representative 10 min average wind-veer profiles for the different hub-height 10 min average wind speeds between 8 and 12 m/s in the wind direction bins of 337.5 11.25 (the ◦ ± ◦ dominant wind direction) and 157.5 11.25 (the second most dominant wind direction). Wind ◦ ± ◦ direction is normalized by hub-height wind direction in Figure 27. All of the wind-veer profiles in wind direction bin 337.5 11.25 are similar, independently of wind speed (Figure 27a. In addition, ◦ ± ◦ the wind-veer profiles in wind direction bin 157.5 11.25 change dramatically between 8 and 9 m/s; ◦ ± ◦ further, there is also considerable variation in three wind-veer profiles with a hub-height wind speed of 8 m/s. We suggest that flows from that direction have complex wind characteristics which changed significantly in response to wind speed. These complex wind characteristics cause nonuniformity in space and fluctuation in time of flow over the rotor plane, resulting in an increase in fluctuating wind-loading to the wind turbine rotor blades. This is thus very useful for wind farm operators and wind turbine manufacturers to be able to capture complex wind characteristics on wind farms located in areas with complex terrains using LiDAR. However, due to the considerable uncertainties associated with LiDAR measurements at sites with complex terrains, it is necessary to accumulate more measurements in such areas to draw more universal conclusion.

(a) (b) (c) (d)

Figure 26. Wind direction bin-averaged wind-shear profiles at the position between turbines for different wind directions and different hub-height 10 min average wind speed ranges at the position Energies 2020, 13, 5135 21 of 25

Figure 25. Turbulence standard deviation ratio σ3/σ1 plotted against wind direction (average wind speed: 4–8 m/s; measurement location: position between turbines; measurement height: h = 78 m).

Figure 26 shows the wind direction bin-averaged wind-shear profiles for different wind directions and four hub-height 10 min average wind speed ranges. Wind speed is normalized by hub- height wind speed in Figure 26. No profiles mean that there were not enough 10 min average wind- shear samples in the wind direction bins for the wind speed ranges. While the difference in wind- shear profiles between different wind speed ranges in each wind direction (Figure 26b–d) is small, it differs among the different wind directions. The wind speed is increased with increasing measurement height at wind direction 270°±11.25° (Figure 26a). Conversely, the wind-shear profiles for 292.5°±11.25°, 315°±11.25° and 337.5°±11.25° (Figure 26b–d) are more vertical and concave-shaped, with a maximum wind speed deficit at approximately 10 m above the rotor hub height. Although it is likely that these concave-shaped wind-shear profiles are caused by the nearby wind turbine wakes Energies(Figure2020 2 and, 13, Figure 5135 14), it is also suspected that the flow over the ridge is accelerated near the terrain21 of 24 surface by the flow contraction effect on the ridge.

Energies 2020, 13, 5135 22 of 25

between turbines. (a) Wind direction: 270°±11.25°; (b) wind direction: 292.5°±11.25°; (c) wind direction: 315°±11.25°; (d) wind direction: 337.5°±11.25°.

Figure 27 shows the representative 10 min average wind-veer profiles for the different hub- height 10 min average wind speeds between 8 and 12 m/s in the wind direction bins of 337.5°±11.25° (the dominant wind direction) and 157.5°±11.25° (the second most dominant wind direction). Wind direction is normalized by hub-height wind direction in Figure 27. All of the wind-veer profiles in wind direction bin 337.5°±11.25° are similar, independently of wind speed (Figure 27a. In addition, the wind-veer profiles in wind direction bin 157.5°±11.25° change dramatically between 8 and 9 m/s; further, there is also considerable variation in three wind-veer profiles with a hub-height wind speed of 8 m/s. We suggest that flows from that direction have complex wind characteristics which changed significantly in response to wind speed. These complex wind characteristics cause nonuniformity in space and fluctuation in time of flow over the rotor plane, resulting in an increase in fluctuating wind- loading to the(a wind) turbine rotor blades.(b) This is thus very useful(c) for wind farm operators(d) and wind turbineFigure manufacturers 26.26. Wind direction to be able bin-averaged to capture wi wind-shear complexnd-shear windprofiles profiles characteristics at the position on between wind farms turbines located for in areasdidifferent withfferent complex windwind directionsdirections terrains andusingand didiffe ffLiDAR.erentrent hub-heighthub-height However, 1010 due minmin to averageaverage the considerable windwind speedspeed rangesrangesuncertainties at the position associated with betweenLiDAR turbines.measurements (a) Wind at direction: sites with 270 complex11.25 ;(terrains,b) wind it direction: is necessary 292.5 to11.25 accumulate;(c) wind more ◦ ± ◦ ◦ ± ◦ measurementsdirection: 315 in such11.25 areas;(d to) wind draw direction: more universal 337.5 11.25conclusion.. ◦ ± ◦ ◦ ± ◦

(a) (b)

Figure 27. 27. Wind-veerWind-veer profiles proﬁles for for different diﬀerent hub-height hub-height 10 10 min min average average wind wind speeds speeds between between 8 and 8 and 12 m/s12 m at/s the at theposition position between between turbines. turbines. (a) Wind (a) Winddirection: direction: 337.5° ± 337.5 11.25°; (11.25b) wind;( bdirection:) wind direction: 157.5° ± ◦ ± ◦ 11.25°.157.5 11.25 . ◦ ± ◦ 4. Conclusions With the ultimate goal of elucidating the characteristics of downwind turbines in complex terrains, we measured the wind characteristics on a wind farm using a vertical-profiling LiDAR and obtained the following results. In complex terrains, the correlation between the horizontal wind speed measured with a vertical- profiling LiDAR and with a cup anemometer was as good as the correlation between the horizontal wind speed measured with the ultrasonic anemometer and cup anemometer. The flow inclination angle measured with the vertical-profiling LiDAR agreed well with that measured with the ultrasonic Energies 2020, 13, 5135 22 of 24

4. Conclusions With the ultimate goal of elucidating the characteristics of downwind turbines in complex terrains, we measured the wind characteristics on a wind farm using a vertical-profiling LiDAR and obtained the following results. In complex terrains, the correlation between the horizontal wind speed measured with a vertical-profiling LiDAR and with a cup anemometer was as good as the correlation between the horizontal wind speed measured with the ultrasonic anemometer and cup anemometer. The flow inclination angle measured with the vertical-profiling LiDAR agreed well with that measured with the ultrasonic anemometer, except in the low wind-speed range of less than 4 m/s. These findings indicate that vertical-profiling LiDARs may be moderately useful for evaluating the flow inclination angle. The LiDAR tended to slightly overestimate the turbulence intensity compared with the cup anemometer and underestimate the turbulence intensity compared with the ultrasonic anemometer. The difference between the LiDAR and cup anemometer measurements of turbulence intensity in the wind speed range of 4–16 m/s was less than the width (Iref = 0.02) between the turbulence categories in IEC 61400-1 Ed. 4 (2019) [15] and the LiDAR measurement was on the conservative side because it tended to overestimate the turbulence intensity when compared with the cup anemometer. These results suggest that LiDARs can evaluate turbulence intensity to some extent, even in complex terrains. It should be noted, however, that the obtained results are specific to the measurement conditions of the study (such as the location of the LiDAR in the complex terrain and the type of LiDAR used). To draw universal conclusions, it is necessary to accumulate more measurements in complex terrains. There is some concern regarding the application of LiDAR measurements to wind farms in complex terrains. Specifically, it is considered that the accuracy and reliability of LiDAR-based wind measurements are adversely affected by airflow distortion caused by the complexity of the terrain, turbine wakes, and stagnation effects on the upstream side of the measurement location, and the effect that these factors have on the assumption of spatial uniformity in wind speed. To address these issues, this study examined whether the data-acquisition rate and wind speed measurement accuracy were adversely affected by interference between the LiDAR and the wind turbines by performing a detailed analysis of the LOS wind speeds measured by the LiDAR. Geometric analysis—with the LiDAR located between two turbines separated by only 2D (D: diameter of turbines)—indicated that the turbine rotor may have interfered with the LiDAR LOS. However, the measurement results showed no significant decrease in the data-acquisition rate in specific wind directions that may cause rotor-based interference with the LiDAR LOS, indicating that the effect of interference was minimal. We have also proposed an index for assessing the extent of spatial nonuniformity in the wind speed, based on the LOS wind speeds on a measurement plane above the LiDAR. We found this index to be effective and used it to estimate spatial nonuniformity in the wind speed increased due to the topographic complexity on the upstream side of the measurement location and the turbine wakes. By using the LiDAR to evaluate a variety of wind characteristics on a wind farm in complex terrains, we demonstrated that it is possible to evaluate, to some extent, the wind characteristics that are important for wind turbine design and wind farm operation. These characteristics include the flow inclination angle, turbulence intensity, turbulence standard deviation ratio, wind shear and wind veer, for different wind directions. However, our results should be interpreted with caution because they are affected by site-specific attributes, such as the complexity of the terrain, wakes from nearby turbines and stagnation on the upstream side. Nevertheless, it was demonstrated that LiDAR can be used, to some extent, to measure wind characteristics on a wind farm in complex terrains in a way that considers the effect of wind directions with large spatial nonuniformity index values. To draw universal conclusions, it will be necessary to accumulate additional measurements in complex terrains. Investigation on relationship between the wind characteristics and the downwind turbine characteristics will be reported in our future work. Energies 2020, 13, 5135 23 of 24

Author Contributions: T.K., K.S., S.S. and H.K. developed the idea, carried out the study, prepared the models, analyzed the corresponding results and wrote the paper. Y.O., K.K. and E.F. contributed by drafting the paper and performed critical revisions. All authors organized and refined the manuscript to its present form. All authors have read and agreed to the published version of the manuscript. Funding: This study was conducted as part of a collaborative project between Hitachi, Ltd. and AIST entitled, “Research on the up-flow wind flowing into downwind turbines and the power output characteristics of turbines.” Acknowledgments: Our LiDAR measurements were supported by the wind farm operator. We would like to express our gratitude to all concerned. Conflicts of Interest: The authors declare no conflict of interest.

Abbreviations

AGL Above ground level CFD Computational ﬂuid dynamics LiDAR Light detection and ranging LOS Line-of-sight SNR Signal-to-noise ratio SNWS Spatial nonuniformity in the wind speed

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