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Analysis of Wind Turbine Aging Through Operation Data Calibrated by Lidar Measurement

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Analysis of Wind Turbine Aging Through Operation Data Calibrated by Lidar Measurement energies Article Analysis of Wind Turbine Aging through Operation Data Calibrated by LiDAR Measurement Hyun-Goo Kim * and Jin-Young Kim Korea Institute of Energy Research, Daejeon 34129, Korea; [email protected] * Correspondence: [email protected]; Tel.: +82-42-860-3376 Abstract: This study analyzed the performance decline of wind turbine with age using the SCADA (Supervisory Control And Data Acquisition) data and the short-term in situ LiDAR (Light Detection and Ranging) measurements taken at the Shinan wind farm located on the coast of Bigeumdo Island in the southwestern sea of South Korea. Existing methods have generally attempted to estimate performance aging through long-term trend analysis of a normalized capacity factor in which wind speed variability is calibrated. However, this study proposes a new method using SCADA data for wind farms whose total operation period is short (less than a decade). That is, the trend of power output deficit between predicted and actual power generation was analyzed in order to estimate performance aging, wherein a theoretically predicted level of power generation was calculated by substituting a free stream wind speed projecting to a wind turbine into its power curve. To calibrate a distorted wind speed measurement in a nacelle anemometer caused by the wake effect resulting from the rotation of wind-turbine blades and the shape of the nacelle, the free stream wind speed was measured using LiDAR remote sensing as the reference data; and the nacelle transfer function, which converts nacelle wind speed into free stream wind speed, was derived. A four-year analysis of the Shinan wind farm showed that the rate of performance aging of the wind turbines was estimated Citation: Kim, H.-G.; Kim, J.-Y. to be −0.52%p/year. Analysis of Wind Turbine Aging through Operation Data Calibrated Keywords: performance aging; normalized capacity factor; nacelle transfer function (NTF); LiDAR; by LiDAR Measurement. Energies 2021, 14, 2319. https://doi.org/ Shinan wind farm 10.3390/en14082319 Academic Editors: Davide Astolfi and Francesco Castellani 1. Introduction According to IEC 61400-1, which explains the design condition of wind turbines, the Received: 12 March 2021 design life-span of wind turbines should be more than twenty years [1]. Since a wind Accepted: 17 April 2021 turbine is a machine that operates continuously under repetitive fatigue load conditions, Published: 20 April 2021 its performance inevitably deteriorates as a result of aging during its twenty-year life cycle (blade erosion, efficiency reduction of gearbox, bearing, generator, etc.) [2]. Publisher’s Note: MDPI stays neutral The results of a comprehensive analysis of 5600 wind turbines in the UK and 7600 wind with regard to jurisdictional claims in turbines in Denmark [3] verified that performance due to aging deteriorated to a significant published maps and institutional affil- extent. Nonetheless, performance aging has not been taken into consideration during iations. evaluations of the economic feasibility of wind farm projects due to a lack of reliable estimation data. According to the results of another study on thirty years of operational records of 3200 wind turbines in Denmark [4], the rate of reduction of their capacity was calculated Copyright: © 2021 by the authors. to be −0.32%p (percent point) per year, but the report did not specify whether this rate Licensee MDPI, Basel, Switzerland. was due purely to a deterioration of the wind turbines or to variations in the annual wind This article is an open access article resources. The results of an analysis of wind farms located in Ontario, Canada, for five distributed under the terms and years [5] showed that if the annual wind speed change is calibrated every year, the rate conditions of the Creative Commons of reduction of their capacity was −1.0%p per year, which is a much higher rate than Attribution (CC BY) license (https:// that reported in Denmark. The estimation by Hughes (2012) [3] and that by Staffell and creativecommons.org/licenses/by/ Green (2014) [6], who calibrated wind resource variability using MERRA (Modern-Era 4.0/). Energies 2021, 14, 2319. https://doi.org/10.3390/en14082319 https://www.mdpi.com/journal/energies Energies 2021, 14, x FOR PEER REVIEW 2 of 12 Energies 2021, 14, 2319 2 of 12 (2014) [6], who calibrated wind resource variability using MERRA (Modern-Era Retro- Retrospectivespective analysis analysis for Research for Research and Applications) and Applications) reanalysis reanalysis data, showed data, showed that the that normal- the normalizedized load factor load (or factor capacity (or capacity factor) factor)in onshore in onshore wind farms wind in farms the UK in thehad UK fallen had steadily fallen steadilyby −0.48%p by − per0.48%p year for per a year decade. for aHere, decade. the normalized Here, the normalized load factor load is the factor total ispower the total out- powerput over output a certain over period a certain of periodtime divided of time by divided the maximum by the maximumpotential power potential output power ad- outputjusted for adjusted monthly for variations monthly variations of wind speed. of wind speed. ByrneByrne etet al. (2020) (2020) [7] [7 ]and and Astolfi Astolfi et etal. al.(2020) (2020) [8] demonstrated [8] demonstrated the wind the wind turbine turbine aging agingof Vestas of Vestas V52 using V52 usingthe support the support vector vector machin machinee regression regression of operation of operation curves, curves, and found and foundthat the that performance the performance decline decline over overten years ten years was about was about 5%. Hamilton 5%. Hamilton et al. et (2020) al. (2020) [9] ana- [9] analyzedlyzed fleet-wide fleet-wide performance performance with with age age with with 917 917 wind wind farms farms installed installed before 20082008 inin thethe USUS andand showedshowed thethe performanceperformance decline decline of of− −0.54%p per year. InIn SouthSouth Korea, Korea, the the rate rate of of deterioration deterioration in in the the capacity capacity of of Sungsan Sungsan wind wind farm farm in in Jeju Jeju waswas reportedreported toto be be− −0.12%p0.12%p perper yearyear forfor fivefive yearsyears [[10],10], butbut thethe reliabilityreliability ofof thethe resultresult is is suspicioussuspicious becausebecause itit waswas obtainedobtained byby aa trendtrend analysisanalysis thatthat usedused onlyonly fivefive values values for for the the annualannual averageaverage capacitycapacity factor.factor. MostMost previousprevious studies studies have have estimated estimated the the deterioration deterioration of wind of wind farm farm capacity capacity by con- by ductingconducting a trend a trend analysis analysis of the of normalized the normaliz capacityed capacity factor, factor, which waswhich calibrated was calibrated according ac- tocording monthly to windmonthly speed wind variability. speed va However,riability. However, according accordin to one analysisg to one of analysis wind resource of wind variabilityresource variability off the western off the coast western of South coast Korea of South [11 ],Korea the uncertainty [11], the uncertainty of the yearly of the capacity yearly factorcapacity decreased factor decreased to 0.7% when to 0.7% the when analysis the an periodalysis was period set was to ten set years to ten or years longer, or longer, while thewhile monthly the monthly capacity capacity factor fell factor to 2.7% fell whento 2.7% the when analysis the period analysis was period set to 36was months set to or36 longer.months This or longer. implies This that, implies since the that, uncertainty since the of uncertainty the capacity of factor the capacity due to wind factor resource due to variabilitywind resource is comparable variability or is higher comparable than that or higher of the windthan that turbine of the deterioration wind turbine rate, deteriora- a long- termtion trendrate, a analysis long-term of moretrend than analysis ten years of more would than be ten needed years to would obtain be a reliableneeded qualitativeto obtain a estimatereliable qualitative of the rate ofestimate deterioration of the rate by a of monthly deterioration trend analysis.by a monthly trend analysis. ForFor windwind farmsfarms whosewhose totaltotal operatingoperating periodperiod isis short, short, any any estimate estimate of of performance performance agingaging would be be unreliable unreliable since since the the uncertainty uncertainty of wind of wind resource resource variability variability is more is more dom- dominantinant than than the wind the wind farm farmdeterioration deterioration rate. The rate. cumulative The cumulative installation installation capacity capacity of MW- ofclass MW-class wind turbines wind turbines in South in Korea South is Korea1512 MW is 1512 (as of MW the (asend of of the2019), end and of 2019),the cumulative and the cumulativenumber of installations number of installationsis over 600 turbines, is over 600around turbines, half of around which halfhave of a whichtotal operating have a totalperiod operating of less than period five of years less (Figure than five 1) years[12]. Thus, (Figure even1)[ if12 the]. Thus,monthly even wind if the speed monthly varia- windtion is speed calibrated variation in the is case calibrated of wind in turbines the case in of South wind Korea, turbines few in of South the available Korea, few cumu- of thelative available operational cumulative data cover operational a long enough data cover peri aod long to enable
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