Abstract. Through numerical simulations and the analysis of field measurements, we investigate the dependence of the accuracy and uncertainty of turbulence estimations on the main features of the nacelle lidars' scanning strategy, i.e., the number of measurement points, the half-cone opening angle, the focus distance and the type of the lidar system. We assume homogeneous turbulence over the lidar scanning area in front of a Vestas V52 wind turbine. The Reynolds stresses are computed via a least-squares procedure that uses the radial velocity variances of each lidar beam without the need to reconstruct the wind components. The lidar-retrieved Reynolds stresses are compared with those from a sonic anemometer at turbine hub height. Our findings from the analysis of both simulations and measurements demonstrate that to estimate the six Reynolds stresses accurately, a nacelle lidar system with at least six beams is required. Further, one of the beams of this system should have a different opening angle. Adding one central beam improves the estimations of the velocity components' variances. Assuming the relations of the velocity components' variances as suggested in the IEC standard, all considered lidars can estimate the along-wind variance accurately using the least-squares procedure and the Doppler radial velocity spectra. Increasing the opening angle increases the accuracy and reduces the uncertainty on the transverse components, while enlarging the measurement distance has opposite effects. All in all, a six-beam continuous-wave lidar measuring at a close distance with a large opening angle provides the best estimations of all Reynolds stresses. This work gives insights on designing and utilizing nacelle lidars for inflow turbulence characterization.
{"title":"Dependence of turbulence estimations on nacelle lidar scanning strategies","authors":"Wei Fu, A. Sebastiani, A. Peña, J. Mann","doi":"10.5194/wes-8-677-2023","DOIUrl":"https://doi.org/10.5194/wes-8-677-2023","url":null,"abstract":"Abstract. Through numerical simulations and the analysis of field measurements, we investigate the dependence of the accuracy and uncertainty of turbulence\u0000estimations on the main features of the nacelle lidars' scanning strategy, i.e., the number of measurement points, the half-cone opening angle, the\u0000focus distance and the type of the lidar system. We assume homogeneous turbulence over the lidar scanning area in front of a Vestas V52 wind\u0000turbine. The Reynolds stresses are computed via a least-squares procedure that uses the radial velocity variances of each lidar beam without the\u0000need to reconstruct the wind components. The lidar-retrieved Reynolds stresses are compared with those from a sonic anemometer at turbine hub\u0000height. Our findings from the analysis of both simulations and measurements demonstrate that to estimate the six Reynolds stresses accurately, a\u0000nacelle lidar system with at least six beams is required. Further, one of the beams of this system should have a different opening angle. Adding one\u0000central beam improves the estimations of the velocity components' variances. Assuming the relations of the velocity components' variances as\u0000suggested in the IEC standard, all considered lidars can estimate the along-wind variance accurately using the least-squares procedure and the\u0000Doppler radial velocity spectra. Increasing the opening angle increases the accuracy and reduces the uncertainty on the transverse components, while\u0000enlarging the measurement distance has opposite effects. All in all, a six-beam continuous-wave lidar measuring at a close distance with a large\u0000opening angle provides the best estimations of all Reynolds stresses. This work gives insights on designing and utilizing nacelle lidars for inflow\u0000turbulence characterization.\u0000","PeriodicalId":46540,"journal":{"name":"Wind Energy Science","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44030628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract. We investigate the aerodynamics of a surging, heaving, and yawing wind turbine with numerical simulations based on a free-wake panel method. We focus on the UNAFLOW (UNsteady Aerodynamics of FLOating Wind turbines) case: a surging wind turbine which was modeled experimentally and with various numerical methods. Good agreement with experimental data is observed for amplitude and phase of the thrust with surge motion. We achieve numerical results of a wind turbine wake that accurately reproduce experimentally verified effects of surging motion. We then extend our simulations beyond the frequency range of the UNAFLOW experiments and reach results that do not follow a quasi-steady response for surge. Finally, simulations are done with the turbine in yaw and heave motion, and the impact of the wake motion on the blade thrust is examined. Our work seeks to contribute a different method to the pool of results for the UNAFLOW case while extending the analysis to conditions that have not been simulated before and providing insights into nonlinear aerodynamic effects of wind turbine motion.
{"title":"Nonlinear inviscid aerodynamics of a wind turbine rotor in surge, sway, and yaw motions using a free-wake panel method","authors":"André F. P. Ribeiro, D. Casalino, C. Ferreira","doi":"10.5194/wes-8-661-2023","DOIUrl":"https://doi.org/10.5194/wes-8-661-2023","url":null,"abstract":"Abstract. We investigate the aerodynamics of a surging, heaving, and yawing wind turbine with numerical simulations based on a free-wake panel method. We focus on the UNAFLOW (UNsteady Aerodynamics of FLOating Wind turbines) case: a surging wind turbine which was modeled experimentally and with various numerical methods. Good agreement with experimental data is observed for amplitude and phase of the thrust with surge motion. We achieve numerical results of a wind turbine wake that accurately reproduce experimentally verified effects of surging motion. We then extend our simulations beyond the frequency range of the UNAFLOW experiments and reach results that do not follow a quasi-steady response for surge. Finally, simulations are done with the turbine in yaw and heave motion, and the impact of the wake motion on the blade thrust is examined. Our work seeks to contribute a different method to the pool of results for the UNAFLOW case while extending the analysis to conditions that have not been simulated before and providing insights into nonlinear aerodynamic effects of wind turbine motion.\u0000","PeriodicalId":46540,"journal":{"name":"Wind Energy Science","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44444024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract. The increasing demand for wind energy offshore requires more hub-height-relevant wind information, while larger wind turbine sizes require measurements at greater heights. In situ measurements are harder to acquire at higher atmospheric levels; meanwhile the emergence of machine-learning applications has led to several studies demonstrating the improvement in accuracy for vertical wind extrapolation over conventional power-law and logarithmic-profile methods. Satellite wind retrievals supply multiple daily wind observations offshore, however only at 10 m height. The goal of this study is to develop and validate novel machine-learning methods using satellite wind observations and near-surface atmospheric measurements to extrapolate wind speeds to higher heights. A machine-learning model is trained on 12 years of collocated offshore wind measurements from a meteorological mast (FINO3) and space-borne wind observations from the Advanced Scatterometer (ASCAT). The model is extended vertically to predict the FINO3 vertical wind profile. Horizontally, it is validated against the NORwegian hindcast Archive (NORA3) mesoscale model reanalysis data. In both cases the model slightly over-predicts the wind speed with differences of 0.25 and 0.40 m s−1, respectively. An important feature in the model-training process is the air–sea temperature difference; thus satellite sea surface temperature observations were included in the horizontal extension of the model, resulting in 0.20 m s−1 differences with NORA3. A limiting factor when training machine-learning models with satellite observations is the small finite number of daily samples at discrete times; this can skew the training process to higher-/lower-wind-speed predictions depending on the average wind speed at the satellite observational times. Nonetheless, results shown in this proof-of-concept study demonstrate the limited applicability of using machine-learning techniques to extrapolate long-term satellite wind observations when enough samples are available.
摘要海上风能需求的增加需要更多与轮毂高度相关的风力信息，而更大的风力涡轮机尺寸需要在更高的高度进行测量。在更高的大气水平下，现场测量更难获得；与此同时，机器学习应用的出现导致了几项研究表明，与传统的幂律和对数剖面方法相比，垂直风外推的精度有所提高。卫星风力反演提供了多个海上每日风力观测，但仅为10 m高。这项研究的目标是开发和验证新的机器学习方法，利用卫星风观测和近地表大气测量将风速外推到更高的高度。机器学习模型是根据气象桅杆（FINO3）的12年并置海上风测量和高级散射仪（ASCAT）的星载风观测进行训练的。对该模型进行了垂直扩展，以预测FINO3的垂直风廓线。在水平方向上，它与挪威后预报档案（NORA3）中尺度模式再分析数据进行了验证。在这两种情况下，模型都略微高估了风速，差异为0.25和0.40 m s−1。模型训练过程中的一个重要特征是海气温差；因此，卫星海面温度观测结果被包括在模型的水平扩展中，结果为0.20 m s−1与NORA3的差异。用卫星观测训练机器学习模型时的一个限制因素是离散时间的每日样本数量有限；这可能会使训练过程偏离到更高/更低的风速预测，这取决于卫星观测时间的平均风速。尽管如此，这项概念验证研究的结果表明，在有足够样本的情况下，使用机器学习技术推断长期卫星风观测的适用性有限。
{"title":"Vertical extrapolation of Advanced Scatterometer (ASCAT) ocean surface winds using machine-learning techniques","authors":"Daniel Hatfield, C. Hasager, Ioanna Karagali","doi":"10.5194/wes-8-621-2023","DOIUrl":"https://doi.org/10.5194/wes-8-621-2023","url":null,"abstract":"Abstract. The increasing demand for wind energy offshore requires more hub-height-relevant wind information, while larger wind turbine sizes require measurements at greater heights. In situ measurements are harder to acquire at higher atmospheric levels; meanwhile the emergence of machine-learning applications has led to several studies demonstrating the improvement in accuracy for vertical wind extrapolation over conventional power-law and logarithmic-profile methods. Satellite wind retrievals supply multiple daily wind observations offshore, however only at 10 m height. The goal of this study is to develop and validate novel machine-learning methods using satellite wind observations and near-surface atmospheric measurements to extrapolate wind speeds to higher heights. A machine-learning model is trained on 12 years of collocated offshore wind measurements from a meteorological mast (FINO3) and space-borne wind observations from the Advanced Scatterometer (ASCAT). The model is extended vertically to predict the FINO3 vertical wind profile. Horizontally, it is validated against the NORwegian hindcast Archive (NORA3) mesoscale model reanalysis data. In both cases the model slightly over-predicts the wind speed with differences of 0.25 and 0.40 m s−1, respectively. An important feature in the model-training process is the air–sea temperature difference; thus satellite sea surface temperature observations were included in the horizontal extension of the model, resulting in 0.20 m s−1 differences with NORA3. A limiting factor when training machine-learning models with satellite observations is the small finite number of daily samples at discrete times; this can skew the training process to higher-/lower-wind-speed predictions depending on the average wind speed at the satellite observational times. Nonetheless, results shown in this proof-of-concept study demonstrate the limited applicability of using machine-learning techniques to extrapolate long-term satellite wind observations when enough samples are available.\u0000","PeriodicalId":46540,"journal":{"name":"Wind Energy Science","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43862344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract. Uncertainty quantification of long-term modeled wind speed is essential to ensure stakeholders can best leverage wind resource numerical data sets. Offshore, this need is even stronger given the limited availability of observations of wind speed at heights relevant for wind energy purposes and the resulting heavier relative weight of numerical data sets for wind energy planning and operational projects. In this analysis, we consider the National Renewable Energy Laboratory's 21-year updated numerical offshore data set for the US East Coast and provide a methodological framework to leverage both floating lidar and near-surface buoy observations in the region to quantify uncertainty in the modeled hub-height wind resource. We first show how using a numerical ensemble to quantify the uncertainty in modeled wind speed is insufficient to fully capture the model deviation from real-world observations. Next, we train and validate a random forest to vertically extrapolate near-surface wind speed to hub height using the available short-term lidar data sets in the region. We then apply this model to vertically extrapolate the long-term near-surface buoy wind speed observations to hub height so that they can be directly compared to the long-term numerical data set. We find that the mean 21-year uncertainty in 140 m hourly average wind speed is slightly lower than 3 m s−1 (roughly 30 % of the mean observed wind speed) across the considered region. Atmospheric stability is strictly connected to the modeled wind speed uncertainty, with stable conditions associated with an uncertainty which is, on average, about 20 % larger than the overall mean uncertainty.
摘要长期建模风速的不确定性量化对于确保利益相关者能够最好地利用风资源数值数据集至关重要。在海上，考虑到与风能目的相关的高度风速观测的可用性有限，以及由此产生的风能规划和运营项目的数值数据集的相对权重更大，这种需求更加强烈。在这项分析中，我们考虑了美国国家可再生能源实验室21年来更新的美国东海岸海上数值数据集，并提供了一个方法框架，以利用该地区的浮动激光雷达和近表面浮标观测来量化建模的枢纽高度风资源的不确定性。我们首先展示了使用数值系综来量化建模风速的不确定性不足以完全捕捉模型与真实世界观测的偏差。接下来，我们训练并验证一个随机森林，使用该地区可用的短期激光雷达数据集，将近地表风速垂直外推到轮毂高度。然后，我们应用该模型将长期近水面浮标风速观测值垂直外推到轮毂高度，以便可以直接将其与长期数值数据集进行比较。我们发现，在140 m小时平均风速略低于3 m s−1（大约30 % 平均观测风速）。大气稳定性与建模的风速不确定性严格相关，稳定条件与平均约20的不确定性相关 % 大于总体平均不确定性。
{"title":"Long-term uncertainty quantification in WRF-modeled offshore wind resource off the US Atlantic coast","authors":"Nicola Bodini, Simon Castagneri, M. Optis","doi":"10.5194/wes-8-607-2023","DOIUrl":"https://doi.org/10.5194/wes-8-607-2023","url":null,"abstract":"Abstract. Uncertainty quantification of long-term modeled wind speed is essential to ensure stakeholders can best leverage wind resource numerical data sets. Offshore, this need is even stronger given the limited availability of observations of wind speed at heights relevant for wind energy purposes and the resulting heavier relative weight of numerical data sets for wind energy planning and operational projects. In this analysis, we consider the National Renewable Energy Laboratory's 21-year updated numerical offshore data set for the US East Coast and provide a methodological framework to leverage both floating lidar and near-surface buoy observations in the region to quantify uncertainty in the modeled hub-height wind resource. We first show how using a numerical ensemble to quantify the uncertainty in modeled wind speed is insufficient to fully capture the model deviation from real-world observations. Next, we train and validate a random forest to vertically extrapolate near-surface wind speed to hub height using the available short-term lidar data sets in the region. We then apply this model to vertically extrapolate the long-term near-surface buoy wind speed observations to hub height so that they can be directly compared to the long-term numerical data set. We find that the mean 21-year uncertainty in 140 m hourly average wind speed is slightly lower than 3 m s−1 (roughly 30 % of the mean observed wind speed) across the considered region. Atmospheric stability is strictly connected to the modeled wind speed uncertainty, with stable conditions associated with an uncertainty which is, on average, about 20 % larger than the overall mean uncertainty.\u0000","PeriodicalId":46540,"journal":{"name":"Wind Energy Science","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49415372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract. Extensive measurements in the area of wind turbines were performed in order to validate a sound propagation model which is based on the Crank–Nicolson parabolic equation method. The measurements were carried out over a flat grass-covered landscape and under various environmental conditions. During the measurements, meteorological and wind turbine performance data were acquired and acoustical data sets were recorded at distances of 178, 535 and 845 m from the wind turbine. By processing and analysing the measurement data, validation cases and input parameters for the sound propagation model were derived. The validation includes five groups that are characterised by different sound propagation directions, i.e. downwind, crosswind and upwind conditions in varying strength. In strong upwind situations, the sound pressure levels at larger distances are overestimated because turbulence is not considered in the modelling. In the other directions, the model reproduces the measured sound propagation losses well in the overall sound pressure level and in the third octave band spectra. As in the recorded measurements, frequency-dependent maxima and minima are identified, and losses generally increase with increasing distance and frequency. The agreement between measured and modelled sound propagation losses decreases with distance. The data sets used in the validation are freely accessible for further research.
{"title":"A new base of wind turbine noise measurement data and its application for a systematic validation of sound propagation models","authors":"Susanne Könecke, Jasmin Hörmeyer, Tobias Bohne, Raimund Rolfes","doi":"10.5194/wes-8-639-2023","DOIUrl":"https://doi.org/10.5194/wes-8-639-2023","url":null,"abstract":"Abstract. Extensive measurements in the area of wind turbines were performed in order to validate a sound propagation model which is based on the Crank–Nicolson parabolic equation method. The measurements were carried out over a flat grass-covered landscape and under various environmental conditions. During the measurements, meteorological and wind turbine performance data were acquired and acoustical data sets were recorded at distances of 178, 535 and 845 m from the wind turbine. By processing and analysing the measurement data, validation cases and input parameters for the sound propagation model were derived. The validation includes five groups that are characterised by different sound propagation directions, i.e. downwind, crosswind and upwind conditions in varying strength. In strong upwind situations, the sound pressure levels at larger distances are overestimated because turbulence is not considered in the modelling. In the other directions, the model reproduces the measured sound propagation losses well in the overall sound pressure level and in the third octave band spectra. As in the recorded measurements, frequency-dependent maxima and minima are identified, and losses generally increase with increasing distance and frequency. The agreement between measured and modelled sound propagation losses decreases with distance. The data sets used in the validation are freely accessible for further research.","PeriodicalId":46540,"journal":{"name":"Wind Energy Science","volume":"278 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136000635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Anna von Brandis, G. Centurelli, Jonas Schmidt, L. Vollmer, B. Djath, M. Dörenkämper
Abstract. We propose that considering mesoscale wind direction changes in the computation of wind farm cluster wakes could reduce the uncertainty of engineering wake modeling tools. The relevance of mesoscale wind direction changes is investigated using a wind climatology of the German Bight area covering 30 years, derived from the New European Wind Atlas (NEWA). Furthermore, we present a new solution for engineering modeling tools that accounts for the effect of such changes on the propagation of cluster wakes. The mesoscale wind direction changes relevant to the operation of wind farm clusters in the German Bight are found to exceed 11∘ in 50 % of all cases. Particularly in the lower partial load range, which is associated with strong wake formation, the wind direction changes are the most pronounced, with quartiles reaching up to 20∘. Especially on a horizontal scale of several tens of kilometers to 100 km, wind direction changes are relevant. Both the temporal and spatial scales at which large wind direction changes occur depend on the presence of synoptic pressure systems. Furthermore, atmospheric conditions which promote far-reaching wakes were found to align with a strong turning in 14.6 % of the cases. In order to capture these mesoscale wind direction changes in engineering model tools, a wake propagation model was implemented in the Fraunhofer IWES wind farm and wake modeling software flappy (Farm Layout Program in Python). The propagation model derives streamlines from the horizontal velocity field and forces the single turbine wakes along these streamlines. This model has been qualitatively evaluated by simulating the flow around wind farm clusters in the German Bight with data from the mesoscale atlas of the NEWA and comparing the results to synthetic aperture radar (SAR) measurements for selected situations. The comparison reveals that the flow patterns are in good agreement if the underlying mesoscale data capture the velocity field well. For such cases, the new model provides an improvement compared to the baseline approach of engineering models, which assumes a straight-line propagation of wakes. The streamline and the baseline models have been further compared in terms of their quantitative effect on the energy yield. Simulating two neighboring wind farm clusters over a time period of 10 years, it is found that there are no significant differences across the models when computing the total energy yield of both clusters. However, extracting the wake effect of one cluster on the other, the two models show a difference of about 1 %. Even greater differences are commonly observed when comparing single situations. Therefore, we claim that the model has the potential to reduce uncertainty in applications such as site assessment and short-term power forecasting.
{"title":"An investigation of spatial wind direction variability and its consideration in engineering models","authors":"Anna von Brandis, G. Centurelli, Jonas Schmidt, L. Vollmer, B. Djath, M. Dörenkämper","doi":"10.5194/wes-8-589-2023","DOIUrl":"https://doi.org/10.5194/wes-8-589-2023","url":null,"abstract":"Abstract. We propose that considering mesoscale wind direction changes in the computation of wind farm cluster wakes could reduce the uncertainty of engineering wake modeling tools. The relevance of mesoscale wind direction changes is investigated using a wind climatology of the German Bight area covering 30 years, derived from the New European Wind Atlas (NEWA). Furthermore, we present a new solution for engineering modeling tools that accounts for the effect of such changes on the propagation of cluster wakes. The mesoscale wind direction changes relevant to the operation of wind farm clusters in the German Bight are found to exceed 11∘ in 50 % of all cases. Particularly in the lower partial load range, which is associated with strong wake formation, the wind direction changes are the most pronounced, with quartiles reaching up to 20∘. Especially on a horizontal scale of several tens of kilometers to 100 km, wind direction changes are relevant. Both the temporal and spatial scales at which large wind direction changes occur depend on the presence of synoptic pressure systems. Furthermore, atmospheric conditions which promote far-reaching wakes were found to align with a strong turning in 14.6 % of the cases. In order to capture these mesoscale wind direction changes in engineering model tools, a wake propagation model was implemented in the Fraunhofer IWES wind farm and wake modeling software flappy (Farm Layout Program in Python). The propagation model derives streamlines from the horizontal velocity field and forces the single turbine wakes along these streamlines. This model has been qualitatively evaluated by simulating the flow around wind farm clusters in the German Bight with data from the mesoscale atlas of the NEWA and comparing the results to synthetic aperture radar (SAR) measurements for selected situations. The comparison reveals that the flow patterns are in good agreement if the underlying mesoscale data capture the velocity field well. For such cases, the new model provides an improvement compared to the baseline approach of engineering models, which assumes a straight-line propagation of wakes. The streamline and the baseline models have been further compared in terms of their quantitative effect on the energy yield. Simulating two neighboring wind farm clusters over a time period of 10 years, it is found that there are no significant differences across the models when computing the total energy yield of both clusters. However, extracting the wake effect of one cluster on the other, the two models show a difference of about 1 %. Even greater differences are commonly observed when comparing single situations. Therefore, we claim that the model has the potential to reduce uncertainty in applications such as site assessment and short-term power forecasting.\u0000","PeriodicalId":46540,"journal":{"name":"Wind Energy Science","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43260463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Edwin Kipchirchir, M. Hung Do, Jackson G. Njiri, Dirk Söffker
Abstract. Variability in wind profiles in both space and time is responsible for fatigue loading in wind turbine components. Advanced control methods for mitigating structural loading in these components have been proposed in previous works. These also incorporate other objectives like speed and power regulation for above-rated wind speed operation. In recent years, lifetime control and extension strategies have been proposed to guarantee power supply and operational reliability of wind turbines. These control strategies typically rely on a fatigue load evaluation criteria to determine the consumed lifetime of these components, subsequently varying the control set point to guarantee a desired lifetime of the components. Most of these methods focus on controlling the lifetime of specific structural components of a wind turbine, typically the rotor blade or tower. Additionally, controllers are often designed to be valid about specific operating points and hence exhibit deteriorating performance in varying operating conditions. Therefore, they are not able to guarantee a desired lifetime in varying wind conditions. In this paper an adaptive lifetime control strategy is proposed for controlled aging of rotor blades to guarantee a desired lifetime while considering damage accumulation level in the tower. The method relies on an online structural health monitoring system to vary the lifetime controller gains based on a state-of-health (SoH) measure by considering the desired lifetime at every time step. For demonstration, a 1.5 MW National Renewable Energy Laboratory (NREL) reference wind turbine is used. The proposed adaptive lifetime controller regulates structural loading in the rotor blades to guarantee a predefined damage level at the desired lifetime without sacrificing the speed regulation performance of the wind turbine. Additionally, a significant reduction in the tower fatigue damage is observed.
{"title":"Prognostics-based adaptive control strategy for lifetime control of wind turbines","authors":"Edwin Kipchirchir, M. Hung Do, Jackson G. Njiri, Dirk Söffker","doi":"10.5194/wes-8-575-2023","DOIUrl":"https://doi.org/10.5194/wes-8-575-2023","url":null,"abstract":"Abstract. Variability in wind profiles in both space and time is responsible for fatigue loading in wind turbine components. Advanced control methods for mitigating structural loading in these components have been proposed in previous works. These also incorporate other objectives like speed and power regulation for above-rated wind speed operation. In recent years, lifetime control and extension strategies have been proposed to guarantee power supply and operational reliability of wind turbines. These control strategies typically rely on a fatigue load evaluation criteria to determine the consumed lifetime of these components, subsequently varying the control set point to guarantee a desired lifetime of the components. Most of these methods focus on controlling the lifetime of specific structural components of a wind turbine, typically the rotor blade or tower. Additionally, controllers are often designed to be valid about specific operating points and hence exhibit deteriorating performance in varying operating conditions. Therefore, they are not able to guarantee a desired lifetime in varying wind conditions. In this paper an adaptive lifetime control strategy is proposed for controlled aging of rotor blades to guarantee a desired lifetime while considering damage accumulation level in the tower. The method relies on an online structural health monitoring system to vary the lifetime controller gains based on a state-of-health (SoH) measure by considering the desired lifetime at every time step. For demonstration, a 1.5 MW National Renewable Energy Laboratory (NREL) reference wind turbine is used. The proposed adaptive lifetime controller regulates structural loading in the rotor blades to guarantee a predefined damage level at the desired lifetime without sacrificing the speed regulation performance of the wind turbine. Additionally, a significant reduction in the tower fatigue damage is observed.","PeriodicalId":46540,"journal":{"name":"Wind Energy Science","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135278185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Lorena Campoverde-Vilela, Maria del Cisne Feijóo, Y. Vidal, José Sampietro, C. Tutivén
Abstract. Renewable energy is a clean and inexhaustible source of energy, so every year interest in the study and the search for improvements in production increases. Wind energy is one of the most used sources of energy, and therefore the need for predictive maintenance management to guarantee the reliability and operability of each of the wind turbines has become a great study opportunity. In this work, a fault detection system is developed by applying an anomaly detector based on principal component analysis (PCA), in order to state early warnings of possible faults in the main bearing. For the development of the model, SCADA data from a wind park in operation are utilized. The results obtained allow detection of failures even months before the fatal breakdown occurs. This model requires (to be constructed) only the use of healthy SCADA data, without the need to obtain the fault history or install additional equipment or sensors that require greater investment. In conclusion, this proposed strategy provides a tool for the planning and execution of predictive maintenance within wind parks.
{"title":"Anomaly-based fault detection in wind turbine main bearings","authors":"Lorena Campoverde-Vilela, Maria del Cisne Feijóo, Y. Vidal, José Sampietro, C. Tutivén","doi":"10.5194/wes-8-557-2023","DOIUrl":"https://doi.org/10.5194/wes-8-557-2023","url":null,"abstract":"Abstract. Renewable energy is a clean and inexhaustible source of energy, so every year interest in the study and the search for improvements in production increases. Wind energy is one of the most used sources of energy, and therefore the need for predictive maintenance management to guarantee the reliability and operability of each of the wind turbines has become a great study opportunity. In this work, a fault detection system is developed by applying an anomaly detector based on principal component analysis (PCA), in order to state early warnings of possible faults in the main bearing. For the development of the model, SCADA data from a wind park in operation are utilized. The results obtained allow detection of failures even months before the fatal breakdown occurs. This model requires (to be constructed) only the use of healthy SCADA data, without the need to obtain the fault history or install additional equipment or sensors that require greater investment. In conclusion, this proposed strategy provides a tool for the planning and execution of predictive maintenance within wind parks.\u0000","PeriodicalId":46540,"journal":{"name":"Wind Energy Science","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47736175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract. The size of newly installed offshore wind farms increases rapidly. Planned offshore wind farm clusters have a rated capacity of several gigawatts and a length of up to 100 km. The flow through and around wind farms of this scale can be significantly different than the flow through and around smaller wind farms on the sub-gigawatt scale. A good understanding of the involved flow physics is vital for accurately predicting the wind farm power output as well as predicting the meteorological conditions in the wind farm wake. To date there is no study that directly compares small wind farms (sub-gigawatt) with large wind farms (super-gigawatt) in terms of flow effects or power output. The aim of this study is to fill this gap by providing this direct comparison by performing large-eddy simulations of a small wind farm (13 km length) and a large wind farm (90 km length) in a convective boundary layer, which is the most common boundary layer type in the North Sea. The results show that there are significant differences in the flow field and the energy budgets of the small and large wind farm. The large wind farm triggers an inertial wave with a wind direction amplitude of approximately 10∘ and a wind speed amplitude of more than 1 m s−1. In a certain region in the far wake of a large wind farm the wind speed is greater than far upstream of the wind farm, which can be beneficial for a downstream located wind farm. The inertial wave also exists for the small wind farm, but the amplitudes are approximately 4 times weaker and thus may be hardly observable in real wind farm flows that are more heterogeneous. Regarding turbulence intensity, the wake of the large wind farm has the same length as the wake of the small wind farm and is only a few kilometers long. Both wind farms trigger inertial gravity waves in the free atmosphere, whereas the amplitude is approximately twice as large for the large wind farm. The inertial gravity waves induce streamwise pressure gradients inside the boundary layer, affecting the energy budgets of the wind farms. The most dominant energy source of the small wind farm is the horizontal advection of kinetic energy, but for the large wind farm the vertical turbulent flux of kinetic energy is 5 times greater than the horizontal advection of kinetic energy. The energy input by the gravity-wave-induced pressure gradient is greater for the small wind farm because the pressure gradient is greater. For the large wind farm, the energy input by the geostrophic forcing (synoptic-scale pressure gradient) is significantly enhanced by the wind direction change that is related to the inertial oscillation. For both wind farms approximately 75 % of the total available energy is extracted by the wind turbines and 25 % is dissipated.
摘要新安装的海上风电场的规模迅速增加。计划中的海上风电场集群的额定容量为几吉瓦，长度可达100 这种规模的风电场及其周围的流量可能与亚千兆瓦规模的小型风电场及其附近的流量显著不同。对相关流动物理的良好理解对于准确预测风电场功率输出以及预测风电场尾流中的气象条件至关重要。到目前为止，还没有研究在流量效应或功率输出方面直接比较小型风电场（亚千兆瓦）和大型风电场（超千兆瓦）。本研究的目的是通过对小型风电场（13 公里长）和一个大型风电场（90 km长），这是北海最常见的边界层类型。结果表明，小型和大型风电场的流场和能量预算存在显著差异。大型风电场触发惯性波，风向振幅约为10∘，风速振幅大于1 m s−1.在大型风电场远尾流的某个区域，风速大于风电场远上游的风速，这对位于下游的风电场有利。惯性波也存在于小型风电场中，但振幅大约弱4倍，因此在更不均匀的真实风电场流中可能很难观察到。关于湍流强度，大型风电场的尾流与小型风电场的尾流长度相同，只有几公里长。两个风电场都会在自由大气中触发惯性重力波，而大型风电场的振幅大约是其两倍。惯性重力波在边界层内引起流向压力梯度，影响风电场的能量预算。小型风电场最主要的能量来源是动能的水平平流，但大型风电场的动能垂直湍流通量是动能水平平流的5倍。对于小型风电场，重力波引起的压力梯度所输入的能量更大，因为压力梯度更大。对于大型风电场，与惯性振荡有关的风向变化显著增强了地转强迫（天气尺度压力梯度）输入的能量。对于两个风电场，约75 % 总可用能量的25%由风力涡轮机提取 % 消散。
{"title":"From gigawatt to multi-gigawatt wind farms: wake effects, energy budgets and inertial gravity waves investigated by large-eddy simulations","authors":"Oliver Maas","doi":"10.5194/wes-8-535-2023","DOIUrl":"https://doi.org/10.5194/wes-8-535-2023","url":null,"abstract":"Abstract. The size of newly installed offshore wind farms increases rapidly. Planned offshore wind farm clusters have a rated capacity of several gigawatts and a length of up to 100 km.\u0000The flow through and around wind farms of this scale can be significantly different than the flow through and around smaller wind farms on the sub-gigawatt scale. A good understanding of the involved flow physics is vital for accurately predicting the wind farm power output as well as predicting the meteorological conditions in the wind farm wake. To date there is no study that directly compares small wind farms (sub-gigawatt) with large wind farms (super-gigawatt) in terms of flow effects or power output. The aim of this study is to fill this gap by providing this direct comparison by performing large-eddy simulations of a small wind farm (13 km length) and a large wind farm (90 km length) in a convective boundary layer, which is the most common boundary layer type in the North Sea. The results show that there are significant differences in the flow field and the energy budgets of the small and large wind farm.\u0000The large wind farm triggers an inertial wave with a wind direction amplitude of approximately 10∘ and a wind speed amplitude of more than 1 m s−1. In a certain region in the far wake of a large wind farm the wind speed is greater than far upstream of the wind farm, which can be beneficial for a downstream located wind farm. The inertial wave also exists for the small wind farm, but the amplitudes are approximately 4 times weaker and thus may be hardly observable in real wind farm flows that are more heterogeneous. Regarding turbulence intensity, the wake of the large wind farm has the same length as the wake of the small wind farm and is only a few kilometers long.\u0000Both wind farms trigger inertial gravity waves in the free atmosphere, whereas the amplitude is approximately twice as large for the large wind farm. The inertial gravity waves induce streamwise pressure gradients inside the boundary layer, affecting the energy budgets of the wind farms.\u0000The most dominant energy source of the small wind farm is the horizontal advection of kinetic energy, but for the large wind farm the vertical turbulent flux of kinetic energy is 5 times greater than the horizontal advection of kinetic energy. The energy input by the gravity-wave-induced pressure gradient is greater for the small wind farm because the pressure gradient is greater. For the large wind farm, the energy input by the geostrophic forcing (synoptic-scale pressure gradient) is significantly enhanced by the wind direction change that is related to the inertial oscillation. For both wind farms approximately 75 % of the total available energy is extracted by the wind turbines and 25 % is dissipated.\u0000","PeriodicalId":46540,"journal":{"name":"Wind Energy Science","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44725011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract. The demand on wind energy for power generation will increase significantly in the next decade due to the transformation towards renewable energy production. In order to optimize the power generation of a wind farm, it is crucial to understand the flow in the wind turbine wake. The flow in the near wake close to downstream of the wind turbine (WT) is complex and highly three-dimensional. In the present study, for the first time, the SWUF-3D (Simultaneous Wind measurement with Unmanned Flight Systems in 3D) fleet of multirotor UASs (uncrewed aerial systems) is deployed for field measurements on an operating 2 MW WT in complex terrain. The UAS fleet has the potential to fill the meteorological gap of observations in the near wake with high-temporal- and high-spatial-resolution wind vector measurements plus temperature, humidity and pressure. During the experiment, the flow up- and downstream of the WT is measured simultaneously. Various flight patterns are used to investigate the near wake of the WT. The velocity deficit and the turbulence profile at different downstream distances are measured by distributed UASs which are aligned perpendicular to the flow in the near wake. The results show the expected double-Gaussian shape in the near wake under nearly stable atmospheric conditions. However, measurements in unstable atmospheric conditions with high turbulence intensity levels lead to single-Gaussian-like profiles at equal downstream distances (<1 D). Additionally, horizontal momentum fluxes and turbulence spectra are analyzed. The turbulence spectra of the wind measurement at the edge of the wake could reveal that tip vortices can be observed with the UASs.
{"title":"Multi-point in situ measurements of turbulent flow in a wind turbine wake and inflow with a fleet of uncrewed aerial systems","authors":"","doi":"10.5194/wes-8-515-2023","DOIUrl":"https://doi.org/10.5194/wes-8-515-2023","url":null,"abstract":"Abstract. The demand on wind energy for power generation will increase significantly in the next decade due to the transformation towards renewable energy production. In order to optimize the power generation of a wind farm, it is crucial to understand the flow in the wind turbine wake. The flow in the near wake close to downstream of the wind turbine (WT) is complex and highly three-dimensional. In the present study, for the first time, the SWUF-3D (Simultaneous Wind measurement with Unmanned Flight Systems in 3D) fleet of multirotor UASs (uncrewed aerial systems) is deployed for field measurements on an operating 2 MW WT in complex terrain. The UAS fleet has the potential to fill the meteorological gap of observations in the near wake with high-temporal- and high-spatial-resolution wind vector measurements plus temperature, humidity and pressure. During the experiment, the flow up- and downstream of the WT is measured simultaneously. Various flight patterns are used to investigate the near wake of the WT. The velocity deficit and the turbulence profile at different downstream distances are measured by distributed UASs which are aligned perpendicular to the flow in the near wake. The results show the expected double-Gaussian shape in the near wake under nearly stable atmospheric conditions. However, measurements in unstable atmospheric conditions with high turbulence intensity levels lead to single-Gaussian-like profiles at equal downstream distances (<1 D). Additionally, horizontal momentum fluxes and turbulence spectra are analyzed. The turbulence spectra of the wind measurement at the edge of the wake could reveal that tip vortices can be observed with the UASs.","PeriodicalId":46540,"journal":{"name":"Wind Energy Science","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42854134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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