Turbulent and gusty wind conditions can cause generator overspeed peaks to exceed a threshold that then lead to wind turbine shutdowns, which then decrease the energy production of the wind turbines. We derive so‐called “gust measures” that predict when generator overspeed peaks may occur. These gust measures are then used to develop advanced controllers to mitigate generator overspeed peaks so that wind turbines can operate more robustly in difficult wind conditions without exceeding generator overspeed thresholds that would lead to turbine shutdown events. The advanced controllers are demonstrated in nonlinear aeroelastic simulations using the open‐source wind turbine simulation tool OpenFAST. To increase the realism of the simulations, they are run using field‐replicated wind conditions and a wind turbine model based on data from an experimental field campaign on a downscaled demonstrator of a novel extreme‐scale, two‐bladed, downwind rotor design.
{"title":"Advanced wind turbine control development using field test analysis for generator overspeed mitigation","authors":"Mandar Phadnis, D. Zalkind, Lucy Pao","doi":"10.1002/we.2860","DOIUrl":"https://doi.org/10.1002/we.2860","url":null,"abstract":"Turbulent and gusty wind conditions can cause generator overspeed peaks to exceed a threshold that then lead to wind turbine shutdowns, which then decrease the energy production of the wind turbines. We derive so‐called “gust measures” that predict when generator overspeed peaks may occur. These gust measures are then used to develop advanced controllers to mitigate generator overspeed peaks so that wind turbines can operate more robustly in difficult wind conditions without exceeding generator overspeed thresholds that would lead to turbine shutdown events. The advanced controllers are demonstrated in nonlinear aeroelastic simulations using the open‐source wind turbine simulation tool OpenFAST. To increase the realism of the simulations, they are run using field‐replicated wind conditions and a wind turbine model based on data from an experimental field campaign on a downscaled demonstrator of a novel extreme‐scale, two‐bladed, downwind rotor design.","PeriodicalId":23689,"journal":{"name":"Wind Energy","volume":" ","pages":""},"PeriodicalIF":4.1,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49577537","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}
Ming Huang, Yugandhar Vijaykumar Patil, Andrea Sciacchitano, C. Ferreira
Wakes and wake interactions in wind turbine arrays diminish energy output and raise the risk of structural fatigue; hence, comprehending the features of rotor–wake interactions is of practical relevance. Previous studies suggest that vertical axis wind turbines (VAWTs) can facilitate a quicker wake recovery. This study experimentally investigates the rotor–wake and wake–wake interaction of VAWTs; different pitch angles of the blades of the upwind VAWT are considered to assess the interactions for different wake deflections. With stereoscopic particle image velocimetry, the wake interactions of two VAWTs are analysed in nine distinct wake deflection and rotor location configurations. The time‐average velocity fields at several planes upwind and downwind from the rotors are measured. Additionally, time‐average loads on the VAWTs are measured via force balances. The results validate the rapid wake recovery and the efficacy of wake deflection, which increases the available power in the second rotor.
{"title":"Experimental study of the wake interaction between two vertical axis wind turbines","authors":"Ming Huang, Yugandhar Vijaykumar Patil, Andrea Sciacchitano, C. Ferreira","doi":"10.1002/we.2863","DOIUrl":"https://doi.org/10.1002/we.2863","url":null,"abstract":"Wakes and wake interactions in wind turbine arrays diminish energy output and raise the risk of structural fatigue; hence, comprehending the features of rotor–wake interactions is of practical relevance. Previous studies suggest that vertical axis wind turbines (VAWTs) can facilitate a quicker wake recovery. This study experimentally investigates the rotor–wake and wake–wake interaction of VAWTs; different pitch angles of the blades of the upwind VAWT are considered to assess the interactions for different wake deflections. With stereoscopic particle image velocimetry, the wake interactions of two VAWTs are analysed in nine distinct wake deflection and rotor location configurations. The time‐average velocity fields at several planes upwind and downwind from the rotors are measured. Additionally, time‐average loads on the VAWTs are measured via force balances. The results validate the rapid wake recovery and the efficacy of wake deflection, which increases the available power in the second rotor.","PeriodicalId":23689,"journal":{"name":"Wind Energy","volume":" ","pages":""},"PeriodicalIF":4.1,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44041103","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}
Offshore wind turbines play a critical role as a renewable energy source and are experiencing continuous growth in usage. Both the design and implementation phases of constructing these structures present difficulties. It is crucial to ensure these structures are built to resist such conditions, assuring their durability, as they are exposed to lateral external influences such as wind and wave loads. This study investigated how monopile foundations behave in saturated sandy soil under cyclic loading. Pore water pressure accumulations in saturated sandy soil, monopile head lateral displacements, and vertical settlements around the monopile are investigated using the hypoplastic material model and two‐phase element with the ANSYS finite element program. Analyses conducted in this study demonstrated that lateral cyclic loads could cause excessive pore water pressure accumulations around the monopile, leading to displacements in the monopile head and soil settlements around it, highlighting the importance of carefully considering loading characteristics during the design process to provide the security and longevity of offshore wind turbines.
{"title":"Investigation of the effects of cyclic lateral load characteristics on monopiles in saturated sandy soils using hypoplastic material model","authors":"Sacit Sarımurat","doi":"10.1002/we.2862","DOIUrl":"https://doi.org/10.1002/we.2862","url":null,"abstract":"Offshore wind turbines play a critical role as a renewable energy source and are experiencing continuous growth in usage. Both the design and implementation phases of constructing these structures present difficulties. It is crucial to ensure these structures are built to resist such conditions, assuring their durability, as they are exposed to lateral external influences such as wind and wave loads. This study investigated how monopile foundations behave in saturated sandy soil under cyclic loading. Pore water pressure accumulations in saturated sandy soil, monopile head lateral displacements, and vertical settlements around the monopile are investigated using the hypoplastic material model and two‐phase element with the ANSYS finite element program. Analyses conducted in this study demonstrated that lateral cyclic loads could cause excessive pore water pressure accumulations around the monopile, leading to displacements in the monopile head and soil settlements around it, highlighting the importance of carefully considering loading characteristics during the design process to provide the security and longevity of offshore wind turbines.","PeriodicalId":23689,"journal":{"name":"Wind Energy","volume":" ","pages":""},"PeriodicalIF":4.1,"publicationDate":"2023-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46945858","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}
Numerical simulations of rain droplet impacts on real rough surfaces of leading edges of wind turbine blades are presented. The effect of rough blade surface conditions during liquid impacts on the stress distribution in the protective coating is studied. Realistic rough surfaces of wind turbine blades, obtained from 3D reconstruction of real blades with photogrammetry, as well as artificially generated rough surfaces were introduced into finite element models of the droplet/blade coating interaction. Stress distributions in the protective coating with rough and flat surfaces were studied and compared. The results of the simulations suggest that roughness on the surface of the blade leads to increased stresses in the protective coating.
{"title":"Erosion modelling on reconstructed rough surfaces of wind turbine blades","authors":"Antonios Tempelis, Leon Mishnaevsky Jr.","doi":"10.1002/we.2848","DOIUrl":"https://doi.org/10.1002/we.2848","url":null,"abstract":"Numerical simulations of rain droplet impacts on real rough surfaces of leading edges of wind turbine blades are presented. The effect of rough blade surface conditions during liquid impacts on the stress distribution in the protective coating is studied. Realistic rough surfaces of wind turbine blades, obtained from 3D reconstruction of real blades with photogrammetry, as well as artificially generated rough surfaces were introduced into finite element models of the droplet/blade coating interaction. Stress distributions in the protective coating with rough and flat surfaces were studied and compared. The results of the simulations suggest that roughness on the surface of the blade leads to increased stresses in the protective coating.","PeriodicalId":23689,"journal":{"name":"Wind Energy","volume":" ","pages":""},"PeriodicalIF":4.1,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46318825","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}
Can Yang, Longfei Xiao, Peng Chen, Zhengshun Cheng, Mingyue Liu, Lei Liu
The fore‐aft motion of the rotor‐nacelle assembly (RNA) of a rotating floating wind turbine (FWT) can cause an oscillation in aerodynamic thrust, which may be equivalently treated as frequency‐dependent aerodynamic mass and damping effects. In this study, an explicit frequency‐domain analytical model is proposed to calculate the equivalent aerodynamic mass and damping of FWTs, with proper linearization of control system. Assuming that an FWT operates under steady wind conditions and a forced oscillation is exerted at the RNA along the wind direction, the thrust fluctuations are equivalently represented by the force and moment acting on the nacelle instead of pure aerodynamic loads. Based on the thrust oscillation expression, equivalent aerodynamic mass and damping are derived analytically. After verifying the model by numerical comparison, it is used to demonstrate equivalent aerodynamic mass and damping of three wind turbines (5–15 MW). Effects of wind turbine up‐scaling and controller dynamics are addressed. Results show that equivalent aerodynamic mass and damping present a nonlinear characteristic with oscillation frequency in the below‐rated region, while the relationship is close to linear for higher wind speeds. The effect of wind turbine up‐scaling has a visible impact on equivalent aerodynamic mass and damping, especially at near‐rated wind speed. Controller gains affect equivalent aerodynamic mass and damping and should be tuned reasonably in the controller design for FWTs. Outcomes of our study can be used to establish a frequency‐domain coupled model of FWTs and are beneficial for conceptual design and parameter optimization of the platform of FWTs.
{"title":"An analytical frequency‐domain model of aerodynamic mass and damping of floating wind turbines","authors":"Can Yang, Longfei Xiao, Peng Chen, Zhengshun Cheng, Mingyue Liu, Lei Liu","doi":"10.1002/we.2861","DOIUrl":"https://doi.org/10.1002/we.2861","url":null,"abstract":"The fore‐aft motion of the rotor‐nacelle assembly (RNA) of a rotating floating wind turbine (FWT) can cause an oscillation in aerodynamic thrust, which may be equivalently treated as frequency‐dependent aerodynamic mass and damping effects. In this study, an explicit frequency‐domain analytical model is proposed to calculate the equivalent aerodynamic mass and damping of FWTs, with proper linearization of control system. Assuming that an FWT operates under steady wind conditions and a forced oscillation is exerted at the RNA along the wind direction, the thrust fluctuations are equivalently represented by the force and moment acting on the nacelle instead of pure aerodynamic loads. Based on the thrust oscillation expression, equivalent aerodynamic mass and damping are derived analytically. After verifying the model by numerical comparison, it is used to demonstrate equivalent aerodynamic mass and damping of three wind turbines (5–15 MW). Effects of wind turbine up‐scaling and controller dynamics are addressed. Results show that equivalent aerodynamic mass and damping present a nonlinear characteristic with oscillation frequency in the below‐rated region, while the relationship is close to linear for higher wind speeds. The effect of wind turbine up‐scaling has a visible impact on equivalent aerodynamic mass and damping, especially at near‐rated wind speed. Controller gains affect equivalent aerodynamic mass and damping and should be tuned reasonably in the controller design for FWTs. Outcomes of our study can be used to establish a frequency‐domain coupled model of FWTs and are beneficial for conceptual design and parameter optimization of the platform of FWTs.","PeriodicalId":23689,"journal":{"name":"Wind Energy","volume":" ","pages":""},"PeriodicalIF":4.1,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42967858","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. A. Fremmelev, P. Ladpli, E. Orlowitz, N. Dervilis, M. McGugan, K. Branner
The present work investigates the performance of different features, extracted from vibration‐based data, for structural health monitoring of a 52‐meter wind turbine blade during fatigue testing. An active vibration monitoring system was used during the test campaign, providing periodic excitation of single frequencies in the medium‐frequency range, and using accelerometers to measure the vibration output on different parts of the blade. Based on previous work from the authors, data is available for the wind turbine blade in healthy state, with a manually induced damage, and with progressively increasing damage severity. Using the vibration data, different signal processing methods are used to extract damage‐sensitive features. Time series methods and time‐frequency domain methods are used to quantify the applied active vibration signal. Using outlier analysis, the health state of the blade is classified, and the classification accuracy through use of the different features is compared. Highest performance is generally obtained by auto‐regressive modeling of the vibration outputs, using the auto‐regressive parameters as features. Finally, suggestions for future improvements of the present method toward practical implementation are given.
{"title":"Feasibility study on a full‐scale wind turbine blade monitoring campaign: Comparing performance and robustness of features extracted from medium‐frequency active vibrations","authors":"M. A. Fremmelev, P. Ladpli, E. Orlowitz, N. Dervilis, M. McGugan, K. Branner","doi":"10.1002/we.2854","DOIUrl":"https://doi.org/10.1002/we.2854","url":null,"abstract":"The present work investigates the performance of different features, extracted from vibration‐based data, for structural health monitoring of a 52‐meter wind turbine blade during fatigue testing. An active vibration monitoring system was used during the test campaign, providing periodic excitation of single frequencies in the medium‐frequency range, and using accelerometers to measure the vibration output on different parts of the blade. Based on previous work from the authors, data is available for the wind turbine blade in healthy state, with a manually induced damage, and with progressively increasing damage severity. Using the vibration data, different signal processing methods are used to extract damage‐sensitive features. Time series methods and time‐frequency domain methods are used to quantify the applied active vibration signal. Using outlier analysis, the health state of the blade is classified, and the classification accuracy through use of the different features is compared. Highest performance is generally obtained by auto‐regressive modeling of the vibration outputs, using the auto‐regressive parameters as features. Finally, suggestions for future improvements of the present method toward practical implementation are given.","PeriodicalId":23689,"journal":{"name":"Wind Energy","volume":" ","pages":""},"PeriodicalIF":4.1,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45837029","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}
Effective and timely health monitoring of wind turbine gearboxes and generators is essential to reduce the costs of operations and maintenance activities, especially offshore. This paper presents a scalable and lightweight convolutional neural network (CNN) framework using high‐dimensional raw condition monitoring data for the automatic detection of multiple wind turbine electromechanical faults. The proposed approach leverages the potential of combining information from a variety of signals to learn features and to discriminate the types of fault and their severity. As a result of the CNN layers used to extract features from the signals, this architecture works in the time domain and can digest high‐resolution multi‐sensor data streams in real‐time. To overcome the inherent black‐box nature of AI models, this research proposes two interpretability techniques, multidimensional scaling and layer‐wise relevance propagation, to analyse the proposed model's inner‐working and identify the signal features relevant for fault classification. Experimental results show high performance and classification accuracies above 99.9% for all fault cases tested, demonstrating the efficacy of the proposed fault‐detection system.
{"title":"Convolutional neural network framework for wind turbine electromechanical fault detection","authors":"Emilie Stone, S. Giani, D. Zappalá, C. Crabtree","doi":"10.1002/we.2857","DOIUrl":"https://doi.org/10.1002/we.2857","url":null,"abstract":"Effective and timely health monitoring of wind turbine gearboxes and generators is essential to reduce the costs of operations and maintenance activities, especially offshore. This paper presents a scalable and lightweight convolutional neural network (CNN) framework using high‐dimensional raw condition monitoring data for the automatic detection of multiple wind turbine electromechanical faults. The proposed approach leverages the potential of combining information from a variety of signals to learn features and to discriminate the types of fault and their severity. As a result of the CNN layers used to extract features from the signals, this architecture works in the time domain and can digest high‐resolution multi‐sensor data streams in real‐time. To overcome the inherent black‐box nature of AI models, this research proposes two interpretability techniques, multidimensional scaling and layer‐wise relevance propagation, to analyse the proposed model's inner‐working and identify the signal features relevant for fault classification. Experimental results show high performance and classification accuracies above 99.9% for all fault cases tested, demonstrating the efficacy of the proposed fault‐detection system.","PeriodicalId":23689,"journal":{"name":"Wind Energy","volume":" ","pages":""},"PeriodicalIF":4.1,"publicationDate":"2023-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47376632","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}
{ The use of state estimation techniques offers a means of inferring rotor effective wind speed from standard measurements of wind turbines. Typical wind speed estimators rely upon a pre-computed quasi-steady aerodynamic mapping, which describes the relationship between pitch angle and tip-speed ratio and the power coefficient. In practice, the static mapping does not capture the influence of turbine structural dynamics and atmospheric turbulence, inevitably resulting in poor performance of the wind speed estimation. In addition, the turbine aerodynamic properties might not be easily accessible. Thus, this paper presents a rotor effective wind speed estimation method that obviates the requirement for prior knowledge of turbine power coefficients. Specifically, the proposed method exploits a simple actuator disc model, where the aerodynamic power and thrust coefficients can be characterised in terms of axial induction factors. Based on this insight and standard turbine measurements, real-time estimation of rotor effective wind speed and axial induction factors can then be achieved using a simplified turbine
{"title":"Real‐time rotor effective wind speed estimation based on actuator disc theory: Design and full‐scale experimental validation","authors":"A. Lio, F. Meng, G. Larsen","doi":"10.1002/we.2858","DOIUrl":"https://doi.org/10.1002/we.2858","url":null,"abstract":"{ The use of state estimation techniques offers a means of inferring rotor effective wind speed from standard measurements of wind turbines. Typical wind speed estimators rely upon a pre-computed quasi-steady aerodynamic mapping, which describes the relationship between pitch angle and tip-speed ratio and the power coefficient. In practice, the static mapping does not capture the influence of turbine structural dynamics and atmospheric turbulence, inevitably resulting in poor performance of the wind speed estimation. In addition, the turbine aerodynamic properties might not be easily accessible. Thus, this paper presents a rotor effective wind speed estimation method that obviates the requirement for prior knowledge of turbine power coefficients. Specifically, the proposed method exploits a simple actuator disc model, where the aerodynamic power and thrust coefficients can be characterised in terms of axial induction factors. Based on this insight and standard turbine measurements, real-time estimation of rotor effective wind speed and axial induction factors can then be achieved using a simplified turbine","PeriodicalId":23689,"journal":{"name":"Wind Energy","volume":" ","pages":""},"PeriodicalIF":4.1,"publicationDate":"2023-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46301167","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}
Mohammad Odeh, Kazi Mohsin, Tri D. Ngo, D. Zalkind, J. Jonkman, A. Wright, A. Robertson, Tuhin Das
{"title":"Development of a wind turbine model and simulation platform using an acausal approach: Multiphysics modeling, validation, and control","authors":"Mohammad Odeh, Kazi Mohsin, Tri D. Ngo, D. Zalkind, J. Jonkman, A. Wright, A. Robertson, Tuhin Das","doi":"10.1002/we.2853","DOIUrl":"https://doi.org/10.1002/we.2853","url":null,"abstract":"","PeriodicalId":23689,"journal":{"name":"Wind Energy","volume":" ","pages":""},"PeriodicalIF":4.1,"publicationDate":"2023-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45239896","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}
E. Taschner, M. Folkersma, Luis A Martínez‐Tossas, R. Verzijlbergh, J. van Wingerden
{"title":"A new coupling of a GPU‐resident large‐eddy simulation code with a multiphysics wind turbine simulation tool","authors":"E. Taschner, M. Folkersma, Luis A Martínez‐Tossas, R. Verzijlbergh, J. van Wingerden","doi":"10.1002/we.2844","DOIUrl":"https://doi.org/10.1002/we.2844","url":null,"abstract":"","PeriodicalId":23689,"journal":{"name":"Wind Energy","volume":" ","pages":""},"PeriodicalIF":4.1,"publicationDate":"2023-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48767954","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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