Feng Gao, Chen Qian, Lin Xu, Juncheng Liu, Hong Zhang
Bolt looseness may occur on wind turbine (WT) blades exposed to operational and environmental variability conditions, which sometimes can cause catastrophic consequences. Therefore, it is necessary to monitor the loosening state of WT blade root bolts. In order to solve this problem, this paper proposes a method to monitor the looseness of blade root bolts using the sensors installed on the WT blade. An experimental platform was first built by installing acceleration and strain sensors for monitoring bolt looseness. Through the physical experiment of blade root bolts' looseness, the response data of blade sensors is then obtained under different bolt looseness numbers and degrees. Afterwards, the sensor signal of the blade root bolts is analyzed in time domain, frequency domain, and time‐frequency domain, and the sensitivity features of various signals are extracted. So the eigenvalue category as the input of the state discrimination model was determined. The LightGBM (light gradient boosting machine) classification algorithm was applied to identify different bolt looseness states for the multi‐domain features. The impact of different combinations of sensor categories and quantities as the data source on the identification results is discussed, and a reference for the selection of sensors is provided. The proposed method can discriminate four bolt states at an accuracy of around 99.8% using 5‐fold cross‐validation.
{"title":"An experimental study on the identification of the root bolts' state of wind turbine blades using blade sensors","authors":"Feng Gao, Chen Qian, Lin Xu, Juncheng Liu, Hong Zhang","doi":"10.1002/we.2892","DOIUrl":"https://doi.org/10.1002/we.2892","url":null,"abstract":"Bolt looseness may occur on wind turbine (WT) blades exposed to operational and environmental variability conditions, which sometimes can cause catastrophic consequences. Therefore, it is necessary to monitor the loosening state of WT blade root bolts. In order to solve this problem, this paper proposes a method to monitor the looseness of blade root bolts using the sensors installed on the WT blade. An experimental platform was first built by installing acceleration and strain sensors for monitoring bolt looseness. Through the physical experiment of blade root bolts' looseness, the response data of blade sensors is then obtained under different bolt looseness numbers and degrees. Afterwards, the sensor signal of the blade root bolts is analyzed in time domain, frequency domain, and time‐frequency domain, and the sensitivity features of various signals are extracted. So the eigenvalue category as the input of the state discrimination model was determined. The LightGBM (light gradient boosting machine) classification algorithm was applied to identify different bolt looseness states for the multi‐domain features. The impact of different combinations of sensor categories and quantities as the data source on the identification results is discussed, and a reference for the selection of sensors is provided. The proposed method can discriminate four bolt states at an accuracy of around 99.8% using 5‐fold cross‐validation.","PeriodicalId":23689,"journal":{"name":"Wind Energy","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139600053","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}
Ashesh Sharma, Michael J. Brazell, Ganesh Vijayakumar, S. Ananthan, Lawrence Cheung, Nathaniel deVelder, Marc T. Henry de Frahan, Neil Matula, P. Mullowney, Jonathan S. Rood, Philip Sakievich, Ann Almgren, Paul S. Crozier, Michael Sprague
Predictive high‐fidelity modeling of wind turbines with computational fluid dynamics, wherein turbine geometry is resolved in an atmospheric boundary layer, is important to understanding complex flow accounting for design strategies and operational phenomena such as blade erosion, pitch‐control, stall/vortex‐induced vibrations, and aftermarket add‐ons. The biggest challenge with high‐fidelity modeling is the realization of numerical algorithms that can capture the relevant physics in detail through effective use of high‐performance computing. For modern supercomputers, that means relying on GPUs for acceleration. In this paper, we present ExaWind, a GPU‐enabled open‐source incompressible‐flow hybrid‐computational fluid dynamics framework, comprising the near‐body unstructured grid solver Nalu‐Wind, and the off‐body block‐structured‐grid solver AMR‐Wind, which are coupled using the Topology Independent Overset Grid Assembler. Turbine simulations employ either a pure Reynolds‐averaged Navier–Stokes turbulence model or hybrid turbulence modeling wherein Reynolds‐averaged Navier–Stokes is used for near‐body flow and large eddy simulation is used for off‐body flow. Being two‐way coupled through overset grids, the two solvers enable simulation of flows across a huge range of length scales, for example, 10 orders of magnitude going from O(μm) boundary layers along the blades to O(10 km) across a wind farm. In this paper, we describe the numerical algorithms for geometry‐resolved turbine simulations in atmospheric boundary layers using ExaWind. We present verification studies using canonical flow problems. Validation studies are presented using megawatt‐scale turbines established in literature. Additionally presented are demonstration simulations of a small wind farm under atmospheric inflow with different stability states.
{"title":"ExaWind: Open‐source CFD for hybrid‐RANS/LES geometry‐resolved wind turbine simulations in atmospheric flows","authors":"Ashesh Sharma, Michael J. Brazell, Ganesh Vijayakumar, S. Ananthan, Lawrence Cheung, Nathaniel deVelder, Marc T. Henry de Frahan, Neil Matula, P. Mullowney, Jonathan S. Rood, Philip Sakievich, Ann Almgren, Paul S. Crozier, Michael Sprague","doi":"10.1002/we.2886","DOIUrl":"https://doi.org/10.1002/we.2886","url":null,"abstract":"Predictive high‐fidelity modeling of wind turbines with computational fluid dynamics, wherein turbine geometry is resolved in an atmospheric boundary layer, is important to understanding complex flow accounting for design strategies and operational phenomena such as blade erosion, pitch‐control, stall/vortex‐induced vibrations, and aftermarket add‐ons. The biggest challenge with high‐fidelity modeling is the realization of numerical algorithms that can capture the relevant physics in detail through effective use of high‐performance computing. For modern supercomputers, that means relying on GPUs for acceleration. In this paper, we present ExaWind, a GPU‐enabled open‐source incompressible‐flow hybrid‐computational fluid dynamics framework, comprising the near‐body unstructured grid solver Nalu‐Wind, and the off‐body block‐structured‐grid solver AMR‐Wind, which are coupled using the Topology Independent Overset Grid Assembler. Turbine simulations employ either a pure Reynolds‐averaged Navier–Stokes turbulence model or hybrid turbulence modeling wherein Reynolds‐averaged Navier–Stokes is used for near‐body flow and large eddy simulation is used for off‐body flow. Being two‐way coupled through overset grids, the two solvers enable simulation of flows across a huge range of length scales, for example, 10 orders of magnitude going from O(μm) boundary layers along the blades to O(10 km) across a wind farm. In this paper, we describe the numerical algorithms for geometry‐resolved turbine simulations in atmospheric boundary layers using ExaWind. We present verification studies using canonical flow problems. Validation studies are presented using megawatt‐scale turbines established in literature. Additionally presented are demonstration simulations of a small wind farm under atmospheric inflow with different stability states.","PeriodicalId":23689,"journal":{"name":"Wind Energy","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139603915","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}
Huixue Dang, Guohua Xing, Hailong Wang, Dani Harmanto, Weigang Yao
This study proposes an empirical model for preliminary wind‐resist design of downburst flow. Existing empirical models were compared with field data and found to underpredict horizontal wind speed below the height corresponding to the maximum radial velocity, due to the neglect of viscous effects and the evolution of vertical wind profiles along radial direction. To address these deficiencies, semi‐empirical piecewise functions including wall shear effects in the local turbulent boundary layer and interpolation functions were proposed to improve the accuracy of existing models. The wind profile based on Coles' theory was found to agree well with field data, with the parabola interpolation function being the most desirable. Using the proposed method, the vertical profile of horizontal wind speed at different local radial locations can be predicted for wind resist design given the inlet wind speed of the downburst flow. Overall, this model improves upon existing empirical models and allows for more accurate wind‐resist design.
{"title":"A novel empirical model for vertical profiles of downburst horizontal wind speed","authors":"Huixue Dang, Guohua Xing, Hailong Wang, Dani Harmanto, Weigang Yao","doi":"10.1002/we.2895","DOIUrl":"https://doi.org/10.1002/we.2895","url":null,"abstract":"This study proposes an empirical model for preliminary wind‐resist design of downburst flow. Existing empirical models were compared with field data and found to underpredict horizontal wind speed below the height corresponding to the maximum radial velocity, due to the neglect of viscous effects and the evolution of vertical wind profiles along radial direction. To address these deficiencies, semi‐empirical piecewise functions including wall shear effects in the local turbulent boundary layer and interpolation functions were proposed to improve the accuracy of existing models. The wind profile based on Coles' theory was found to agree well with field data, with the parabola interpolation function being the most desirable. Using the proposed method, the vertical profile of horizontal wind speed at different local radial locations can be predicted for wind resist design given the inlet wind speed of the downburst flow. Overall, this model improves upon existing empirical models and allows for more accurate wind‐resist design.","PeriodicalId":23689,"journal":{"name":"Wind Energy","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139606273","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}
Over the past few decades, global demand for renewable energy has been rising steadily. To meet this demand, there has been an exponential growth in size of wind turbines (WTs) to capture more energy from wind. Consequent increase in weight and flexibility of WT components has led to increased structural loading, affecting reliability of these wind energy conversion systems. Spatio‐temporal variation of rotor effective wind field acts as a disturbance to a WT system, hence, necessitating controllers that can cancel this disturbance. Additionally, assumptions made in extracting linear models for controller design lead to modeling errors resulting from changing operating conditions. Previous attempts have proposed robust controllers incorporating wind disturbance models. However, these controllers have been evaluated on smaller WTs, which experience lower structural loading than larger ones. Additionally, a majority these controllers are based on collective pitch control (CPC), hence do not address loading in the blades. To address these challenges, this contribution proposes an independent pitch‐based robust disturbance accommodating controller (IPC‐RDAC) for reducing structural loads and regulating generator speed in utility‐scale WTs. The proposed controller is designed using ‐synthesis approach and is evaluated on the 5 MW National Renewable Energy Laboratory (NREL) reference WT. Its performance is evaluated against a gain‐scheduled proportional integral (GSPI)‐based reference open‐source controller (ROSCO) and a CPC‐based RDAC (CPC‐RDAC) controller, developed previously by the authors. Simulation results for various wind conditions show that the proposed controller offers improved performance in blade and tower load mitigation, as well a generator speed regulation.
{"title":"IPC‐based robust disturbance accommodating control for load mitigation and speed regulation of wind turbines","authors":"Edwin Kipchirchir, D. Söffker","doi":"10.1002/we.2893","DOIUrl":"https://doi.org/10.1002/we.2893","url":null,"abstract":"Over the past few decades, global demand for renewable energy has been rising steadily. To meet this demand, there has been an exponential growth in size of wind turbines (WTs) to capture more energy from wind. Consequent increase in weight and flexibility of WT components has led to increased structural loading, affecting reliability of these wind energy conversion systems. Spatio‐temporal variation of rotor effective wind field acts as a disturbance to a WT system, hence, necessitating controllers that can cancel this disturbance. Additionally, assumptions made in extracting linear models for controller design lead to modeling errors resulting from changing operating conditions. Previous attempts have proposed robust controllers incorporating wind disturbance models. However, these controllers have been evaluated on smaller WTs, which experience lower structural loading than larger ones. Additionally, a majority these controllers are based on collective pitch control (CPC), hence do not address loading in the blades. To address these challenges, this contribution proposes an independent pitch‐based robust disturbance accommodating controller (IPC‐RDAC) for reducing structural loads and regulating generator speed in utility‐scale WTs. The proposed controller is designed using ‐synthesis approach and is evaluated on the 5 MW National Renewable Energy Laboratory (NREL) reference WT. Its performance is evaluated against a gain‐scheduled proportional integral (GSPI)‐based reference open‐source controller (ROSCO) and a CPC‐based RDAC (CPC‐RDAC) controller, developed previously by the authors. Simulation results for various wind conditions show that the proposed controller offers improved performance in blade and tower load mitigation, as well a generator speed regulation.","PeriodicalId":23689,"journal":{"name":"Wind Energy","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139627469","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}
A. Russell, M. Collu, A. McDonald, P. Thies, Aidan M. Keane, A. R. Quayle
Nacelle‐mounted, forward‐facing light detection and ranging (LIDAR) technology can deliver benefits to rotor speed regulation and loading reductions of floating offshore wind turbines (FOWTs) when assisting with blade pitch control in above‐rated wind speed conditions. Large‐scale wind turbines may be subject to significant variations in structural loads due to differences in the wind profile across the rotor‐swept area. These loading fluctuations can be mitigated by individual pitch control (IPC). This paper presents a novel LIDAR‐assisted feedforward IPC approach that uses each blade's rotor azimuth position to allocate an individual pitch command from a multi‐beam LIDAR. In this study, the source code of OpenFAST wind turbine modelling software was modified to enable LIDAR simulation and LIDAR‐assisted control. The LIDAR simulation modifications were accepted by the National Renewable Energy Laboratory (NREL) and are now present within OpenFAST releases from v3.5 onwards. Simulations of a 15 MW FOWT were performed across the above‐rated wind spectrum. Under a turbulent wind field with an average wind speed of 17 ms−1, the LIDAR‐assisted feedforward IPC delivered up to 54% reductions in the root mean squared errors and standard deviations of key FOWT parameters. Feedforward IPC delivered enhancements of up to 12% over feedforward collective pitch control, relative to the baseline feedback controller. The reductions to the standard deviation and range of the rotor speed may enable structural optimization of the tower, while the reductions in the variations of the loadings present an opportunity for reduced fatigue damage on turbine components and, consequently, a reduction in maintenance expenditure.
{"title":"LIDAR‐assisted feedforward individual pitch control of a 15 MW floating offshore wind turbine","authors":"A. Russell, M. Collu, A. McDonald, P. Thies, Aidan M. Keane, A. R. Quayle","doi":"10.1002/we.2891","DOIUrl":"https://doi.org/10.1002/we.2891","url":null,"abstract":"Nacelle‐mounted, forward‐facing light detection and ranging (LIDAR) technology can deliver benefits to rotor speed regulation and loading reductions of floating offshore wind turbines (FOWTs) when assisting with blade pitch control in above‐rated wind speed conditions. Large‐scale wind turbines may be subject to significant variations in structural loads due to differences in the wind profile across the rotor‐swept area. These loading fluctuations can be mitigated by individual pitch control (IPC). This paper presents a novel LIDAR‐assisted feedforward IPC approach that uses each blade's rotor azimuth position to allocate an individual pitch command from a multi‐beam LIDAR. In this study, the source code of OpenFAST wind turbine modelling software was modified to enable LIDAR simulation and LIDAR‐assisted control. The LIDAR simulation modifications were accepted by the National Renewable Energy Laboratory (NREL) and are now present within OpenFAST releases from v3.5 onwards. Simulations of a 15 MW FOWT were performed across the above‐rated wind spectrum. Under a turbulent wind field with an average wind speed of 17 ms−1, the LIDAR‐assisted feedforward IPC delivered up to 54% reductions in the root mean squared errors and standard deviations of key FOWT parameters. Feedforward IPC delivered enhancements of up to 12% over feedforward collective pitch control, relative to the baseline feedback controller. The reductions to the standard deviation and range of the rotor speed may enable structural optimization of the tower, while the reductions in the variations of the loadings present an opportunity for reduced fatigue damage on turbine components and, consequently, a reduction in maintenance expenditure.","PeriodicalId":23689,"journal":{"name":"Wind Energy","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139628045","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}
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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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}
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