Xavier Chesterman, Timothy Verstraeten, Pieter-Jan Daems, Ann Nowé, Jan Helsen
Abstract. Condition monitoring and failure prediction for wind turbines currently comprise a hot research topic. This follows from the fact that investments in the wind energy sector have increased dramatically due to the transition to renewable energy production. This paper reviews and implements several techniques from state-of-the-art research on condition monitoring for wind turbines using SCADA data and the normal behavior modeling framework. The first part of the paper consists of an in-depth overview of the current state of the art. In the second part, several techniques from the overview are implemented and compared using data (SCADA and failure data) from five operational wind farms. To this end, six demonstration experiments are designed. The first five experiments test different techniques for the modeling of normal behavior. The sixth experiment compares several techniques that can be used for identifying anomalous patterns in the prediction error. The selection of the tested techniques is driven by requirements from industrial partners, e.g., a limited number of training data and low training and maintenance costs of the models. The paper concludes with several directions for future work.
{"title":"Overview of normal behavior modeling approaches for SCADA-based wind turbine condition monitoring demonstrated on data from operational wind farms","authors":"Xavier Chesterman, Timothy Verstraeten, Pieter-Jan Daems, Ann Nowé, Jan Helsen","doi":"10.5194/wes-8-893-2023","DOIUrl":"https://doi.org/10.5194/wes-8-893-2023","url":null,"abstract":"Abstract. Condition monitoring and failure prediction for wind turbines currently comprise a hot research topic. This follows from the fact that investments in the wind energy sector have increased dramatically due to the transition to renewable energy production. This paper reviews and implements several techniques from state-of-the-art research on condition monitoring for wind turbines using SCADA data and the normal behavior modeling framework. The first part of the paper consists of an in-depth overview of the current state of the art. In the second part, several techniques from the overview are implemented and compared using data (SCADA and failure data) from five operational wind farms. To this end, six demonstration experiments are designed. The first five experiments test different techniques for the modeling of normal behavior. The sixth experiment compares several techniques that can be used for identifying anomalous patterns in the prediction error. The selection of the tested techniques is driven by requirements from industrial partners, e.g., a limited number of training data and low training and maintenance costs of the models. The paper concludes with several directions for future work.","PeriodicalId":46540,"journal":{"name":"Wind Energy Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135603320","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}
Jared J. Thomas, Nicholas F. Baker, P. Malisani, Erik Quaeghebeur, Sebastian Sanchez Perez-Moreno, John P. Jasa, C. Bay, F. Tilli, David Bieniek, N. Robinson, A. Stanley, Wesley Holt, A. Ning
Abstract. Selecting a wind farm layout optimization method is difficult. Comparisons between optimization methods in different papers can be uncertain due to the difficulty of exactly reproducing the objective function. Comparisons by just a few authors in one paper can be uncertain if the authors do not have experience using each algorithm. In this work we provide an algorithm comparison for a wind farm layout optimization case study between eight optimization methods applied, or directed, by researchers who developed those algorithms or who had other experience using them. We provided the objective function to each researcher to avoid ambiguity about relative performance due to a difference in objective function. While these comparisons are not perfect, we try to treat each algorithm more fairly by having researchers with experience using each algorithm apply each algorithm and by having a common objective function provided for analysis. The case study is from the International Energy Association (IEA) Wind Task 37, based on the Borssele III and IV wind farms with 81 turbines. Of particular interest in this case study is the presence of disconnected boundary regions and concave boundary features. The optimization methods studied represent a wide range of approaches, including gradient-free, gradient-based, and hybrid methods; discrete and continuous problem formulations; single-run and multi-start approaches; and mathematical and heuristic algorithms. We provide descriptions and references (where applicable) for each optimization method, as well as lists of pros and cons, to help readers determine an appropriate method for their use case. All the optimization methods perform similarly, with optimized wake loss values between 15.48 % and 15.70 % as compared to 17.28 % for the unoptimized provided layout. Each of the layouts found were different, but all layouts exhibited similar characteristics. Strong similarities across all the layouts include tightly packing wind turbines along the outer borders, loosely spacing turbines in the internal regions, and allocating similar numbers of turbines to each discrete boundary region. The best layout by annual energy production (AEP) was found using a new sequential allocation method, discrete exploration-based optimization (DEBO). Based on the results in this study, it appears that using an optimization algorithm can significantly improve wind farm performance, but there are many optimization methods that can perform well on the wind farm layout optimization problem, given that they are applied correctly.
{"title":"A comparison of eight optimization methods applied to a wind farm layout optimization problem","authors":"Jared J. Thomas, Nicholas F. Baker, P. Malisani, Erik Quaeghebeur, Sebastian Sanchez Perez-Moreno, John P. Jasa, C. Bay, F. Tilli, David Bieniek, N. Robinson, A. Stanley, Wesley Holt, A. Ning","doi":"10.5194/wes-8-865-2023","DOIUrl":"https://doi.org/10.5194/wes-8-865-2023","url":null,"abstract":"Abstract. Selecting a wind farm layout optimization method is difficult. Comparisons between optimization methods in different papers can be uncertain due to the difficulty of exactly reproducing the objective function. Comparisons by just a few authors in one paper can be uncertain if the authors do not have experience using each algorithm. In this work we provide an algorithm comparison for a wind farm layout optimization case study between eight optimization methods applied, or directed, by researchers who developed those algorithms or who had other experience using them. We provided the objective function to each researcher to avoid ambiguity about relative performance due to a difference in objective function. While these comparisons are not perfect, we try to treat each algorithm more fairly by having researchers with experience using each algorithm apply each algorithm and by having a common objective function provided for analysis. The case study is from the International Energy Association (IEA) Wind Task 37, based on the Borssele III and IV wind farms with 81 turbines. Of particular interest in this case study is the presence of disconnected boundary regions and concave boundary features. The optimization methods studied represent a wide range of approaches, including gradient-free, gradient-based, and hybrid methods; discrete and continuous problem formulations; single-run and multi-start approaches; and mathematical and heuristic algorithms. We provide descriptions and references (where applicable) for each optimization method, as well as lists of pros and cons, to help readers determine an appropriate method for their use case. All the optimization methods perform similarly, with optimized wake loss values between 15.48 % and 15.70 % as compared to 17.28 % for the unoptimized provided layout. Each of the layouts found were different, but all layouts exhibited similar characteristics. Strong similarities across all the layouts include tightly packing wind turbines along the outer borders, loosely spacing turbines in the internal regions, and allocating similar numbers of turbines to each discrete boundary region. The best layout by annual energy production (AEP) was found using a new sequential allocation method, discrete exploration-based optimization (DEBO). Based on the results in this study, it appears that using an optimization algorithm can significantly improve wind farm performance, but there are many optimization methods that can perform well on the wind farm layout optimization problem, given that they are applied correctly.\u0000","PeriodicalId":46540,"journal":{"name":"Wind Energy Science","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42725979","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}
Daniël Van Den Berg, D. De Tavernier, J. van Wingerden
Abstract. In recent years, control techniques such as dynamic induction control (often referred to as “the pulse”) have shown great potential in increasing wake mixing, with the goal of minimising turbine-to-turbine interaction within a wind farm. Dynamic induction control disturbs the wake by varying the thrust of the turbine over time, which results in a time-varying induction zone. If applied to a floating wind turbine, this time-varying thrust force will, besides changing the wake, change the motion of the platform. In light of the expected movement, this work investigates if applying the pulse to a floating wind turbine yields similar results to that of the pulse applied to bottom-fixed turbines. This is done by considering first the magnitude of motions of the floating wind turbine due to the application of a time-varying thrust force and secondly the effect of these motions on the wake mixing. A frequency response experiment shows that the movement of the floating turbine is heavily frequency dependent, as is the thrust force. Time domain simulations, using a free-wake vortex method with uniform inflow, show that the expected gain in average wind speed at a distance of 5 rotor diameters downstream is more sensitive to the excitation frequency compared to a bottom-fixed turbine with the same pulse applied. This is due to the fact that, at certain frequencies, platform motion decreases the thrust force variation and thus reduces the onset of wake mixing.
{"title":"The dynamic coupling between the pulse wake mixing strategy and floating wind turbines","authors":"Daniël Van Den Berg, D. De Tavernier, J. van Wingerden","doi":"10.5194/wes-8-849-2023","DOIUrl":"https://doi.org/10.5194/wes-8-849-2023","url":null,"abstract":"Abstract. In recent years, control techniques such as dynamic induction control (often referred to as “the pulse”) have shown great potential in increasing wake mixing, with the goal of minimising turbine-to-turbine interaction within a wind farm. Dynamic induction control disturbs the wake by varying the thrust of the turbine over time, which results in a time-varying induction zone. If applied to a floating wind turbine, this time-varying thrust force will, besides changing the wake, change the motion of the platform. In light of the expected movement, this work investigates if applying the pulse to a floating wind turbine yields similar results to that of the pulse applied to bottom-fixed turbines. This is done by considering first the magnitude of motions of the floating wind turbine due to the application of a time-varying thrust force and secondly the effect of these motions on the wake mixing. A frequency response experiment shows that the movement of the floating turbine is heavily frequency dependent, as is the thrust force. Time domain simulations, using a free-wake vortex method with uniform inflow, show that the expected gain in average wind speed at a distance of 5 rotor diameters downstream is more sensitive to the excitation frequency compared to a bottom-fixed turbine with the same pulse applied. This is due to the fact that, at certain frequencies, platform motion decreases the thrust force variation and thus reduces the onset of wake mixing.\u0000","PeriodicalId":46540,"journal":{"name":"Wind Energy Science","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43921020","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}
M. P. van der Laan, O. García-Santiago, M. Kelly, A. M. Meyer Forsting, C. Dubreuil-Boisclair, Knut Sponheim Seim, Marc Imberger, A. Peña, N. Sørensen, P. Réthoré
Abstract. Offshore wind farms are more commonly installed in wind farm clusters, where wind farm interaction can lead to energy losses; hence, there is a need for numerical models that can properly simulate wind farm interaction. This work proposes a Reynolds-averaged Navier–Stokes (RANS) method to efficiently simulate the effect of neighboring wind farms on wind farm power and annual energy production. First, a novel steady-state atmospheric inflow is proposed and tested for the application of RANS simulations of large wind farms. Second, a RANS-based wind farm parameterization is introduced, the actuator wind farm (AWF) model, which represents the wind farm as a forest canopy and allows to use of coarser grids compared to modeling all wind turbines as actuator disks (ADs). When the horizontal resolution of the RANS-AWF model is increased, the model results approach the results of the RANS-AD model. A double wind farm case is simulated with RANS to show that replacing an upstream wind farm with an AWF model only causes a deviation of less than 1 % in terms of the wind farm power of the downstream wind farm. Most importantly, a reduction in CPU hours of 75.1 % is achieved, provided that the AWF inputs are known, namely, wind farm thrust and power coefficients. The reduction in CPU hours is further reduced when all wind farms are represented by AWF models, namely, 92.3 % and 99.9 % for the double wind farm case and for a wind farm cluster case consisting of three wind farms, respectively. If the wind farm thrust and power coefficient inputs are derived from RANS-AD simulations, then the CPU time reduction is still 82.7 % for the wind farm cluster case. For the double wind farm case, the RANS models predict different wind speed flow fields compared to output from simulations performed with the mesoscale Weather Research and Forecasting model, but the models are in agreement with the inflow wind speed of the downstream wind farm. The RANS-AD-AWF model is also validated with measurements in terms of wind farm wake shape; the model captures the trend of the measurements for a wide range of wind directions, although the measurements indicate more pronounced wind farm wake shapes for certain wind directions.
{"title":"A new RANS-based wind farm parameterization and inflow model for wind farm cluster modeling","authors":"M. P. van der Laan, O. García-Santiago, M. Kelly, A. M. Meyer Forsting, C. Dubreuil-Boisclair, Knut Sponheim Seim, Marc Imberger, A. Peña, N. Sørensen, P. Réthoré","doi":"10.5194/wes-8-819-2023","DOIUrl":"https://doi.org/10.5194/wes-8-819-2023","url":null,"abstract":"Abstract. Offshore wind farms are more commonly installed in wind farm clusters, where wind farm interaction can lead to energy losses; hence, there is a need for numerical models that can properly simulate wind farm interaction.\u0000This work proposes a Reynolds-averaged Navier–Stokes (RANS) method to efficiently simulate the effect of neighboring wind farms on wind farm power and annual energy production. First, a novel steady-state atmospheric inflow is proposed and tested for the application of RANS simulations of large wind farms. Second, a RANS-based wind farm parameterization is introduced, the actuator wind farm (AWF) model, which represents the wind farm as a forest canopy and allows to use of coarser grids compared to modeling all wind turbines as actuator disks (ADs). When the horizontal resolution of the RANS-AWF model is increased, the model results approach the results of the RANS-AD model. A double wind farm case is simulated with RANS to show that replacing an upstream wind farm with an AWF model only causes a deviation of less than 1 % in terms of the wind farm power of the downstream wind farm. Most importantly, a reduction in CPU hours of 75.1 % is achieved, provided that the AWF inputs are known, namely, wind farm thrust and power coefficients. The reduction in CPU hours is further reduced when all wind farms are represented by AWF models, namely, 92.3 % and 99.9 % for the double wind farm case and for a wind farm cluster case consisting of three wind farms, respectively. If the wind farm thrust and power coefficient inputs are derived from RANS-AD simulations, then the CPU time reduction is still 82.7 % for the wind farm cluster case.\u0000For the double wind farm case, the RANS models predict different wind speed flow fields compared to output from simulations performed with the mesoscale Weather Research and Forecasting model, but the models are in agreement with the inflow wind speed of the downstream wind farm. The RANS-AD-AWF model is also validated with measurements in terms of wind farm wake shape; the model captures the trend of the measurements for a wide range of wind directions, although the measurements indicate more pronounced wind farm wake shapes for certain wind directions.\u0000","PeriodicalId":46540,"journal":{"name":"Wind Energy Science","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49102313","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 orientation of a three-legged offshore wind jacket structure in 60 m water depth, supporting the IEA 15 MW reference turbine, has been assessed for optimizing the jacket pile design. A reference site off the coast of Massachusetts was considered, including site-specific metocean conditions and realistically plausible geotechnical conditions. Soil–structure interaction was modeled using three-dimensional finite-element (FE) ground–structure simulations to obtain equivalent mudline springs, which were subsequently used in nonlinear elastic simulations, considering aerodynamic and hydrodynamic loading of extreme sea states in the time domain. Jacket pile loads were found to be sensitive to the maximum 50-year wave direction, as opposed to the wind direction, indicating that the jacket orientation should be considered relative to the dominant wave direction. The results further demonstrated that the jacket orientation has a substantial impact on the overall jacket pile mass and maximum pile embedment depth and therefore represents an important opportunity for project cost and risk reductions. Finally, this research highlights the importance of detailed knowledge of the full global model behavior (both turbine and foundation) for capturing this optimization potential, particularly due to the influence of wind–wave misalignment on pile loads. Close collaboration between the turbine supplier and foundation designer, at the appropriate design stages, is essential.
{"title":"Design optimization of offshore wind jacket piles by assessing support structure orientation relative to metocean conditions","authors":"Maciej M. Mroczek, S. Arwade, M. Lackner","doi":"10.5194/wes-8-807-2023","DOIUrl":"https://doi.org/10.5194/wes-8-807-2023","url":null,"abstract":"Abstract. The orientation of a three-legged offshore wind jacket structure in 60 m water depth, supporting the IEA 15 MW reference turbine, has been assessed for optimizing the jacket pile design. A reference site off the coast of Massachusetts was considered, including site-specific metocean conditions and realistically plausible geotechnical conditions. Soil–structure interaction was modeled using three-dimensional finite-element (FE) ground–structure simulations to obtain equivalent mudline springs, which were subsequently used in nonlinear elastic simulations, considering aerodynamic and hydrodynamic loading of extreme sea states in the time domain. Jacket pile loads were found to be sensitive to the maximum 50-year wave direction, as opposed to the wind direction, indicating that the jacket orientation should be considered relative to the dominant wave direction. The results further demonstrated that the jacket orientation has a substantial impact on the overall jacket pile mass and maximum pile embedment depth and therefore represents an important opportunity for project cost and risk reductions. Finally, this research highlights the importance of detailed knowledge of the full global model behavior (both turbine and foundation) for capturing this optimization potential, particularly due to the influence of wind–wave misalignment on pile loads. Close collaboration between the turbine supplier and foundation designer, at the appropriate design stages, is essential.\u0000","PeriodicalId":46540,"journal":{"name":"Wind Energy Science","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47738688","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}
P. Baas, R. Verzijlbergh, Pim van Dorp, Harm J. J. Jonker
Abstract. As a consequence of the rapid growth of the globally installed offshore wind energy capacity, the size of individual wind farms is increasing. This poses a challenge to models that predict energy production. For instance, the current generation of wake models has mostly been calibrated on existing wind farms of much smaller size. This work analyzes annual energy production and wake losses for future, multi-gigawatt wind farms with atmospheric large-eddy simulation. To that end, 1 year of actual weather has been simulated for a suite of hypothetical 4 GW offshore wind farm scenarios. The scenarios differ in terms of applied turbine type, installed capacity density, and layout. The results suggest that production numbers increase significantly when the rated power of the individual turbines is larger while keeping the total installed capacity the same. Even for turbine types with similar rated power but slightly different power curves, significant differences in production were found. Although wind speed was identified as the most dominant factor determining the aerodynamic losses, a clear impact of atmospheric stability and boundary layer height has been identified. By analyzing losses of the first-row turbines, the yearly average global-blockage effect is estimated to between 2 and 3 %, but it can reach levels over 10 % for stably stratified conditions and wind speeds around 8 m s−1. Using a high-fidelity modeling technique, the present work provides insights into the performance of future, multi-gigawatt wind farms for a full year of realistic weather conditions.
摘要由于全球海上风能装机容量的快速增长，单个风电场的规模正在增加。这对预测能源生产的模型提出了挑战。例如，当前一代的尾流模型大多是在现有规模小得多的风电场上进行校准的。这项工作通过大气大涡模拟分析了未来数十亿瓦风电场的年发电量和尾流损失。为此，我们模拟了一组假设的4年实际天气 GW海上风电场场景。应用的涡轮机类型、装机容量密度和布局不同。结果表明，在保持总装机容量不变的情况下，当单个涡轮机的额定功率较大时，产量显著增加。即使对于额定功率相似但功率曲线略有不同的涡轮机类型，也发现产量存在显著差异。尽管风速被确定为决定空气动力学损失的最主要因素，但已经确定了大气稳定性和边界层高度的明显影响。通过分析第一排涡轮机的损失，估计年平均全球阻塞效应在2到3之间 %, 但它可以达到10以上的水平 % 对于稳定的分层条件和大约8的风速 m s−1。利用高保真建模技术，本工作深入了解了未来数十亿瓦风电场在全年真实天气条件下的性能。
{"title":"Investigating energy production and wake losses of multi-gigawatt offshore wind farms with atmospheric large-eddy simulation","authors":"P. Baas, R. Verzijlbergh, Pim van Dorp, Harm J. J. Jonker","doi":"10.5194/wes-8-787-2023","DOIUrl":"https://doi.org/10.5194/wes-8-787-2023","url":null,"abstract":"Abstract. As a consequence of the rapid growth of the globally installed offshore wind energy capacity, the size of individual wind farms is increasing. This poses a challenge to models that predict energy production. For instance, the current generation of wake models has mostly been calibrated on existing wind farms of much smaller size. This work analyzes annual energy production and wake losses for future, multi-gigawatt wind farms with atmospheric large-eddy simulation. To that end, 1 year of actual weather has been simulated for a suite of hypothetical 4 GW offshore wind farm scenarios. The scenarios differ in terms of applied turbine type, installed capacity density, and layout. The results suggest that production numbers increase significantly when the rated power of the individual turbines is larger while keeping the total installed capacity the same. Even for turbine types with similar rated power but slightly different power curves, significant differences in production were found. Although wind speed was identified as the most dominant factor determining the aerodynamic losses, a clear impact of atmospheric stability and boundary layer height has been identified. By analyzing losses of the first-row turbines, the yearly average global-blockage effect is estimated to between 2 and 3 %, but it can reach levels over 10 % for stably stratified conditions and wind speeds around 8 m s−1. Using a high-fidelity modeling technique, the present work provides insights into the performance of future, multi-gigawatt wind farms for a full year of realistic weather conditions.\u0000","PeriodicalId":46540,"journal":{"name":"Wind Energy Science","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2023-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43505204","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}
Robin Marcille, M. Thiébaut, P. Tandeo, J. Filipot
Abstract. Wind resource assessment is a crucial step for the development of offshore wind energy. It relies on the installation of measurement devices, whose placement is an open challenge for developers. Indeed, the optimal sensor placement for field reconstruction is an open challenge in the field of sparse sampling. As for the application to offshore wind field reconstruction, no similar study was found, and standard strategies are based on semi-empirical choices. In this paper, a sparse sampling method using a Gaussian mixture model on numerical weather prediction data is developed for offshore wind reconstruction. It is applied to France's main offshore wind energy development areas: Normandy, southern Brittany and the Mediterranean Sea. The study is based on 3 years of Météo-France AROME's data, available through the MeteoNet data set. Using a Gaussian mixture model for data clustering, it leads to optimal sensor locations with regards to wind field reconstruction error. The proposed workflow is described and compared to state-of-the-art methods for sparse sampling. It constitutes a robust yet simple method for the definition of optimal sensor siting for offshore wind field reconstruction. The described method applied to the study area output sensor arrays of respectively seven, four and four sensors for Normandy, southern Brittany and the Mediterranean Sea. Those sensor arrays perform approximately 20 % better than the median Monte Carlo case and more than 30 % better than state-of-the-art methods with regards to wind field reconstruction error.
{"title":"Gaussian mixture models for the optimal sparse sampling of offshore wind resource","authors":"Robin Marcille, M. Thiébaut, P. Tandeo, J. Filipot","doi":"10.5194/wes-8-771-2023","DOIUrl":"https://doi.org/10.5194/wes-8-771-2023","url":null,"abstract":"Abstract. Wind resource assessment is a crucial step for the development of offshore wind energy. It relies on the installation of measurement devices, whose placement is an open challenge for developers. Indeed, the optimal sensor placement for field reconstruction is an open challenge in the field of sparse sampling. As for the application to offshore wind field reconstruction, no similar study was found, and standard strategies are based on semi-empirical choices. In this paper, a sparse sampling method using a Gaussian mixture model on numerical weather prediction data is developed for offshore wind reconstruction. It is applied to France's main offshore wind energy development areas: Normandy, southern Brittany and the Mediterranean Sea. The study is based on 3 years of Météo-France AROME's data, available through the MeteoNet data set. Using a Gaussian mixture model for data clustering, it leads to optimal sensor locations with regards to wind field reconstruction error. The proposed workflow is described and compared to state-of-the-art methods for sparse sampling. It constitutes a robust yet simple method for the definition of optimal sensor siting for offshore wind field reconstruction. The described method applied to the study area output sensor arrays of respectively seven, four and four sensors for Normandy, southern Brittany and the Mediterranean Sea. Those sensor arrays perform approximately 20 % better than the median Monte Carlo case and more than 30 % better than state-of-the-art methods with regards to wind field reconstruction error.\u0000","PeriodicalId":46540,"journal":{"name":"Wind Energy Science","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2023-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42596729","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. Data-driven wake models have recently shown a high accuracy in reproducing wake characteristics from numerical data sets. This study used wake measurements from a lidar-equipped commercial wind turbine and inflow measurements from a nearby meteorological mast to validate an interpretable data-driven surrogate wake model. The trained data-driven model was then compared to a state-of-the-art analytical wake model. A multi-plane lidar measurement strategy captured the occurrence of the wake curl during yaw misalignment, which had not yet conclusively been observed in the field. The comparison between the wake models showed that the available power estimations of a virtual turbine situated four rotor diameters downstream were significantly more accurate with the data-driven model than with the analytical model. The mean absolute percentage error was reduced by 19 % to 36 %, depending on the input variables used. Especially under turbine yaw misalignment and high vertical shear, the data-driven model performed better. Further analysis suggested that the accuracy of the data-driven model is hardly affected when using only supervisory control and data acquisition (SCADA) data as input. Although the results are only obtained for a single turbine type, downstream distance and range of yaw misalignments, the outcome of this study is believed to demonstrate the potential of data-driven wake models.
{"title":"Validation of an interpretable data-driven wake model using lidar measurements from a field wake steering experiment","authors":"B.A.M. Sengers, G. Steinfeld, P. Hulsman, M. Kühn","doi":"10.5194/wes-8-747-2023","DOIUrl":"https://doi.org/10.5194/wes-8-747-2023","url":null,"abstract":"Abstract. Data-driven wake models have recently shown a high accuracy in reproducing wake characteristics from numerical data sets. This study used wake measurements from a lidar-equipped commercial wind turbine and inflow measurements from a nearby meteorological mast to validate an interpretable data-driven surrogate wake model. The trained data-driven model was then compared to a state-of-the-art analytical wake model. A multi-plane lidar measurement strategy captured the occurrence of the wake curl during yaw misalignment, which had not yet conclusively been observed in the field. The comparison between the wake models showed that the available power estimations of a virtual turbine situated four rotor diameters downstream were significantly more accurate with the data-driven model than with the analytical model. The mean absolute percentage error was reduced by 19 % to 36 %, depending on the input variables used. Especially under turbine yaw misalignment and high vertical shear, the data-driven model performed better. Further analysis suggested that the accuracy of the data-driven model is hardly affected when using only supervisory control and data acquisition (SCADA) data as input. Although the results are only obtained for a single turbine type, downstream distance and range of yaw misalignments, the outcome of this study is believed to demonstrate the potential of data-driven wake models.\u0000","PeriodicalId":46540,"journal":{"name":"Wind Energy Science","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45014981","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. As the need to transition from global reliance on fossil fuels grows, solutions for producing green alternative fuels are necessary. These fuels will be especially important for hard-to-decarbonize sectors such as shipping. Mobile offshore wind energy systems (MOWESs) have been proposed as one such solution. These systems aim to harness the far-offshore wind resource, which is abundant and yet untapped because of installation and grid-connection limitations. Two classes of MOWES have been proposed in the literature: unmoored floating offshore wind turbines (UFOWTs) and energy ships (ESs). Both systems operate as autonomous power-to-X (PtX) plants, powered entirely by wind energy, and so can be used to produce synthetic green fuels such as hydrogen or ammonia, or for other energy-intensive applications such as direct air carbon capture. The two technologies differ in form; UFOWTs are based on a conventional FOWT but include propellers in place of mooring lines for course keeping, while ESs operate like a sailing ship and generate power via hydro-turbines mounted on the underside of the hull. Though much research and development is necessary for these systems to be feasible, the promise of harnessing strong winds far offshore, as well as the potential to avoid siting regulatory challenges, is enticing. This paper develops models of each MOWES concept to compare their power production on a consistent basis. The performance of the technologies is examined at steady-state operating points across relative wind speeds and angles. An optimization scheme is used to determine the values of the control variables which define the operating point for each set of environmental conditions. Results for each model show good agreement with published results for both UFOWTs and ESs. Model results suggest that UFOWTs can generate more power than ESs under ideal environmental conditions but are very sensitive to off-design operating conditions. In above-rated wind speeds, the UFOWT is able to produce as much power as a conventional, moored FOWT, whereas the ES cannot, since some power is always consumed to spin the Flettner rotors. The models developed here and their results may both be useful in future works that focus on the routing of UFOWTs or holistically designing a mobile UFOWT. Although differences in the performance of the systems have been identified, more work is necessary to discern which is a more viable producer of green electrofuels (e-fuels).
{"title":"Comparison of optimal power production and operation of unmoored floating offshore wind turbines and energy ships","authors":"Patrick Connolly, C. Crawford","doi":"10.5194/wes-8-725-2023","DOIUrl":"https://doi.org/10.5194/wes-8-725-2023","url":null,"abstract":"Abstract. As the need to transition from global reliance on fossil fuels grows, solutions for producing green alternative fuels are necessary.\u0000These fuels will be especially important for hard-to-decarbonize sectors such as shipping.\u0000Mobile offshore wind energy systems (MOWESs) have been proposed as one such solution.\u0000These systems aim to harness the far-offshore wind resource, which is abundant and yet untapped because of installation and grid-connection limitations.\u0000Two classes of MOWES have been proposed in the literature: unmoored floating offshore wind turbines (UFOWTs) and energy ships (ESs).\u0000Both systems operate as autonomous power-to-X (PtX) plants, powered entirely by wind energy, and so can be used to produce synthetic green fuels such as hydrogen or ammonia, or for other energy-intensive applications such as direct air carbon capture.\u0000The two technologies differ in form; UFOWTs are based on a conventional FOWT but include propellers in place of mooring lines for course keeping, while ESs operate like a sailing ship and generate power via hydro-turbines mounted on the underside of the hull.\u0000Though much research and development is necessary for these systems to be feasible, the promise of harnessing strong winds far offshore, as well as the potential to avoid siting regulatory challenges, is enticing. This paper develops models of each MOWES concept to compare their power production on a consistent basis.\u0000The performance of the technologies is examined at steady-state operating points across relative wind speeds and angles.\u0000An optimization scheme is used to determine the values of the control variables which define the operating point for each set of environmental conditions.\u0000Results for each model show good agreement with published results for both UFOWTs and ESs.\u0000Model results suggest that UFOWTs can generate more power than ESs under ideal environmental conditions but are very sensitive to off-design operating conditions.\u0000In above-rated wind speeds, the UFOWT is able to produce as much power as a conventional, moored FOWT, whereas the ES cannot, since some power is always consumed to spin the Flettner rotors.\u0000The models developed here and their results may both be useful in future works that focus on the routing of UFOWTs or holistically designing a mobile UFOWT.\u0000Although differences in the performance of the systems have been identified, more work is necessary to discern which is a more viable producer of green electrofuels (e-fuels).\u0000","PeriodicalId":46540,"journal":{"name":"Wind Energy Science","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47227948","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. This paper describes a method to identify the heterogenous flow characteristics that develop within a wind farm in its interaction with the atmospheric boundary layer. The whole farm is used as a distributed sensor, which gauges through its wind turbines the flow field developing within its boundaries. The proposed method is based on augmenting an engineering wake model with an unknown correction field, which results in a hybrid (grey-box) model. Operational SCADA (supervisory control and data acquisition) data are then used to simultaneously learn the parameters that describe the correction field and to tune the ones of the engineering wake model. The resulting monolithic maximum likelihood estimation is in general ill-conditioned because of the collinearity and low observability of the redundant parameters. This problem is solved by a singular value decomposition, which discards parameter combinations that are not identifiable given the informational content of the dataset and solves only for the identifiable ones. The farm-as-a-sensor approach is demonstrated on two wind plants with very different characteristics: a relatively small onshore farm at a site with moderate terrain complexity and a large offshore one in close proximity to the coastline. In both cases, the data-driven correction and tuning of the grey-box model results in much improved prediction capabilities. The identified flow fields reveal the presence of significant terrain-induced effects in the onshore case and of large direction and ambient-condition-dependent intra-plant effects in the offshore one. Analysis of the coordinate transformation and mode shapes generated by the singular value decomposition help explain relevant characteristics of the solution, as well as couplings among modeling parameters. Computational fluid dynamics (CFD) simulations are used for confirming the plausibility of the identified flow fields.
{"title":"The wind farm as a sensor: learning and explaining orographic and plant-induced flow heterogeneities from operational data","authors":"R. Braunbehrens, A. Vad, C. Bottasso","doi":"10.5194/wes-8-691-2023","DOIUrl":"https://doi.org/10.5194/wes-8-691-2023","url":null,"abstract":"Abstract. This paper describes a method to identify the heterogenous flow\u0000characteristics that develop within a wind farm in its interaction with the\u0000atmospheric boundary layer. The whole farm is used as a distributed sensor,\u0000which gauges through its wind turbines the flow field developing within\u0000its boundaries. The proposed method is based on augmenting an engineering\u0000wake model with an unknown correction field, which results in a hybrid\u0000(grey-box) model. Operational SCADA (supervisory control and data acquisition) data are then used to simultaneously learn the parameters that describe the correction field and to tune the ones of the engineering wake model. The resulting monolithic maximum likelihood estimation is in general ill-conditioned because of the collinearity and low observability of the redundant parameters. This problem is solved by a singular value decomposition, which discards parameter combinations that are not identifiable given the informational content of the dataset and solves only for the identifiable ones. The farm-as-a-sensor approach is demonstrated on two wind plants with very\u0000different characteristics: a relatively small onshore farm at a site with\u0000moderate terrain complexity and a large offshore one in close proximity to\u0000the coastline. In both cases, the data-driven correction and tuning of the\u0000grey-box model results in much improved prediction capabilities. The\u0000identified flow fields reveal the presence of significant terrain-induced\u0000effects in the onshore case and of large direction and ambient-condition-dependent intra-plant effects in the offshore one. Analysis of the coordinate transformation and mode shapes generated by the singular value decomposition help explain relevant characteristics of the solution, as well as couplings among modeling parameters. Computational fluid dynamics (CFD) simulations are used for confirming the plausibility of the identified flow fields.\u0000","PeriodicalId":46540,"journal":{"name":"Wind Energy Science","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46174633","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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