GALION LIDAR PERFORMANCE VERIFICATION Technical report

Transcription

1 F RAUNHOF ER INSTITUTE FOR WIND ENERGY AND ENERGY SYSTEM TECHNOLOGY GALION LIDAR PERFORMANCE VERIFICATION Technical report

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3 GALION LIDAR PERFORMANCE VERIFICATION Technical report Dr. Julia Gottschall Fraunhofer Institute for Wind Energy and Energy System Technology (IWES), Bremerhaven 29 May 2013 (final version)

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5 Contents Executive Summary Introduction Scope of work Review of instrumentation and datasets Lidar performance verification Lidar error Information supplied by SgurrEnergy Wind data Contexts and concepts Results Review of instrumentation and datasets Suitability of the mast for use as reference Extent to which the measurements made by the mast and the Galion are comparable Prevailing wind conditions Lidar performance verification Data preparation Data filtering Results for performance verification Lidar error Discussion Conclusions References Annex A Reference mast equipment layout Annex B Acceptance criteria B.1 SgurrEnergy in-house site acceptance criteria Fraunhofer IWES Galion Lidar Performance Verification 5 37

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7 Executive Summary Setup of performance verification test and data preparation The measurement performance of a Galion Lidar (Galion G4000 Offshore, Unit ) was analyzed in a performance verification test. For this, wind speed and direction data acquired using a Galion Lidar were compared with concurrent data acquired using conventional mast mounted cup anemometry and wind sensors. The Galion Lidar under test was measuring with an arc scan technique, a configuration also referred to as remote mast since the Lidar measures the wind vector at several heights not at the location of the device (as e.g. a Lidar scanning along a vertical cone above the instrument) but in a certain horizontal distance. The measurement campaign took place in moderately complex terrain, at a location close to Myres Hill, Scotland. The setup of the Galion Lidar and the reference mast was assessed and considered as appropriate with respect to the suitability of the mast for use as a reference (complying with relevant standards), and the extent to which the measurements made by the mast and the Galion Lidar are comparable. Furthemore, the prevailing wind conditions were checked and documented. The actual data analysis for the performance verification was performed for 10-minaverage Galion Lidar and reference data. The concurrent time period for both datasets is from 12/09/2012 (15:27:01) to 18/10/2012 (12:28:05), corresponding to 35 complete days and 5404 complete 10-min-data periods or samples, respectively. The following data filters were defined and applied to the data: a minimum number of rays accepted for the Galion Lidar raw data record, a filter based on a threshold for the Galion Lidar turbulence measure, number of records per 10-min averaging interval for the Galion Lidar, wind direction limits (the valid wind sector was pre-defined as [240, 330 ]), and an optional filter with respect to the considered wind speed range. Applying the first four filters reduced the dataset to min-data samples. Results Linear regression of 10-min average data 1-parameter and 2-parameter OLS linear regressions of Galion 10-min average horizontal wind speeds (GHWS) on concurrent and collocated reference mast 10-min average horizontal wind speeds (RHWS) were performed for all concurrent data. The models for the fit were defined as... 1-parameter linear regression model: 2-parameter linear regression model: where x refers to RHWS and y to GHWS. y = mx y = kx+c The following coeffecients were derived: R2-fit: coefficient of determination (for 1- and 2-parameter model, resp.), R-P: Pearson correlation coefficient, R-Sp: Spearman rank coefficient. Table 1 shows the respective results (fit parameters and coefficients) for the different wind speed ranges. Scatter plots of the data and best-fit lines are presented in Figure 1. Fraunhofer IWES Galion Lidar Performance Verification 7 37

8 Table 1: Results of linear regression (1- and 2-parameter model) of 10-min average data, and Pearson and Spearman rank correlation coefficients. # samples m [-] R2-fit1 k [-] C [ms -1 ] R2-fit R2-P R2-Sp Figure 1: Scatter plot of GHWS against RHWS 10-min mean values with best-fit lines obtained by 1-parameter (blue) and 2-parameter (red) OLS linear regression. Linear regression of wind speed bin-averaged data OLS linear regressions of wind speed bin-averaged measurements were performed, where the wind speed bins are binned according to 0.5 ms -1 RHWS bins centred on integer multiples of 0.5 ms -1. Suitable confidence intervals were defined as: bin mean of GHWS bin standard deviation of GHWS / sqrt(number of samples in bin). Results are presented in Table 2 (fit parameters and coefficients of determination for 1- and 2-parameter linear regression model) and Figure 2 (plot of bin means and best-fit lines) Fraunhofer IWES Galion Lidar Performance Verification

9 Table 2: Results of linear regression (1- and 2-parameter model) of RHWS-bin averages. # bins m [-] R2-fit1 k [-] C [ms -1 ] R2-fit Figure 2: Scatter plot of GHWS against RHWS bin averages with best-fit lines obtained by 1-parameter (blue) and 2-parameter (red) OLS linear regression. Error bars correspond to suitable confidence intervals. Linear regression of wind direction measurements A 2-parameter OLS linear regression of Galion 10-min average wind direction (GWD) on concurrent and collocated reference mast 10-min average wind direction (RWD) was performed with the model y = kx+c, where x refers to RWD and y to GWD). The results are presented in Table 3 (fit parameters and coefficient of determination) and Figure 3 (scatter plot of wind direction data and best-fit line). Table 3: Results of linear regression (2-parameter model) of 10-min average wind direction data for all concurrent data but reduced dataset. # samples k [-] C [deg] R2-fit F F for GWD < for GWD 180 Fraunhofer IWES Galion Lidar Performance Verification 9 37

10 Figure 3: Scatter plot of GWD against RWD 10-min mean values with best-fit lines obtained by 2-parameter (red and blue) OLS linear regression for two separated datasets. Lidar error An analysis of the Lidar error (defined as RHWS minus GHWS), presented both as an absolute error in ms -1 and as a percentage of the corresponding RHWS (referred to as relative Lidar error), was performed. Results for the mean value and standard deviation of the Lidar error are presented in Table 4. In Figure 4 Lidar error bin averages are shown. Table 4: Statistics of Lidar error. # samples mean [ms -1 ] sd [ms -1 ] mean [%] sd [%] [240, 330 ] Fraunhofer IWES Galion Lidar Performance Verification

11 Figure 4: Scatter plot of Lidar error against RHWS bin averages. Error bars correspond to suitable confidence intervals (here: bin averages standard error); 10-min averages as red dots. Figure 5 shows a comparison of the observed Lidar error with the uncertainties introduced by the reference sensors here for reduced wind speed range (4 ms -1 to 16 ms -1 ). Figure 5: Comparison of the observed Lidar error (red: 10-min average values, black: bin averages) with the uncertainty associated with the reference sensors (blue lines). Fraunhofer IWES Galion Lidar Performance Verification 11 37

12 Conclusions The observed performance of the Galion Lidar in the considered test setup, expressed in the presented verification results, meets the SgurrEnergy in-house criteria (i.e. the OLS regression slope is within 2% of unity, and R 2 > 0.982F1 ), indicating an acceptable performance of the device under test. Given that the measurements were performed in moderately complex (and definitely not perfectly flat) terrain, and that the Galion Lidar was operated in a mode that is not aimed at a best possible agreement with the reference measurements but has further advantages, the results of the verification test indicate a good agreement with the reference mast. The arc scan mode of operation may be recommended for applications where a horizontal distance between the location of the measurement device and its measurements is necessary, e.g. for a power performance assessment offshore with the Galion Lidar installed on the transition piece of the test turbine. It is expected that the measurement uncertainties for such an application are even smaller than for the presented Galion Lidar performance verification since the influences of the terrain would be less significant for an offshore site. 1 taken here as coefficient of determination (R2-fit) Fraunhofer IWES Galion Lidar Performance Verification

13 1 Introduction This report summarizes a verification of wind speed and direction data acquired using a Galion Lidar relative to concurrent data acquired using conventional mast mounted cup anemometry and wind sensors, with the data analysis performed by Fraunhofer IWES. For this, SgurrEnergy had acquired suitable datasets at a number of locations. The Galion Lidar under test (Galion G4000 Offshore, Unit ) was measuring with an arc scan technique (for a detailed definition of the scan geometry see 4.1.2). This configuration is also referred to as remote mast since the Lidar measures the wind vector at several heights not at the location of the device (as e.g. a Lidar scanning along a vertical cone above the instrument) but in a certain horizontal distance. The performance verification documented here has been performed for one dataset and one location. The comparison of the two sets of measurements acquired using conventional wind sensors and Galion Lidar, and the degree to which they agree, may be taken to indicate the level of confidence that may be placed in the Galion data, given a suitably high level of confidence in the mast data being used as a reference, and an understanding of how the two sets of measurements are related in terms of their locations and surroundings. The report is structured as follows: this brief introduction is followed by a description of the scope of work (in section 2) and the supplied information (in section 3), as detailed in the definition in the study. In section 4, the results are presented. The report is completed by a discussion and the conclusions in section 5 and 6, resp. An Executive Summary precedes the report. Fraunhofer IWES Galion Lidar Performance Verification 13 37

14 2 Scope of work The Scope of Work for the Galion Lidar Performance Verification, as defined in [1], requires a comparison to be made between the measurements acquired using a reference instrument and the Galion, and conclusions to be drawn on the basis of this comparison regarding the level of confidence that may be placed in the Galion data. The Scope of Work consists of the following items. 2.1 Review of instrumentation and datasets An assessment of the setup of the Galion Lidar and the reference mast and sensors is to be conducted in terms of the following: 1. The suitability of the mast for use as a reference is to be assessed with regard to its: a. location, b. configuration, c. installation, d. the validity of the calibration of the instruments mounted on it and e. compliance with relevant engineering standards. 2. The extent to which the measurements made by the mast and the Galion are data be expected to of the mast data given the conditions under which the measurements are acquired, taking into account a. the degree of concurrency and co-location of the measurements, i.e. the measurements have i. the same averaging interval, ii. are synchronised, iii. are acquired within the volumes such that the wind conditions influencing both measurements are sufficiently well related, b. the data availability of all instruments are 100% within averaging intervals for which the measurements are compared, or, the influence of data availability lower than 100% but above some other threshold is understood and considered to be negligible, c. the free stream conditions prevailing at the locations of the reference sensors and Galion Lidar measurement volumes where the line of sight data comprising the Lidar measurements are acquired. 3. The prevailing wind conditions in terms of turbulence, shear, veer, flow inclination and terrain induced flow heterogeneity. 2.2 Lidar performance verification A comparison is to be made of the measurements acquired using the Galion and the reference mast for each measurement height at which concurrent data are available, including: 1. 1-parameter and 2-parameter Ordinary Least Squares (OLS) linear regressions of Galion 10-min average horizontal wind speeds (GHWS) on concurrent and collocated reference mast 10-min average horizontal wind speeds (RHWS) giving a. scatter plots of GHWS (vertical axis or ordinate, resp.) against RHWS (horizontal axis or abscissa, resp.) showing best fit lines obtained by 1- parameter and 2-parameter OLS linear regression; Fraunhofer IWES Galion Lidar Performance Verification

15 b. slope parameter (and offset parameter where relevant), and both Pearson correlation and Spearman rank correlation coefficients; c. OLS linear regressions of wind speed bin-averaged measurements, where the wind speeds are binned according to 0.5 ms -1 RHWS bins centred on integer multiples of 0.5 ms -1, showing the scatter plots of the bin averages and the best fit lines and error bars corresponding to suitable confidence intervals; d. OLS linear regressions are to be performed for all concurrent data, 2. Reference mast direction sector-wise OLS linear regression of GHWS and RHWS. 3. OLS regression of wind direction measurements, including a. scatter plots of Galion direction measurements against reference mast wind direction measurements, b. regression parameters, c. reference wind speed direction sector-wise regressions, d. an assessment of mast shadowing, e. an assessment of the shadowing of Galion measurement volumes. 2.3 Lidar error An analysis of Lidar error (defined as RHWS minus GHWS) is to be performed, including 1. mean and standard deviation of Lidar error for all data and data analysed by a. direction sector, b. the RHWS ranges detailed above and c. RHWS wind speed bins as detailed above. 2. Lidar error should be presented both as an absolute error in ms -1 and as a percentage of the corresponding RHWS. 3. A comparison of the observed Lidar error with the uncertainties introduced by the reference sensors is to be undertaken. Fraunhofer IWES Galion Lidar Performance Verification 15 37

16 3 Information supplied by SgurrEnergy 3.1 Wind data SgurrEnergy has provided the following information: 1. concurrent wind speed and direction data sets acquired by the Galion Lidar and the reference mast data files Calibratedmastdata.csv for the reference data (10-min averages), ArcScan_Data_ txt for the Galion Lidar data (as processed real time data approx. three values per minute); 2. mast installation and configuration details, including calibration certificates for all reference sensors, and mast installation reports files in folder Instrument_details; 3. site details, including details of orography and roughness complexity, and any obstacles influencing the flow files in folder Instrument_details (WTG Locations and Freestream Sectors). 3.2 Contexts and concepts The following guidelines and acceptance criteria were specified as references for the verification: 1. IEC CDV (in particular, Annex L) [2], 2. SgurrEnergy in-house site acceptance criteria (communicated via ) Fraunhofer IWES Galion Lidar Performance Verification

17 4 Results 4.1 Review of instrumentation and datasets Suitability of the mast for use as reference a. Mast location: The used reference mast is located in moderately complex terrain. Several maps with different level of detail as well as photographs were provided by SgurrEnergy, allowing a rough classification of the test site. North of the mast location lies Myres Hill (with the wind farm consisting of five turbines), to the South is Crook Hill and to the East Drumduff Hill. The mast itself is located at 329 m AMSL, with slightly sloping terrain to the West (where the free wind sector is placed). b. Mast configuration / layout: The mast layout is shown in the document 007_mast_eqipment_layout.pdf (see Annex A). It is a tubular mast with guy wires to the side. For the verification analysis, two top-mounted cup anemometers at 80 m mast height (mounted on a SE and a NW boom, resp.) are used. The corresponding wind vane for the reference wind direction measurements is one level below at 78.5 m. c. Mast installation: A Mast Installation And Testing Checklist (12_2013_001_GLA Test Mast Installation Testing B1.docx) was provided [3]. There have been no noticeable problems according to this document. d. Validity of the calibration certificates: The two used cup anemometers (SE- and NW-cup at 80 m) had been calibrated by Deutsche WindGuard Wind Tunnel Services GmbH in September 2011, i.e. about 13 months before the measurements and less than that before the erection of the mast (an exact date for the erection of the mast and the installation of the sensors could not be found in the documents). No remarks were listed in the calibration certificates. e. Compliance with relevant engineering standards: As relevant standard we refer to the IEC document [4], detailed requirements on the mounting of cup anemometers are found here in Annex G. As distance between top-mounted primary and control anemometers a range of 1.5 m to 2.5 m is specified. With a boom length of 1.15 m 3F1 (for both sides) the reference mast in the present test lies within these limits. The sensor arm length is specified to be minimum 0.75 m, which is not fulfilled with the present sensor arm of 0.5 m. The vertical distance between reference wind vane and cup anemometers agrees with 1.5 m with the minimum requirement in the standard Extent to which the measurements made by the mast and the Galion are comparable 1 details from document 12_2013_001_GLA Test Mast Installation Testing B1.docx Fraunhofer IWES Galion Lidar Performance Verification 17 37

18 a. Degree of concurrency and co-location of measurements with respect to the averaging interval: Reference data were provided as 10-min averages, Galion Lidar data as raw data that were then averaged by Fraunhofer IWES to 10-min averages. In this process it could be ensured that it was referred to the same averaging interval.... time synchronisation: It was assumed that the datasets are synchronized to a sufficient level, but could not be checked within this evaluation.... measurements are acquired within volumes with sufficiently well related wind conditions: The Galion Lidar scans an arc with a radius of 285 m (range gate 9) from Lidar along line of sight, corresponding to a horizontal distance of 266 m, with an azimuth angle from 59 to 119 (in seven steps). That is, the maximum azimuth angle between the single measurements of the arc is 60, and the maximum distance 266 m. The reference mast 4F1 lies exactly in the middle and along the arc, i.e. more or less in the centre of the Galion Lidar measurement volume, with the Galion itself being located to the West of the mast. The range gate together with the elevation angle of 20.8 for the Galion Lidar measurements implies a measurement height of (285 m sin(20.8 ) = ) m. The deviation of this number from the height of the reference measurements (namely 80 m) is due to the slope of the terrain. It is assumed (and has been confirmed by SgurrEnergy) that the two measurement are at the same height AMSL. b. Data availability: We have no information on the availability of the reference data within the 10- min averaging intervals but we assume that there are no significant data gaps. The availability of the Galion Lidar measurements is controlled with basically two parameters in the data preparation and filtering for the verification analysis (cf ): the number of rays accepted, and the number of records per 10-min averaging interval. Rays in the arc scan are accepted (or neglected) based on a pre-set SNR threshold. The number of accepted rays per arc scan is then compared with the total number of scans for the arc, and the measurement is disregarded if these two numbers do not agree. For the 10- min avarage value, finally, the number of (accepted) records within the interval is determined and used as a filter criterion. We applied the suggested numberof-records value of 18 but it could be adjusted to higher or lower values for an investigation of the effect of availability on the results of the verification analysis. c. Free stream conditions: A detailed analysis of free-stream sectors had been performed for both the mast and the Galion Lidar measurement volume, and documented with the files in the folder WTG_Locations and_freestream_sectors. The suggested valid wind sector of [240, 330 ] seems to be an acceptable compromise solution, taking into account the influence of obstacles within a distance of about 1 km. The closest wind turbine to the reference mast (within the wind sector suggested as valid) has a distance of about 1.4 km. 1 according to the coordinates in document Carrot Moor Freestream Analysis.xls Fraunhofer IWES Galion Lidar Performance Verification

19 4.1.3 Prevailing wind conditions Here we give an overview of the prevailing wind conditions, based on an anaysis of the data from the reference mast for the period 12/09 to 18/10/2012. There was no direction filter applied, and all wind speeds were considered. Figure 6 shows the distributions of wind speed (at 80 m measurement height left) and wind direction (at 78.5 m right). Figure 6: Distributions of horizontal wind speed (at 80 m left) and wind direction (78.5 m right) 10-min average data. Figure 7 shows the measured turbulence intensity (TI) versus the values of the reference wind speed at 80 m measurement height. The mean TI was calculated as 13.5% with a standard deviation of 4.8%. Figure 7: Turbulence intensity (defined as horizontal wind speed standard deviation divided by mean value) versus reference wind speed for 80 m measurement height. Figure 8 shows the wind speed shear and wind veer obtained from the data of the reference mast. The mean wind speed profile is fairly linear with the respective spreading of the presented 10-min profiles. The distribution of wind veer (with the definition in the caption of the figure) shows a slightly positive mean (for the wind Fraunhofer IWES Galion Lidar Performance Verification 19 37

20 direction data at 75 m and 40 m measurement height, in black). The figure also shows the corresponding distribution with data for 78.5 m as upper reference height (in red), the two distributions agree reasonably well. Figure 8: Vertical profiles of horizontal wind speed (10-min data and mean profile left), and distribution of wind veer (right) defined as deviation between wind direction measured at reference height (75 m and 78.5 m, latter in red) and wind direction at lower height (40 m). Figure 9 shows the distribution of flow inclination angles (defined as the angle between the vertical wind speed component, measured by Gill ultrasonic anemometer at 75 m, and horizontal wind speed component, measured by the cup anemometers at 80 m). The mean flow inclination is 1.22 with a standard deviation of Figure 9: Distribution of flow inclination angle (details of derivation in main text). 4.2 Lidar performance verification Data preparation Fraunhofer IWES Galion Lidar Performance Verification

21 Reference mast data were provided as 10-min averages for the period from 11/09/2012 (00:00) to 19/19/2012 (07:50), corresponding to 38 complete days (see Table 5). The timestamp was determined to be the start of the 10-min averaging period. The Reference Horizontal Wind Speed (RHWS) was defined as the average of the values recorded by two cup anemometers at 80 m (WS_80m_SE_Avg and WS_80m_NW_Avg) unless the wind direction was within 15 of the boom orientation, i.e. 135 or [120, 150 ] for the SE and 315 or [300, 330 ] for the NW anemometer. In the case that the average value is used, it is calculated as (WS_80m_SE_Avg + WS_80m_NW_Avg)/2. The Reference Wind Direction (RWD) data was measured by a wind vane at 78.5 m (WD_78_SW_VAvg). An offset of 45 deg had to be added to the data provided. Table 5: Overview of reference and Galion Lidar datasets. Data source Time period # complete days # samples5f1 reference mast 11/09/2012 (00:00) 19/10/2012 (07:50) Galion Lidar 12/09/2012 (15:27:01) 18/10/2012 (12:28:05) Galion Lidar data were provided for the period from 12/09/2012 (15:27:01) to 18/10/2012 (12:28:05), corresponding to 35 complete days lying within the period of the reference data (see also Table 5). The data have been available as raw data, and were then to be averaged for the verification. This allowed us to make sure that the same time steps were used and datasets were actually concurrent. The Galion Lidar dataset consists of the following data series: - Timestamp as date and time of the first ray of data in the pattern of rays constituting the arc scan; - Range gate as the range gate from which the arc scan data (to which a sinusoid was fitted) was taken, corresponds here to 285 m (range gate 9); - Elevation as the elevation angle of the beam, here 20.8 corresponding to a horizontal distance of 266 m; - Rays accepted the number of rays in the arc scan which were accepted after the application of an SNR filter (filter threshold is given in the file header as Intensity Threshold here 1.01, which corresponds to -20 db); - Rays total the total number of rays in the scan; - HWS horizontal wind speed, derived from the amplitude of the sinusoidal fit to the measured line-of-sight Doppler data; - Wind dir wind direction, derived from the phase of the sinusoidal fit; - Turb mean square residual of the sinusoidal fit; - Intensity mean (min) the mean (minimum) intensity of the radial velocity measurements in the fit; - Pitch / Roll recordings of the internal inclinometer. For the 10-min average values of the Galion Lidar data, data samples were averaged assigned to the time interval [ref_time(i), ref_time(i+1)[, where ref_time is the corresponding timestamp of the reference mast data (for the time interval i). 1 or corresponding complete 10-min intervals, resp. Fraunhofer IWES Galion Lidar Performance Verification 21 37

22 4.2.2 Data filtering The following data filters were defined and applied. A. Number of rays accepted Galion Lidar real time data records where the number of rays accepted is less than the total number of rays in the scan were filtered out and not considered for the 10-min average value. B. Galion turbulence measure Galion Lidar real time data records with sqrt(turb)/hws > 10% were filtered out and not considered for the 10-min average value. C. Number of records per 10-min averaging interval 10-min average samples with less than 18 records per 10-min averaging interval for the Galion Lidar data were filtered out. D. Wind direction limits 10-min average samples outside the suggested direction limits (240 and 330 ) were filtered out, resulting in the valid wind sector [240, 330 ]. E. Wind speed range (optional) A general wind speed filter was not defined but the analysis was to be performed for different pre-defined wind speed ranges resulting in a respective reduction of the dataset. No further data filters (e.g. with respect to precipitation / rain or temperature) 6F1 were applied. The effects of the different filters are presented in Table 6. Table 6: Combined Galion Lidar and reference data samples (10-min averages) before and after filtering. C refers to number-of-records filter, and D to wind direction filter. Total7F2 with C with D with C +D Results for performance verification Linear regression of 10-min average data 1 A filter with respect to precipation was tested in several applications and campaigns, according to IEC CDV it is however not recommended. A temperature filter may be necessary if anemometer icing is expected which is not the case for the described measurement campaign. 2 with data filters A and B, excl. NaN-values Fraunhofer IWES Galion Lidar Performance Verification

23 1-parameter and 2-parameter OLS linear regressions of Galion 10-min average horizontal wind speeds (GHWS) on concurrent and collocated reference mast 10-min average horizontal wind speeds (RHWS) were performed for all concurrent data. The models for the fit were defined as... 1-parameter linear regression model: 2-parameter linear regression model: where x refers to RHWS and y to GHWS. y = mx y = kx+c The following coefficients were derived: R2-fit: coefficient of determination (for 1- and 2-parameter model, resp.), R-P: Pearson correlation coefficient 8F1, R-Sp: Spearman rank coefficient. Table 7 and Table 8 show the results for the fit parameters and coefficients. Scatter plots of the data and best-fit lines are presented in Figure 10. Table 7: Results of linear regression (1- and 2-parameter model) of 10-min average data. # samples m [-] R2-fit1 k [-] C [ms -1 ] R2-fit Table 8: Results for Pearson and Spearman rank correlation coefficients. R2-P R2-Sp Note: for a 2-parameter linear model the squared Pearson correlation coefficient equals the coefficient of determination. Fraunhofer IWES Galion Lidar Performance Verification 23 37

24 Figure 10: Scatter plot of GHWS against RHWS 10-min mean values with best-fit lines obtained by 1-parameter (blue) and 2-parameter (red) OLS linear regression Linear regression of wind speed bin-averaged data OLS linear regressions of wind speed bin-averaged measurements were performed, where the wind speed bins are binned according to 0.5 ms -1 RHWS bins centred on integer multiples of 0.5 ms -1. Suitable confidence intervals were defined as: (1) bin mean of GHWS bin standard deviation of GHWS / sqrt(number of samples in bin); (2) bin mean of GHWS bin standard deviation of GHWS. Results for all concurrent data are presented in Table 9 (fit parameters and coefficients of determination for 1- and 2-parameter linear regression model) and Figure 11 (plot of bin means and best-fit lines). Table 9: Results of linear regression (1- and 2-parameter model) of RHWS-bin averages for all concurrent data. # bins m [-] R2-fit1 k [-] C [ms -1 ] R2-fit Fraunhofer IWES Galion Lidar Performance Verification

25 Figure 11: Scatter plot of GHWS against RHWS bin averages with best-fit lines obtained by 1-parameter (blue) and 2-parameter (red) OLS linear regression. Error bars correspond to suitable confidence intervals (left: bin averages standard error; right: bin averages standard deviation) Reference mast direction sector-wise linear regression Reference mast direction sector-wise OLS linear regression of GHWS on RHWS for 10-min averages and reference wind speed range (all concurrent data). Selected (alternative) wind direction sectors: [240, 330 ] reference sector; divided into three subsectors... [240, 270 [, [270, 300[, and [300, 330 [. Corresponding results for the 1- and 2-parameter linear regression model are given in Table 10. Table 10: Results of reference mast direction sector-wise linear regression (1- and 2- parameter model) of 10-min data. # samples m [-] R2-fit1 k [-] C [ms -1 ] R2-fit2 [240, 330 ] [240, 270 [ [270, 300 [ [300, 330 [ Fraunhofer IWES Galion Lidar Performance Verification 25 37

26 Linear regression of wind direction measurements A 2-parameter OLS linear regression of Galion 10-min average wind direction (GWD) on concurrent and collocated reference mast 10-min average wind direction (RWD) was performed for all concurrent data. 2-parameter linear regression model: GWD). y = kx+c, where x refers to RWD and y to The results are presented in Table 11 (fit parameters and coefficient of determination) and Figure 12 (scatter plot of wind direction data and best-fit line). Table 11: Results of linear regression (2-parameter model) of 10-min average wind direction data for all concurrent data but reduced dataset. # samples k [-] C [deg] R2-fit F F Figure 12: Scatter plot of GWD against RWD 10-min mean values with best-fit lines obtained by 2-parameter (red) OLS linear regression for two separated dataset. 1 for GWD < For GWD Fraunhofer IWES Galion Lidar Performance Verification

27 Assessment of mast shadowing and the shadowing of Galion measurement volumes Figure 13 shows the relative difference between the recordings of the two cup anemometers at 80 m measurement height (SE- and NW-cup) versus the values of the reference wind direction measured by the wind vane (at 78.5 m). Particularly large deviations are seen at around 135 (NW-cup records smaller values than those from SEcup) and 310 (SE-cup records smaller values than those from NW-cup). These two directions more or less correspond with the boom directions (135 and 315, resp.), which explains the observed behaviour of the measurements. Figure 13: Relative difference between RHWS measured by SE- and NW-cup anemometer versus RWD (10-min average values). In Figure 14 the recordings of the SE- and NW-cup anemometer are compared with the finally selected values for RHWS, i.e. (according to 4.2.1) either the average of both or (for a 15 sector around the respective boom orientations) the values of one of the cup anemometers. The findings from above are here confirmed. Fraunhofer IWES Galion Lidar Performance Verification 27 37

28 Figure 14: Relative difference between RHWS (selected according to 4.2.1) measured by SE- (red) or NW-cup anemometer (blue) and the respective recordings of the individual cup anemometers versus RWD (10-min average values). In Figure 15 the deviations between RHWS and GHWS (the reference and Galion Lidar wind speed recordings) are presented versus the reference wind direction. A new negative peak, i.e. one that cannot be ascribed to the wind speed data investigated before, is seen at about 155. This could be an effect of the shadowing of the Galion measurement volume. Figure 15: Relative difference between RHWS measured (average of two cup anemometers as defined above) and GHWS versus RWD (10-min average values) Fraunhofer IWES Galion Lidar Performance Verification

29 4.3 Lidar error An analysis of the Lidar error (defined as RHWS minus GHWS), presented both as an absolute error in ms -1 and as a percentage of the corresponding RHWS (referred to as relative Lidar error), was performed including mean and standard deviation of Lidar error for... - all data, - data analysed by direction sector, and - RHWS wind speed bins. Results for the different wind direction sectors (defined in ) are presented in Table 12. In Figure 16 Lidar error bin averages are shown, the corresponding statistics are presented in Table 13. Table 12: Statistics of Lidar error analysed by direction sector (sectors as defined above) for all concurrent data. # samples mean [ms -1 ] sd [ms -1 ] mean [%] sd [%] [240, 330 ] [240, 270 [ [270, 300 [ [300, 330 [ Figure 16: Scatter plot of Lidar error against RHWS bin averages. Error bars correspond to suitable confidence intervals (left: bin averages standard error; right: bin averages standard deviation); 10-min averages as red dots. Fraunhofer IWES Galion Lidar Performance Verification 29 37

30 Table 13: RHWS bin-wise statistics of Lidar error. RHWS [ms -1 ] # samples mean [ms -1 ] sd [ms -1 ] mean [%] sd [%] NA NA NA 5.10 NA Fraunhofer IWES Galion Lidar Performance Verification

31 Figure 17 shows a comparison of the observed Lidar error with the uncertainties introduced by the reference sensors here for reduced wind speed range (4 ms -1 to 16 ms -1 ). The reference uncertainty is derived as the squared sum of the following uncertainty components (cf. [1] with estimates for the present case): i) Uncertainty for wind tunnel calibration (0.1 ms -1 ); ii) Cup anemometer effects according to anemometer classification (0.052 ms RHWS(i) 1 ); iii) Cup anemometer mounting effects (1%, or 0.01 RHWS(i), resp.); iv) Uncertainty of any applied mast correction for the reference sensors (not relevant here). Figure 17: Comparison of the observed Lidar error (red: 10-min average values, black: bin averages) with the uncertainty associated with reference sensors (blue lines). 1 for the used anemometer (A100L2 by Windspeed Ltd) class 1.8A was identified (results of ACCUWIND project) Fraunhofer IWES Galion Lidar Performance Verification 31 37

32 5 Discussion The observed performance of the Galion Lidar in the considered test setup, expressed in the presented verification results (section 4), was evaluated with reference to the SgurrEnergy in-house site acceptance criteria (cf. Annex B). We came to the following findings: the found results do meet the SgurrEnergy in-house criteria i.e. the OLS regression slope is within 2% of unity (between 0.98 and 1.02), and R 2 > is fulfilled. In addition to the OLS linear regression of the 10-min average wind speed data, which is evaluated for the acceptance criteria referred to above, we also checked the wind speed bin averaged data. As to be expected, the results for the respective OLS linear fit show an even better agreement between the Galion Lidar and reference data (R 2 and slope parameter are closer to unity). A reference mast direction sector-wise analysis of the 10-min average wind speed data indicates that smaller sub-sectors of the pre-defined valid wind direction sector may give even slightly better results for the parameters of the linear fit. The definition of the valid sector is at this a trade-off between the achieved correlation of Galion Lidar and reference data and the number of available data points. A linear regression analysis of the 10-min average wind direction measurements indicates an offset in the Galion Lidar data for some part of the measurement period. It was later confirmed that the instrument was orientated in a way that caused that direction offset, for the one part of the period the offset had been removed in the post-processing of the data. For future measurements, it will be possible to accommodate the offset in the control software of the Galion Lidar. As a last step, the Lidar error (defined as reference wind speed minus Galion Lidar wind speed, both as 10-min average values) was analyzed. The mean Lidar error for the analyzed dataset is below 0.05 ms -1 or 1%, with a standard deviation of about 0.4 ms -1 or 5-6%. For most wind speed bins the bin-averaged Lidar error lies within the limits of the reference uncertainty. Given that the measurements were performed in moderately complex (and definitely not perfectly flat) terrain, and that the Galion Lidar was operated in a mode that is not aimed at a best possible agreement with the reference measurements 2 but has further advantages, the results of the verification test indicate a good performance of the measurement device under test. 1 taken here as coefficient of determination (R2-fit) 2 We understand that the arc scan is primarily applied in order to bridge a larger distance between the location of the measurement device and the point of the measurement (here the location defined by the reference mast). A drawback for the comparison with the reference measurements is then that the measurement volume of the Galion Lidar is not as sysmmetric around the point of the reference measurement as e.g. for a conical scan, which may reduce the correlation between Galion Lidar and reference mast measurements a priori Fraunhofer IWES Galion Lidar Performance Verification

33 6 Conclusions The measurement performance of a Galion Lidar (Galion G4000 Offshore, Unit ) was analyzed in a performance verification test. For this, wind speed and direction data acquired using a Galion Lidar were compared with concurrent data acquired using conventional mast mounted cup anemometry and wind sensors. The Galion Lidar under test was measuring with an arc scan technique, a configuration also referred to as remote mast since the Lidar measures the wind vector at several heights not at the location of the device (as e.g. a Lidar scanning along a vertical cone above the instrument) but in a certain horizontal distance. The measurement campaign took place at SgurrEnergy s remote sensing test facility at Carrot Moor (South of Myres Hill), which is close to Whitelee Wind Farm. The observed performance of the Galion Lidar in the considered test setup, expressed in the presented verification results, meets the SgurrEnergy in-house criteria, indicating an acceptable performance of the device under test. Given that the measurements were performed in moderately complex (and definitely not perfectly flat) terrain, and that the Galion Lidar was operated in a mode that is not aimed at a best possible agreement with the reference measurements but has further advantages, the results of the verification test indicate a good agreement with the reference mast. The arc scan mode of operation may be recommended for applications where a horizontal distance between the location of the measurement device and its measurements is necessary, e.g. for a power performance assessment offshore with the Galion Lidar installed on the transition piece of the test turbine. It is expected that the measurement uncertainties for such an application are even smaller than for the present Galion Lidar performance verification since the influences of the terrain would be less significant for an offshore site. Fraunhofer IWES Galion Lidar Performance Verification 33 37

34 References [1] Galion Lidar Performance Verification Request for Quotation, SgurrEnergy Ltd, November 2012 [2] CDV IEC Wind turbines Part 12-1: Power perfromance measurements of electricity producing wind turbines, IEC-TC88 Maintenance Team MT12-1, February 2013 [3] Mast installation and testing checklist (document 12_2013_001_GLA Test Mast Installation Testing B1.docx provided by SgurrEnergy) [4] IEC Ed. 1 Wind turbines Part 12-1: Power perfromance measurements of electricity producing wind turbines, IEC-TC88 Maintenance Team MT12-1, December Fraunhofer IWES Galion Lidar Performance Verification

35 Annex A Reference mast equipment layout Fraunhofer IWES Galion Lidar Performance Verification 35 37

36 Annex B Acceptance criteria B.1 SgurrEnergy in-house site acceptance criteria (communicated via ) Pass criteria: - OLS regression slope within 2% of unity - R 2 > Fraunhofer IWES Galion Lidar Performance Verification

37 Fraunhofer IWES Galion Lidar Performance Verification 37 37