Manuel Pusch, David Stockhouse, N. Abbas, Mandar Phadnis, Lucy Pao
A versatile framework is introduced for determining optimal steady‐state operating points for wind turbine control. The framework is based on solving constrained optimization problems at fixed wind speeds and allows for systematically studying required trade‐offs and parameter sensitivities. It can be used as a basis for many control approaches, for example, to automatically compute optimal schedules for control inputs, steady‐state operating points for model linearization, or reference values for tracking. Steady‐state simulation results are obtained using full nonlinear models to consider complex effects caused by couplings from aerodynamics, structural dynamics, and possibly also hydrodynamics in the case of floating wind turbines. Focusing only on the steady‐state response allows a fast and numerically robust optimization, which makes it especially attractive for co‐design studies. The effectiveness of the framework is demonstrated on two offshore extreme‐scale wind turbines, one floating and one fixed bottom.
{"title":"Optimal operating points for wind turbine control and co‐design","authors":"Manuel Pusch, David Stockhouse, N. Abbas, Mandar Phadnis, Lucy Pao","doi":"10.1002/we.2879","DOIUrl":"https://doi.org/10.1002/we.2879","url":null,"abstract":"A versatile framework is introduced for determining optimal steady‐state operating points for wind turbine control. The framework is based on solving constrained optimization problems at fixed wind speeds and allows for systematically studying required trade‐offs and parameter sensitivities. It can be used as a basis for many control approaches, for example, to automatically compute optimal schedules for control inputs, steady‐state operating points for model linearization, or reference values for tracking. Steady‐state simulation results are obtained using full nonlinear models to consider complex effects caused by couplings from aerodynamics, structural dynamics, and possibly also hydrodynamics in the case of floating wind turbines. Focusing only on the steady‐state response allows a fast and numerically robust optimization, which makes it especially attractive for co‐design studies. The effectiveness of the framework is demonstrated on two offshore extreme‐scale wind turbines, one floating and one fixed bottom.","PeriodicalId":23689,"journal":{"name":"Wind Energy","volume":"28 23","pages":""},"PeriodicalIF":4.1,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138955241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Seyed Ataollah Ashrafzadeh, A. Ghadimi, Ali Jabbari, M. R. Miveh
Air‐cored axial‐flux permanent‐magnet synchronous generators (AFPMSGs) are potential candidates for gearless direct‐coupled wind turbines (DCWTs) owing to providing high efficiency and power density. The design of a DCWT generator is so complicated since the generator cost, dimension, and weight affected by gear elimination. Therefore, it is essential to find an optimal AFPMSG design at rated conditions. In this paper, an accurate procedure for the optimal design of an air‐cored AFPMSG applicable for DCWTs is proposed. The genetic algorithm (GA) is used for multi‐objective design optimization to reach the optimal configuration as well as system dimension in order to decrease the weight, increase the power density and enhance the effectiveness of the generator. To validate the efficiency of the suggested optimization proceducer, a 30 kW AFPMSG has been considered as a case study. The results of optimization have been investigated by finite element analysis (FEA). A prototype generator is also fabricated, and the test results are offered and compared with the numerical study. The outcomes show that there exists an acceptable agreement between FEA and experimental outcomes with the error percentage about of 1.35%.
{"title":"Optimal design of a modular axial‐flux permanent‐magnet synchronous generator for gearless wind turbine applications","authors":"Seyed Ataollah Ashrafzadeh, A. Ghadimi, Ali Jabbari, M. R. Miveh","doi":"10.1002/we.2887","DOIUrl":"https://doi.org/10.1002/we.2887","url":null,"abstract":"Air‐cored axial‐flux permanent‐magnet synchronous generators (AFPMSGs) are potential candidates for gearless direct‐coupled wind turbines (DCWTs) owing to providing high efficiency and power density. The design of a DCWT generator is so complicated since the generator cost, dimension, and weight affected by gear elimination. Therefore, it is essential to find an optimal AFPMSG design at rated conditions. In this paper, an accurate procedure for the optimal design of an air‐cored AFPMSG applicable for DCWTs is proposed. The genetic algorithm (GA) is used for multi‐objective design optimization to reach the optimal configuration as well as system dimension in order to decrease the weight, increase the power density and enhance the effectiveness of the generator. To validate the efficiency of the suggested optimization proceducer, a 30 kW AFPMSG has been considered as a case study. The results of optimization have been investigated by finite element analysis (FEA). A prototype generator is also fabricated, and the test results are offered and compared with the numerical study. The outcomes show that there exists an acceptable agreement between FEA and experimental outcomes with the error percentage about of 1.35%.","PeriodicalId":23689,"journal":{"name":"Wind Energy","volume":"25 22","pages":""},"PeriodicalIF":4.1,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139000729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Sadman Sakib, D. Todd Griffith, Sanower Hossain, Saeid Bayat, James T. Allison
The wind energy market is currently dominated by horizontal axis wind turbines (HAWTs); however, vertical axis wind turbines (VAWTs) are emerging as a design alternative, especially for deep‐water offshore siting due to their low center of gravity, ease of access to drivetrain components, and overall simplicity. Due to the absence of a pitch mechanism in large‐scale Darrieus VAWTs, stall control has often been used to manage power and loads. Introducing a pitching mechanism in H‐type VAWTs has been studied, but this diminishes the mechanical simplicity advantage, and the use of a pitching mechanism in a large‐scale Darrieus‐type VAWT is not practical. This work examines an innovative, alternative method to control the rotor dynamics of a large‐scale 5 MW VAWT to maximize power while constraining loads without introducing any new or complex mechanical elements. This control strategy is termed intracycle revolution per minute (RPM) control, where the rotational speed of the turbine is allowed to vary in an optimal fashion with the azimuthal location of blades as opposed to typical constant RPM operation. An optimization framework is formulated for an open‐loop optimal control problem and solved to maximize power subject to constraints on aerodynamic design loads. Results are presented to demonstrate the benefits and the performance limits of intracycle RPM control for large‐scale 5 MW Darrieus VAWTs, namely, (1) power production (quantified in terms of AEP) that can be increased subject to baseline load limits and (2) opportunities to significantly increase AEP or decrease loads via intracycle RPM control that are examined for both two‐bladed and three‐bladed VAWTs.
{"title":"Intracycle RPM control for vertical axis wind turbines","authors":"M. Sadman Sakib, D. Todd Griffith, Sanower Hossain, Saeid Bayat, James T. Allison","doi":"10.1002/we.2885","DOIUrl":"https://doi.org/10.1002/we.2885","url":null,"abstract":"The wind energy market is currently dominated by horizontal axis wind turbines (HAWTs); however, vertical axis wind turbines (VAWTs) are emerging as a design alternative, especially for deep‐water offshore siting due to their low center of gravity, ease of access to drivetrain components, and overall simplicity. Due to the absence of a pitch mechanism in large‐scale Darrieus VAWTs, stall control has often been used to manage power and loads. Introducing a pitching mechanism in H‐type VAWTs has been studied, but this diminishes the mechanical simplicity advantage, and the use of a pitching mechanism in a large‐scale Darrieus‐type VAWT is not practical. This work examines an innovative, alternative method to control the rotor dynamics of a large‐scale 5 MW VAWT to maximize power while constraining loads without introducing any new or complex mechanical elements. This control strategy is termed intracycle revolution per minute (RPM) control, where the rotational speed of the turbine is allowed to vary in an optimal fashion with the azimuthal location of blades as opposed to typical constant RPM operation. An optimization framework is formulated for an open‐loop optimal control problem and solved to maximize power subject to constraints on aerodynamic design loads. Results are presented to demonstrate the benefits and the performance limits of intracycle RPM control for large‐scale 5 MW Darrieus VAWTs, namely, (1) power production (quantified in terms of AEP) that can be increased subject to baseline load limits and (2) opportunities to significantly increase AEP or decrease loads via intracycle RPM control that are examined for both two‐bladed and three‐bladed VAWTs.","PeriodicalId":23689,"journal":{"name":"Wind Energy","volume":"30 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138972345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Turbulent wind fields are known to be a major driver for structural loads and power fluctuations on offshore wind turbines. At the single‐turbine scale, there exist well‐established design standards based on wind spectra and coherence functions calibrated from years of measurements, which are used to generate multiple 10‐min wind field realisations known as synthetic turbulence boxes, themselves used as input to turbine‐scale aero‐hydro‐servo elastic codes. These methods are however not directly applicable at farm scale. When analysing the dynamics of large offshore wind farms, measurements reveal the importance of large, low‐frequency turbulent vortices for power fluctuations and hence for wind farm control and grid integration. Also, farm‐scale wind fields are needed as input to farm‐scale aero‐servo‐elastic codes for the modelling of wake dynamics, affecting structural loads. These new concerns motivate an upgrade in the original turbine‐scale wind field representation: (1) spectral models need to be based on farm‐scale measurements, (2) the frozen‐turbulence assumption merging temporal and along‐wind coherence must be lifted, (3) simplifications are needed to reduce the number of degrees of freedom as the domain becomes excessively large. This paper suggests models and algorithms for aggregated farm‐wide corrrelated synthetic turbulence generation—lumping the wind field into space‐averaged quantities—adapted to the aero‐hydro‐servo elastic modelling of large offshore wind farms. Starting from the work of Sørensen et al. in the early 2000s for grid integration purposes, methods for structural load modelling (through wake meandering and high‐resolution wind field reconstruction) are introduced. Implementation and efficiency matters involving mathematical subtleties are then presented. Finally, numerical experiments are carried out to (1) verify the approach and implementation against a state‐of‐the‐art point‐based—as opposite to aggregated—synthetic turbulence generation code and (2) illustrate the benefit of turbulence aggregation for the modelling of large offshore wind farms.
{"title":"Synthetic turbulence modelling for offshore wind farm engineering models using coherence aggregation","authors":"Valentin Chabaud","doi":"10.1002/we.2875","DOIUrl":"https://doi.org/10.1002/we.2875","url":null,"abstract":"Turbulent wind fields are known to be a major driver for structural loads and power fluctuations on offshore wind turbines. At the single‐turbine scale, there exist well‐established design standards based on wind spectra and coherence functions calibrated from years of measurements, which are used to generate multiple 10‐min wind field realisations known as synthetic turbulence boxes, themselves used as input to turbine‐scale aero‐hydro‐servo elastic codes. These methods are however not directly applicable at farm scale. When analysing the dynamics of large offshore wind farms, measurements reveal the importance of large, low‐frequency turbulent vortices for power fluctuations and hence for wind farm control and grid integration. Also, farm‐scale wind fields are needed as input to farm‐scale aero‐servo‐elastic codes for the modelling of wake dynamics, affecting structural loads. These new concerns motivate an upgrade in the original turbine‐scale wind field representation: (1) spectral models need to be based on farm‐scale measurements, (2) the frozen‐turbulence assumption merging temporal and along‐wind coherence must be lifted, (3) simplifications are needed to reduce the number of degrees of freedom as the domain becomes excessively large. This paper suggests models and algorithms for aggregated farm‐wide corrrelated synthetic turbulence generation—lumping the wind field into space‐averaged quantities—adapted to the aero‐hydro‐servo elastic modelling of large offshore wind farms. Starting from the work of Sørensen et al. in the early 2000s for grid integration purposes, methods for structural load modelling (through wake meandering and high‐resolution wind field reconstruction) are introduced. Implementation and efficiency matters involving mathematical subtleties are then presented. Finally, numerical experiments are carried out to (1) verify the approach and implementation against a state‐of‐the‐art point‐based—as opposite to aggregated—synthetic turbulence generation code and (2) illustrate the benefit of turbulence aggregation for the modelling of large offshore wind farms.","PeriodicalId":23689,"journal":{"name":"Wind Energy","volume":"24 3","pages":""},"PeriodicalIF":4.1,"publicationDate":"2023-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139003362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Gerard V. Ryan, Thomas A. A. Adcock, Ross A. McAdam
While recent numerical modelling advances have enabled robust simulation of foundation hysteresis behaviour, uptake of these models has been limited in the offshore wind industry. This is partially due to modelling complexity and the unknown influence of including such soil constitutive models within a design philosophy. This paper addresses this issue by outlining a framework of an aero‐hydro‐servo‐elastic offshore wind turbine model that is fully coupled with a multisurface plasticity 1D Winkler foundation model. Comparisons between this model and industry standard aeroelastic tools, such as OpenFAST, are shown to be in good agreement. The hysteretic soil predictions are also shown to be in good agreement with CM6 Cowden PISA test piles, in terms of secant stiffness and loop shape. This tool has then been used to address the unknown influence of hysteretic soil reactions on the design of monopile supported offshore wind turbines against extreme conditions. This study demonstrates that a significant reduction in ultimate and service limit state utilization is observed when a multisurface plasticity foundation model is adopted, as opposed to industry standard pile–soil interaction models.
{"title":"Influence of soil plasticity models on offshore wind turbine response","authors":"Gerard V. Ryan, Thomas A. A. Adcock, Ross A. McAdam","doi":"10.1002/we.2876","DOIUrl":"https://doi.org/10.1002/we.2876","url":null,"abstract":"While recent numerical modelling advances have enabled robust simulation of foundation hysteresis behaviour, uptake of these models has been limited in the offshore wind industry. This is partially due to modelling complexity and the unknown influence of including such soil constitutive models within a design philosophy. This paper addresses this issue by outlining a framework of an aero‐hydro‐servo‐elastic offshore wind turbine model that is fully coupled with a multisurface plasticity 1D Winkler foundation model. Comparisons between this model and industry standard aeroelastic tools, such as OpenFAST, are shown to be in good agreement. The hysteretic soil predictions are also shown to be in good agreement with CM6 Cowden PISA test piles, in terms of secant stiffness and loop shape. This tool has then been used to address the unknown influence of hysteretic soil reactions on the design of monopile supported offshore wind turbines against extreme conditions. This study demonstrates that a significant reduction in ultimate and service limit state utilization is observed when a multisurface plasticity foundation model is adopted, as opposed to industry standard pile–soil interaction models.","PeriodicalId":23689,"journal":{"name":"Wind Energy","volume":"99 33","pages":""},"PeriodicalIF":4.1,"publicationDate":"2023-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138605600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Stephen Guth, Eirini Katsidoniotaki, Themistoklis P. Sapsis
Abstract Accurately determining hydrodynamic force statistics is crucial for designing offshore engineering structures, including offshore wind turbine foundations, due to the significant impact of nonlinear wave–structure interactions. However, obtaining precise load statistics often involves computationally intensive simulations. Furthermore, the estimation of statistics using current practices is subject to ongoing discussion due to the inherent uncertainty involved. To address these challenges, we present a novel machine learning framework that leverages data‐driven surrogate modeling to predict hydrodynamic loads on monopile foundations while reducing reliance on costly simulations and facilitate the load statistics reconstruction. The primary advantage of our approach is the significant reduction in evaluation time compared to traditional modeling methods. The novelty of our framework lies in its efficient construction of the surrogate model, utilizing the Gaussian process regression machine learning technique and a Bayesian active learning method to sequentially sample wave episodes that contribute to accurate predictions of extreme hydrodynamic forces. Additionally, a spectrum transfer technique combines computational fluid dynamics (CFD) results from both quiescent and extreme waves, further reducing data requirements. This study focuses on reducing the dimensionality of stochastic irregular wave episodes and their associated hydrodynamic force time series. Although the dimensionality reduction is linear, Gaussian process regression successfully captures high‐order correlations. Furthermore, our framework incorporates built‐in uncertainty quantification capabilities, facilitating efficient parameter sampling using traditional CFD tools. This paper provides comprehensive implementation details and demonstrates the effectiveness of our approach in delivering reliable statistics for hydrodynamic loads while overcoming the computational cost constraints associated with classical modeling methods.
{"title":"Statistical modeling of fully nonlinear hydrodynamic loads on offshore wind turbine monopile foundations using wave episodes and targeted CFD simulations through active sampling","authors":"Stephen Guth, Eirini Katsidoniotaki, Themistoklis P. Sapsis","doi":"10.1002/we.2880","DOIUrl":"https://doi.org/10.1002/we.2880","url":null,"abstract":"Abstract Accurately determining hydrodynamic force statistics is crucial for designing offshore engineering structures, including offshore wind turbine foundations, due to the significant impact of nonlinear wave–structure interactions. However, obtaining precise load statistics often involves computationally intensive simulations. Furthermore, the estimation of statistics using current practices is subject to ongoing discussion due to the inherent uncertainty involved. To address these challenges, we present a novel machine learning framework that leverages data‐driven surrogate modeling to predict hydrodynamic loads on monopile foundations while reducing reliance on costly simulations and facilitate the load statistics reconstruction. The primary advantage of our approach is the significant reduction in evaluation time compared to traditional modeling methods. The novelty of our framework lies in its efficient construction of the surrogate model, utilizing the Gaussian process regression machine learning technique and a Bayesian active learning method to sequentially sample wave episodes that contribute to accurate predictions of extreme hydrodynamic forces. Additionally, a spectrum transfer technique combines computational fluid dynamics (CFD) results from both quiescent and extreme waves, further reducing data requirements. This study focuses on reducing the dimensionality of stochastic irregular wave episodes and their associated hydrodynamic force time series. Although the dimensionality reduction is linear, Gaussian process regression successfully captures high‐order correlations. Furthermore, our framework incorporates built‐in uncertainty quantification capabilities, facilitating efficient parameter sampling using traditional CFD tools. This paper provides comprehensive implementation details and demonstrates the effectiveness of our approach in delivering reliable statistics for hydrodynamic loads while overcoming the computational cost constraints associated with classical modeling methods.","PeriodicalId":23689,"journal":{"name":"Wind Energy","volume":"37 15","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134993145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pawel Flaszyński, Filip Wasilczuk, Michal Piotrowicz, Janusz Telega, Karol Mitraszewski, Kurt Schaldemose Hansen
Abstract The paper presents a study of the upstream influence of wind farms on the wind speed, which is called blockage effect. A Reynolds Averaged Navier–Stokes (RANS) numerical model using an actuator disc method was devised and validated using the SCADA data from a Horns Rev 1 wind farm. The maximum difference between the average power in the first row for SCADA and the numerical model was 7.8%. The model was used to determine the impact of blockage effect on the wind farm parameters and the extent to which the wind speed and the power generation were reduced. A reference wind farm was defined, with a modified size, spacing, turbine height, and diameter that were used for comparison with other wind farm configurations. The results of the investigation of the wind farm parameter effects on the upstream wind speed reduction are presented in the paper. It has been established that increasing the turbine spacing from 5D to 6.7D reduces the power loss due to blockage by two. Blockage losses are almost eliminated when the spacing is increased two times. Similarly, the wind turbine thrust coefficient (C T ) has a large impact on blockage, which is more pronounced, when C T is higher. In fact, the velocity deficit due to blockage is proportional to C T . The turbine tower height has small impact on blockage effect—the power reduction was changed by 0.3% due to blockage for the investigated range. The number of turbines in a row (with a constant number of turbines in a row) does not affect blockage significantly.
{"title":"Numerical simulations for a parametric study of blockage effect on offshore wind farms","authors":"Pawel Flaszyński, Filip Wasilczuk, Michal Piotrowicz, Janusz Telega, Karol Mitraszewski, Kurt Schaldemose Hansen","doi":"10.1002/we.2878","DOIUrl":"https://doi.org/10.1002/we.2878","url":null,"abstract":"Abstract The paper presents a study of the upstream influence of wind farms on the wind speed, which is called blockage effect. A Reynolds Averaged Navier–Stokes (RANS) numerical model using an actuator disc method was devised and validated using the SCADA data from a Horns Rev 1 wind farm. The maximum difference between the average power in the first row for SCADA and the numerical model was 7.8%. The model was used to determine the impact of blockage effect on the wind farm parameters and the extent to which the wind speed and the power generation were reduced. A reference wind farm was defined, with a modified size, spacing, turbine height, and diameter that were used for comparison with other wind farm configurations. The results of the investigation of the wind farm parameter effects on the upstream wind speed reduction are presented in the paper. It has been established that increasing the turbine spacing from 5D to 6.7D reduces the power loss due to blockage by two. Blockage losses are almost eliminated when the spacing is increased two times. Similarly, the wind turbine thrust coefficient (C T ) has a large impact on blockage, which is more pronounced, when C T is higher. In fact, the velocity deficit due to blockage is proportional to C T . The turbine tower height has small impact on blockage effect—the power reduction was changed by 0.3% due to blockage for the investigated range. The number of turbines in a row (with a constant number of turbines in a row) does not affect blockage significantly.","PeriodicalId":23689,"journal":{"name":"Wind Energy","volume":" 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135242873","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Flow modifications induced by wind turbine rotors on the incoming atmospheric boundary layer (ABL), such as blockage and speedups, can be important factors affecting the power performance and annual energy production (AEP) of a wind farm. Further, these rotor‐induced effects on the incoming ABL can vary significantly with the characteristics of the incoming wind, such as wind shear, veer, and turbulence intensity, and turbine operative conditions. To better characterize the complex flow physics underpinning the interaction between turbine rotors and the ABL, a field campaign was performed by deploying profiling wind LiDARs both before and after the construction of an onshore wind turbine array. Considering that the magnitude of these rotor‐induced flow modifications represents a small percentage of the incoming wind speed ( ), high accuracy needs to be achieved for the analysis of the experimental data and generation of flow predictions. Further, flow distortions induced by the site topography and effects of the local climatology need to be quantified and differentiated from those induced by wind turbine rotors. To this aim, a suite of statistical and machine learning models, such as k‐means cluster analysis coupled with random forest predictions, are used to quantify and predict flow modifications for different wind and atmospheric conditions. The experimental results show that wind velocity reductions of up to 3% can be observed at an upstream distance of 1.5 rotor diameter from the leading wind turbine rotor, with more significant effects occurring for larger positive wind shear. For more complex wind conditions, such as negative shear and low‐level jet, the rotor induction becomes highly complex entailing either velocity reductions (down to 9%) below hub height and velocity increases (up to 3%) above hub height. The effects of the rotor induction on the incoming wind velocity field seem to be already roughly negligible at an upstream distance of three rotor diameters. The results from this field experiment will inform models to simulate wind‐turbine and wind‐farm operations with improved accuracy for flow predictions in the proximity of the rotor area, which will be instrumental for more accurate quantification of wind farm blockage and relative effects on AEP.
{"title":"Profiling wind LiDAR measurements to quantify blockage for onshore wind turbines","authors":"Coleman Moss, Matteo Puccioni, Romit Maulik, Clément Jacquet, Dale Apgar, Giacomo Valerio Iungo","doi":"10.1002/we.2877","DOIUrl":"https://doi.org/10.1002/we.2877","url":null,"abstract":"Abstract Flow modifications induced by wind turbine rotors on the incoming atmospheric boundary layer (ABL), such as blockage and speedups, can be important factors affecting the power performance and annual energy production (AEP) of a wind farm. Further, these rotor‐induced effects on the incoming ABL can vary significantly with the characteristics of the incoming wind, such as wind shear, veer, and turbulence intensity, and turbine operative conditions. To better characterize the complex flow physics underpinning the interaction between turbine rotors and the ABL, a field campaign was performed by deploying profiling wind LiDARs both before and after the construction of an onshore wind turbine array. Considering that the magnitude of these rotor‐induced flow modifications represents a small percentage of the incoming wind speed ( ), high accuracy needs to be achieved for the analysis of the experimental data and generation of flow predictions. Further, flow distortions induced by the site topography and effects of the local climatology need to be quantified and differentiated from those induced by wind turbine rotors. To this aim, a suite of statistical and machine learning models, such as k‐means cluster analysis coupled with random forest predictions, are used to quantify and predict flow modifications for different wind and atmospheric conditions. The experimental results show that wind velocity reductions of up to 3% can be observed at an upstream distance of 1.5 rotor diameter from the leading wind turbine rotor, with more significant effects occurring for larger positive wind shear. For more complex wind conditions, such as negative shear and low‐level jet, the rotor induction becomes highly complex entailing either velocity reductions (down to 9%) below hub height and velocity increases (up to 3%) above hub height. The effects of the rotor induction on the incoming wind velocity field seem to be already roughly negligible at an upstream distance of three rotor diameters. The results from this field experiment will inform models to simulate wind‐turbine and wind‐farm operations with improved accuracy for flow predictions in the proximity of the rotor area, which will be instrumental for more accurate quantification of wind farm blockage and relative effects on AEP.","PeriodicalId":23689,"journal":{"name":"Wind Energy","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136381382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Coleman Moss, Romit Maulik, Patrick Moriarty, Giacomo Valerio Iungo
Abstract The power performance and the wind velocity field of an onshore wind farm are predicted with machine learning models and the pseudo‐2D RANS model, then assessed against SCADA data. The wind farm under investigation is one of the sites involved with the American WAKE experimeNt (AWAKEN). The performed simulations enable predictions of the power capture at the farm and turbine levels while providing insights into the effects on power capture associated with wake interactions that operating upstream turbines induce, as well as the variability caused by atmospheric stability. The machine learning models show improved accuracy compared to the pseudo‐2D RANS model in the predictions of turbine power capture and farm power capture with roughly half the normalized error. The machine learning models also entail lower computational costs upon training. Further, the machine learning models provide predictions of the wind turbulence intensity at the turbine level for different wind and atmospheric conditions with very good accuracy, which is difficult to achieve through RANS modeling. Additionally, farm‐to‐farm interactions are noted, with adverse impacts on power predictions from both models.
{"title":"Predicting wind farm operations with machine learning and the P2D‐RANS model: A case study for an AWAKEN site","authors":"Coleman Moss, Romit Maulik, Patrick Moriarty, Giacomo Valerio Iungo","doi":"10.1002/we.2874","DOIUrl":"https://doi.org/10.1002/we.2874","url":null,"abstract":"Abstract The power performance and the wind velocity field of an onshore wind farm are predicted with machine learning models and the pseudo‐2D RANS model, then assessed against SCADA data. The wind farm under investigation is one of the sites involved with the American WAKE experimeNt (AWAKEN). The performed simulations enable predictions of the power capture at the farm and turbine levels while providing insights into the effects on power capture associated with wake interactions that operating upstream turbines induce, as well as the variability caused by atmospheric stability. The machine learning models show improved accuracy compared to the pseudo‐2D RANS model in the predictions of turbine power capture and farm power capture with roughly half the normalized error. The machine learning models also entail lower computational costs upon training. Further, the machine learning models provide predictions of the wind turbulence intensity at the turbine level for different wind and atmospheric conditions with very good accuracy, which is difficult to achieve through RANS modeling. Additionally, farm‐to‐farm interactions are noted, with adverse impacts on power predictions from both models.","PeriodicalId":23689,"journal":{"name":"Wind Energy","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135405331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract With the evolution of renewable energies, many doubly fed induction generators (DFIGs) are being connected to the power grid, whose operation and grid‐connection stability have a major impact on the power grid. Currently, most studies focus on either modeling the mechanical–electrical section or the electrical‐grid section, and discussions have been limited to shaft oscillation or frequency coupling problems. In this study, a mechanical–electrical‐grid model of a DFIG was established to examine the impacts of wind speed and system control parameters on electrical damping and grid‐connection stability. The accuracy of the proposed model and validity of the analyses were verified using simulations. The following were observed: (1) In the case of changing wind speeds, the wind speed and the applied control model determine the shaft oscillation of DFIG, whereas the grid‐connected impedance on the rotor side is dependent on the wind speed. (2) At a constant wind speed, changes in control parameters under different control modes affect the dynamic characteristics of the drive train differently, whereas the grid‐connected impedance on the rotor side is primarily determined by the proportional gain of the inner/outer loop of the control system. The conclusions drawn from this study can further improve the safe and stable operation of DFIG wind power generation systems as well as their connection to the power grid.
{"title":"Mechanical–electrical‐grid model for the doubly fed induction generator wind turbine system considering oscillation frequency coupling characteristics","authors":"Zheng Wang, Yimin Lu","doi":"10.1002/we.2873","DOIUrl":"https://doi.org/10.1002/we.2873","url":null,"abstract":"Abstract With the evolution of renewable energies, many doubly fed induction generators (DFIGs) are being connected to the power grid, whose operation and grid‐connection stability have a major impact on the power grid. Currently, most studies focus on either modeling the mechanical–electrical section or the electrical‐grid section, and discussions have been limited to shaft oscillation or frequency coupling problems. In this study, a mechanical–electrical‐grid model of a DFIG was established to examine the impacts of wind speed and system control parameters on electrical damping and grid‐connection stability. The accuracy of the proposed model and validity of the analyses were verified using simulations. The following were observed: (1) In the case of changing wind speeds, the wind speed and the applied control model determine the shaft oscillation of DFIG, whereas the grid‐connected impedance on the rotor side is dependent on the wind speed. (2) At a constant wind speed, changes in control parameters under different control modes affect the dynamic characteristics of the drive train differently, whereas the grid‐connected impedance on the rotor side is primarily determined by the proportional gain of the inner/outer loop of the control system. The conclusions drawn from this study can further improve the safe and stable operation of DFIG wind power generation systems as well as their connection to the power grid.","PeriodicalId":23689,"journal":{"name":"Wind Energy","volume":"28 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135413029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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