D. Bensason, A. Sciacchitano, Adhyanth Giri Ajay, Carlos Simao Ferreira
Recent studies have revealed the large potential of vertical‐axis wind turbines (VAWTs) for high‐energy‐density wind farms due to their favorable wake recovery characteristics. The present study provides an experimental demonstration and proof‐of‐concept for the wake recovery mechanism of the novel X‐Rotor VAWT. The phase‐locked flowfield is measured at several streamwise locations along the X‐Rotor's wake using stereoscopic particle image velocimetry (PIV) with fixed‐pitch offsets applied to the blades. The streamwise vortex system of the upper half of the X‐Rotor is first hypothesized and then experimentally verified. The induced wake deformations of the vortex systems are discussed in comparison with previous studies concerning traditional H‐type VAWTs. The results suggest that positive blade pitch is more favorable for accelerated wake recovery due to the dominant tip‐vortex generated on the upwind windward quadrant of the cycle. Utilizing theoretical blade load variations along the span explains distinct unsteady flow features in the near wake generated at select quadrants of the rotor rotation, shedding light on the potential of the two pitch schemes.
{"title":"A Study of the Near Wake Deformation of the X‐Rotor Vertical‐Axis Wind Turbine With Pitched Blades","authors":"D. Bensason, A. Sciacchitano, Adhyanth Giri Ajay, Carlos Simao Ferreira","doi":"10.1002/we.2944","DOIUrl":"https://doi.org/10.1002/we.2944","url":null,"abstract":"Recent studies have revealed the large potential of vertical‐axis wind turbines (VAWTs) for high‐energy‐density wind farms due to their favorable wake recovery characteristics. The present study provides an experimental demonstration and proof‐of‐concept for the wake recovery mechanism of the novel X‐Rotor VAWT. The phase‐locked flowfield is measured at several streamwise locations along the X‐Rotor's wake using stereoscopic particle image velocimetry (PIV) with fixed‐pitch offsets applied to the blades. The streamwise vortex system of the upper half of the X‐Rotor is first hypothesized and then experimentally verified. The induced wake deformations of the vortex systems are discussed in comparison with previous studies concerning traditional H‐type VAWTs. The results suggest that positive blade pitch is more favorable for accelerated wake recovery due to the dominant tip‐vortex generated on the upwind windward quadrant of the cycle. Utilizing theoretical blade load variations along the span explains distinct unsteady flow features in the near wake generated at select quadrants of the rotor rotation, shedding light on the potential of the two pitch schemes.","PeriodicalId":506912,"journal":{"name":"Wind Energy","volume":"59 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141929153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kjell zum Berge, G. Centurelli, M. Dörenkämper, J. Bange, Andreas Platis
The planned expansion of wind energy in the German Bight is creating much more densely staggered wind farms and wind farm clusters. This results in a significantly greater influence of the generated wakes on energy production of neighboring wind farms. The Dornier‐128 research aircraft operated by the Technische Universität of Braunschweig was used to measure the wind field in the lee of single and multiple wind farm clusters in the German Bight on 4 days during July 2020 and July 2021. The data at 120 m aMSL (above mean sea level) were analyzed to identify wake areas and the wind speed decrease behind the wind farm clusters. The observations were then compared to a range of numerical data including the mesoscale model Weather Research and Forecasting (WRF) applying a wind farm parameterization (WRF with wind farm parameterization [WRF‐WF]) to model wake effects and an engineering model with different setups. A model calibrated on a single wind farm is established as the baseline. A modification with a lower wake recovery, the TurbOPark model, and a WRF‐coupled model make up the three additional declinations considered. Overall, the models compared well to the measurement data in the direct vicinity of the wind farms and up to 20–30 km downstream of the wind farm clusters. The accuracy in wind speed prediction of the model results decreased with distance to the wind farms, where the mesoscale model (WRF‐WF) exhibited a more consistent performance across varying distances.
{"title":"Evaluation of Engineering Models for Large‐Scale Cluster Wakes With the Help of In Situ Airborne Measurements","authors":"Kjell zum Berge, G. Centurelli, M. Dörenkämper, J. Bange, Andreas Platis","doi":"10.1002/we.2942","DOIUrl":"https://doi.org/10.1002/we.2942","url":null,"abstract":"The planned expansion of wind energy in the German Bight is creating much more densely staggered wind farms and wind farm clusters. This results in a significantly greater influence of the generated wakes on energy production of neighboring wind farms. The Dornier‐128 research aircraft operated by the Technische Universität of Braunschweig was used to measure the wind field in the lee of single and multiple wind farm clusters in the German Bight on 4 days during July 2020 and July 2021. The data at 120 m aMSL (above mean sea level) were analyzed to identify wake areas and the wind speed decrease behind the wind farm clusters. The observations were then compared to a range of numerical data including the mesoscale model Weather Research and Forecasting (WRF) applying a wind farm parameterization (WRF with wind farm parameterization [WRF‐WF]) to model wake effects and an engineering model with different setups. A model calibrated on a single wind farm is established as the baseline. A modification with a lower wake recovery, the TurbOPark model, and a WRF‐coupled model make up the three additional declinations considered. Overall, the models compared well to the measurement data in the direct vicinity of the wind farms and up to 20–30 km downstream of the wind farm clusters. The accuracy in wind speed prediction of the model results decreased with distance to the wind farms, where the mesoscale model (WRF‐WF) exhibited a more consistent performance across varying distances.","PeriodicalId":506912,"journal":{"name":"Wind Energy","volume":"59 17","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141804602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Federico Zilic de Arcos, A. Wimshurst, R. Willden, Grégory Pinon, Christopher Vogel
This paper presents a reanalysis of four axial‐flow rotor simulation datasets to study the relationship between thrust and axial induction factor. We concentrate on high‐thrust conditions and study variations in induction factor and loads across the span of the different rotor blades. The datasets consist of three different axial‐flow rotors operating at different tip‐speed ratios and, for one dataset, also at different blockage ratios. The reanalysis shows differences between the blade‐resolved CFD results and a widespread empirical turbulent wake model (TWM) used within blade element momentum (BEM) turbine models. These differences result in BEM models underestimating thrust and especially power for axial‐flow rotors operating in high‐thrust regimes. The accuracy of BEM model predictions are improved substantially by correcting this empirical TWM, producing better agreement with blade‐resolved CFD simulations for thrust and torque across most of the span of the blades of the three rotors. Additionally, the paper highlights deficiencies in tiploss modelling in common BEM implementations and highlights the impact of blockage on the relationship between thrust and axial induction factors.
本文重新分析了四个轴流转子模拟数据集，以研究推力与轴向感应因数之间的关系。我们集中研究了高推力条件，并研究了不同转子叶片跨度上感应因数和负载的变化。数据集包括在不同叶尖速度比下运行的三个不同轴流转子，其中一个数据集还包括在不同阻塞比下运行的三个不同轴流转子。重新分析表明，叶片解析 CFD 结果与叶片元素动量（BEM）涡轮机模型中广泛使用的经验湍流尾流模型（TWM）之间存在差异。这些差异导致 BEM 模型低估了推力，尤其是在高推力状态下运行的轴流式转子的功率。通过修正这种经验性 TWM，BEM 模型预测的准确性大幅提高，在三个转子叶片的大部分跨度上与叶片解析 CFD 模拟的推力和扭矩更加一致。此外，论文还强调了常见 BEM 实现中叶尖损失建模的缺陷，并突出了阻塞对推力和轴向感应系数之间关系的影响。
{"title":"A CFD Study on High‐Thrust Corrections for Blade Element Momentum Models","authors":"Federico Zilic de Arcos, A. Wimshurst, R. Willden, Grégory Pinon, Christopher Vogel","doi":"10.1002/we.2937","DOIUrl":"https://doi.org/10.1002/we.2937","url":null,"abstract":"This paper presents a reanalysis of four axial‐flow rotor simulation datasets to study the relationship between thrust and axial induction factor. We concentrate on high‐thrust conditions and study variations in induction factor and loads across the span of the different rotor blades. The datasets consist of three different axial‐flow rotors operating at different tip‐speed ratios and, for one dataset, also at different blockage ratios. The reanalysis shows differences between the blade‐resolved CFD results and a widespread empirical turbulent wake model (TWM) used within blade element momentum (BEM) turbine models. These differences result in BEM models underestimating thrust and especially power for axial‐flow rotors operating in high‐thrust regimes. The accuracy of BEM model predictions are improved substantially by correcting this empirical TWM, producing better agreement with blade‐resolved CFD simulations for thrust and torque across most of the span of the blades of the three rotors. Additionally, the paper highlights deficiencies in tiploss modelling in common BEM implementations and highlights the impact of blockage on the relationship between thrust and axial induction factors.","PeriodicalId":506912,"journal":{"name":"Wind Energy","volume":"77 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141653268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zimin Yang, Xiaosheng Peng, Xiaobing Zhang, Jiajiong Song, Bo Wang, Chun Liu
Offshore wind power is an important technology for low‐carbon power grids. To improve the accuracy, a short‐term offshore wind power prediction method based on significant weather process classification and multitask learning considering neighboring powers is presented in this paper. First, a novel weather process classification method, in which the samples are divided into pieces of waves based on extreme points and are quantified with labels of energy level and fluctuation level, is proposed to classify samples into multiple types of significant weather processes for independent modeling. Second, a multitask learning method, in which the power sequences in neighboring offshore wind farms are innovatively introduced as a new input feature, is proposed for modeling wind power prediction for each wind farm inside a neighboring region under each weather process class. Case studies are presented to verify the effectiveness and superiority of the proposed method. Based on this new method, the 4‐h ultra‐short‐term root mean squared error (RMSE), 24‐h day‐ahead RMSE, 4‐h ultra‐short‐term mean absolute error (MAE), and 24‐h day‐ahead MAE can be reduced by 1.45%, 2.1%, 1.15%, and 1.85%, respectively, compared with benchmark methods, which verify the effectiveness of the proposed method.
{"title":"Short‐Term Offshore Wind Power Prediction Based on Significant Weather Process Classification and Multitask Learning Considering Neighboring Powers","authors":"Zimin Yang, Xiaosheng Peng, Xiaobing Zhang, Jiajiong Song, Bo Wang, Chun Liu","doi":"10.1002/we.2940","DOIUrl":"https://doi.org/10.1002/we.2940","url":null,"abstract":"Offshore wind power is an important technology for low‐carbon power grids. To improve the accuracy, a short‐term offshore wind power prediction method based on significant weather process classification and multitask learning considering neighboring powers is presented in this paper. First, a novel weather process classification method, in which the samples are divided into pieces of waves based on extreme points and are quantified with labels of energy level and fluctuation level, is proposed to classify samples into multiple types of significant weather processes for independent modeling. Second, a multitask learning method, in which the power sequences in neighboring offshore wind farms are innovatively introduced as a new input feature, is proposed for modeling wind power prediction for each wind farm inside a neighboring region under each weather process class. Case studies are presented to verify the effectiveness and superiority of the proposed method. Based on this new method, the 4‐h ultra‐short‐term root mean squared error (RMSE), 24‐h day‐ahead RMSE, 4‐h ultra‐short‐term mean absolute error (MAE), and 24‐h day‐ahead MAE can be reduced by 1.45%, 2.1%, 1.15%, and 1.85%, respectively, compared with benchmark methods, which verify the effectiveness of the proposed method.","PeriodicalId":506912,"journal":{"name":"Wind Energy","volume":"78 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141657724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sylvain Mouton, Alois Peter Schaffarczyk, Nando Timmer
This article reports about a wind‐tunnel experiment carried out in the ONERA F2 low‐speed wind tunnel on a model of the DU 97‐W‐300Mod airfoil designed for wind turbine application. The wind tunnel, the airfoil model, and experimental techniques used are presented, with special emphasis on the data processing and corrections required to derive airfoil forces and pressure distribution. To better document the flow physics at play, the results are illustrated by infrared thermography and surface oil flow visualization. The test allowed investigating Reynolds number effects between 1 and 3.8 millions. To ameliorate the understanding of the benefits and limitations of such airfoil testing, one section is devoted to the comparison of present results with previous experiments in other wind tunnels. Some of the difficulties arising in airfoil testing are evidenced and discussed to contribute to the improvement of test methods.
{"title":"Wind Tunnel Tests of a Thick Wind Turbine Airfoil","authors":"Sylvain Mouton, Alois Peter Schaffarczyk, Nando Timmer","doi":"10.1002/we.2938","DOIUrl":"https://doi.org/10.1002/we.2938","url":null,"abstract":"This article reports about a wind‐tunnel experiment carried out in the ONERA F2 low‐speed wind tunnel on a model of the DU 97‐W‐300Mod airfoil designed for wind turbine application. The wind tunnel, the airfoil model, and experimental techniques used are presented, with special emphasis on the data processing and corrections required to derive airfoil forces and pressure distribution. To better document the flow physics at play, the results are illustrated by infrared thermography and surface oil flow visualization. The test allowed investigating Reynolds number effects between 1 and 3.8 millions. To ameliorate the understanding of the benefits and limitations of such airfoil testing, one section is devoted to the comparison of present results with previous experiments in other wind tunnels. Some of the difficulties arising in airfoil testing are evidenced and discussed to contribute to the improvement of test methods.","PeriodicalId":506912,"journal":{"name":"Wind Energy","volume":"31 38","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141685418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
I. Castro-Fernández, R. Cavallaro, R. Schmehl, G. Sanchez-Arriaga
Three unsteady aerodynamic tools at different levels of fidelity and computational cost were used to investigate the unsteady aerodynamic behavior of a delta kite applied to airborne wind energy. The first tool is an in‐house unsteady panel method that is fast but delivers low to mid fidelity predictions. The second tool uses the open‐source CFD code SU2 to solve the unsteady Reynolds‐averaged Navier–Stokes equations with the SST turbulence model. At an intermediate level of fidelity, a semiempirical dynamic stall model that combines the panel method with a phenomenological dynamic stall module is proposed. The latter has free parameters that are fine‐tuned with CFD results from the second tool. The research on the dynamic stall model has been inspired by two flight test campaigns suggesting dynamic stall phenomena possibly driven by the periodic variation of the angle of attack (aerodynamic pitching motion) during crosswind maneuvers. The recorded inflow along the flight path was prescribed in the three aerodynamic tools. As expected, the price to pay for the low computational cost of the panel method is its inability to capture the dynamic stall phenomenon. The results from unsteady CFD qualitatively matched the experimental data identifying a leading‐edge vortex that forms and detaches cyclically during the pitching motion. Using RANS data, the semiempirical tool was fined‐tuned to reproduce the dynamic stall behavior, becoming an accurate and fast aerodynamic tool for coupling with any kite flight simulator. Further discussions on the effects of kite aerostructural deflections are included.
{"title":"Unsteady Aerodynamics of Delta Kites for Airborne Wind Energy Under Dynamic Stall Conditions","authors":"I. Castro-Fernández, R. Cavallaro, R. Schmehl, G. Sanchez-Arriaga","doi":"10.1002/we.2932","DOIUrl":"https://doi.org/10.1002/we.2932","url":null,"abstract":"Three unsteady aerodynamic tools at different levels of fidelity and computational cost were used to investigate the unsteady aerodynamic behavior of a delta kite applied to airborne wind energy. The first tool is an in‐house unsteady panel method that is fast but delivers low to mid fidelity predictions. The second tool uses the open‐source CFD code SU2 to solve the unsteady Reynolds‐averaged Navier–Stokes equations with the SST turbulence model. At an intermediate level of fidelity, a semiempirical dynamic stall model that combines the panel method with a phenomenological dynamic stall module is proposed. The latter has free parameters that are fine‐tuned with CFD results from the second tool. The research on the dynamic stall model has been inspired by two flight test campaigns suggesting dynamic stall phenomena possibly driven by the periodic variation of the angle of attack (aerodynamic pitching motion) during crosswind maneuvers. The recorded inflow along the flight path was prescribed in the three aerodynamic tools. As expected, the price to pay for the low computational cost of the panel method is its inability to capture the dynamic stall phenomenon. The results from unsteady CFD qualitatively matched the experimental data identifying a leading‐edge vortex that forms and detaches cyclically during the pitching motion. Using RANS data, the semiempirical tool was fined‐tuned to reproduce the dynamic stall behavior, becoming an accurate and fast aerodynamic tool for coupling with any kite flight simulator. Further discussions on the effects of kite aerostructural deflections are included.","PeriodicalId":506912,"journal":{"name":"Wind Energy","volume":"67 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141688298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Vertical axis wind turbine (VAWT) has a rotating axis perpendicular to the wind direction. This type of wind turbine that is suitable for urban environments has low wind direction dependency and noise. In this research, a novel surrogated approach for optimizing a VAWT is proposed, used, tested, and verified, which is not reported in literature. The proposed method consisted of 3D computational fluid dynamics (CFD) analysis of wind flow through the wind turbine with FLUENT software by solving the unsteady turbulent equations. However, 3D CFD analysis was time and cost consuming to obtain the output result (power coefficient) from input values (airfoil chord length, pitch angle, and tip speed ratio as turbine design variables). Thus, artificial neural network (ANN) was applied to obtain weight functions to correlate FLUENT software inputs and outputs after learning process. Finally, genetic algorithm was used for maximizing the turbine power coefficient considering three defined design variables. The optimum value of power coefficient was improved to 0.244, and the optimum values of design variables for blade chord length, blade pitch angle, and blade tip speed ratio were 0.218, −0.453, and 1.24, respectively. This novel surrogated method reduced the computational time and cost of VAWT optimizing considerably.
{"title":"A Novel Surrogated Approach for Optimizing a Vertical Axis Wind Turbine With Straight Blades","authors":"S. Sanaye, Parsa Rezaeian, Armin Farvizi","doi":"10.1002/we.2934","DOIUrl":"https://doi.org/10.1002/we.2934","url":null,"abstract":"Vertical axis wind turbine (VAWT) has a rotating axis perpendicular to the wind direction. This type of wind turbine that is suitable for urban environments has low wind direction dependency and noise. In this research, a novel surrogated approach for optimizing a VAWT is proposed, used, tested, and verified, which is not reported in literature. The proposed method consisted of 3D computational fluid dynamics (CFD) analysis of wind flow through the wind turbine with FLUENT software by solving the unsteady turbulent equations. However, 3D CFD analysis was time and cost consuming to obtain the output result (power coefficient) from input values (airfoil chord length, pitch angle, and tip speed ratio as turbine design variables). Thus, artificial neural network (ANN) was applied to obtain weight functions to correlate FLUENT software inputs and outputs after learning process. Finally, genetic algorithm was used for maximizing the turbine power coefficient considering three defined design variables. The optimum value of power coefficient was improved to 0.244, and the optimum values of design variables for blade chord length, blade pitch angle, and blade tip speed ratio were 0.218, −0.453, and 1.24, respectively. This novel surrogated method reduced the computational time and cost of VAWT optimizing considerably.","PeriodicalId":506912,"journal":{"name":"Wind Energy","volume":"102 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141714265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Imtiaz, Lijun Yang, Hafiz Muhammad Azib Khan, Hafiz Mudassir Munir, Mohammed Alharbi, M. Jamil
The importance of resolving stability concerns in weak AC grid‐connected doubly fed induction generator (DFIG) wind energy systems during low‐voltage ride‐through (LVRT) events cannot be ignored, given the increasing popularity of wind power‐based microgrids. Furthermore, the emergence of generation loss and postfault oscillation within a microgrid (MG) due to grid faults has also become a significant concern. The static synchronous compensator (STATCOM) under consideration in this study is tuned using particle swarm optimization (PSO), the artificial hummingbird algorithm (AHA), and a hybrid approach incorporating both PSO and AHA. Faults of both a symmetrical and an asymmetrical nature have occurred on the power grid side. The proposed hybrid PSO‐AHA‐tuned STATCOM strategy aims to improve LVRT, minimize power generation loss during faults, and reduce oscillations after a fault by controlling the flow of reactive power between point of common coupling (PCC) and MG. The MATLAB simulation environment was used to simulate the 16 MW MG test system. The performance of the PSO‐AHA‐tuned STATCOM was assessed by comparing results with those from conventional STATCOM, PSO, and AHA optimizer‐tuned STATCOM in four fault situations. A comparison of the results shows that the proposed strategy performed better than other approaches mentioned in this paper and achieved the desired objectives.
{"title":"Wind‐assisted microgrid grid code compliance employing a hybrid Particle swarm optimization‐Artificial hummingbird algorithm optimizer‐tuned STATCOM","authors":"S. Imtiaz, Lijun Yang, Hafiz Muhammad Azib Khan, Hafiz Mudassir Munir, Mohammed Alharbi, M. Jamil","doi":"10.1002/we.2908","DOIUrl":"https://doi.org/10.1002/we.2908","url":null,"abstract":"The importance of resolving stability concerns in weak AC grid‐connected doubly fed induction generator (DFIG) wind energy systems during low‐voltage ride‐through (LVRT) events cannot be ignored, given the increasing popularity of wind power‐based microgrids. Furthermore, the emergence of generation loss and postfault oscillation within a microgrid (MG) due to grid faults has also become a significant concern. The static synchronous compensator (STATCOM) under consideration in this study is tuned using particle swarm optimization (PSO), the artificial hummingbird algorithm (AHA), and a hybrid approach incorporating both PSO and AHA. Faults of both a symmetrical and an asymmetrical nature have occurred on the power grid side. The proposed hybrid PSO‐AHA‐tuned STATCOM strategy aims to improve LVRT, minimize power generation loss during faults, and reduce oscillations after a fault by controlling the flow of reactive power between point of common coupling (PCC) and MG. The MATLAB simulation environment was used to simulate the 16 MW MG test system. The performance of the PSO‐AHA‐tuned STATCOM was assessed by comparing results with those from conventional STATCOM, PSO, and AHA optimizer‐tuned STATCOM in four fault situations. A comparison of the results shows that the proposed strategy performed better than other approaches mentioned in this paper and achieved the desired objectives.","PeriodicalId":506912,"journal":{"name":"Wind Energy","volume":"66 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140675874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M.‐A. Dufour, G. Pinon, E. Rivoalen, F. Blondel, G. Germain
This paper presents a lifting‐line implementation in the framework of a Lagrangian vortex particle method (LL‐VP). The novelty of the present implementation lies in the fluid particles properties definition and in the particles shedding process. In spite of mimicking a panel method, the LL‐VP needs some peculiar treatments described in the paper. The present implementation converges rapidly and efficiently during the shedding sub‐iteration process. This LL‐VP method shows good accuracy, even with moderate numbers of sections. Compared to its panel or vortex filaments counterparts, more frequently encountered in the literature, the present implementation inherently accounts for the diffusion term of the Navier‐Stokes equations, possibly with a turbulent viscosity model. Additionally, the present implementation can also account for more complex onset flows: upstream ambient turbulence and upstream turbine wakes. After validation on an analytical elliptic wing configuration, the model is tested on the Mexnext‐III wind turbine application, for three reduced velocities. Accurate results are obtained both on the analytical elliptic wing and on the New MEXICO rotor cases in comparison with other similar numerical models. A focus is made on the Mexnext‐III wake analysis. The numerical wake obtained with the present LL‐VP is close to other numerical and experimental results. Finally, a last configuration with three tidal turbines in interaction is considered based on an experimental campaign carried out at the IFREMER wave and current flume tank. Enhanced turbine‐wake interactions are highlighted, with favourable comparisons with the experiment. Hence, such turbine interactions in a farm are accessible with this LL‐VP implementation, be it wind or tidal energy field.
{"title":"Development and validation of a lifting‐line code associated with the vortex particle method software Dorothy","authors":"M.‐A. Dufour, G. Pinon, E. Rivoalen, F. Blondel, G. Germain","doi":"10.1002/we.2905","DOIUrl":"https://doi.org/10.1002/we.2905","url":null,"abstract":"This paper presents a lifting‐line implementation in the framework of a Lagrangian vortex particle method (LL‐VP). The novelty of the present implementation lies in the fluid particles properties definition and in the particles shedding process. In spite of mimicking a panel method, the LL‐VP needs some peculiar treatments described in the paper. The present implementation converges rapidly and efficiently during the shedding sub‐iteration process. This LL‐VP method shows good accuracy, even with moderate numbers of sections. Compared to its panel or vortex filaments counterparts, more frequently encountered in the literature, the present implementation inherently accounts for the diffusion term of the Navier‐Stokes equations, possibly with a turbulent viscosity model. Additionally, the present implementation can also account for more complex onset flows: upstream ambient turbulence and upstream turbine wakes. After validation on an analytical elliptic wing configuration, the model is tested on the Mexnext‐III wind turbine application, for three reduced velocities. Accurate results are obtained both on the analytical elliptic wing and on the New MEXICO rotor cases in comparison with other similar numerical models. A focus is made on the Mexnext‐III wake analysis. The numerical wake obtained with the present LL‐VP is close to other numerical and experimental results. Finally, a last configuration with three tidal turbines in interaction is considered based on an experimental campaign carried out at the IFREMER wave and current flume tank. Enhanced turbine‐wake interactions are highlighted, with favourable comparisons with the experiment. Hence, such turbine interactions in a farm are accessible with this LL‐VP implementation, be it wind or tidal energy field.","PeriodicalId":506912,"journal":{"name":"Wind Energy","volume":"55 20","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140748557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Claudia Muscari, P. Schito, A. Viré, A. Zasso, J. van Wingerden
Actuator line modeling of wind turbines requires the definition of a free‐stream velocity in a computational mesh and a regularization kernel to project the computed body forces onto the domain. Both choices strongly influence the results. In this work, a novel velocity sampling method—the so‐called effective velocity model (EVM)—is implemented in the CFD software SOWFA, validated, and compared to pre‐existing approaches. Results show superior method robustness with respect to the regularization kernel width ( ) choice while preserving acceptable accuracy. In particular, the power predicted by the EVM is nearly independent of the value.
{"title":"The effective velocity model: An improved approach to velocity sampling in actuator line models","authors":"Claudia Muscari, P. Schito, A. Viré, A. Zasso, J. van Wingerden","doi":"10.1002/we.2894","DOIUrl":"https://doi.org/10.1002/we.2894","url":null,"abstract":"Actuator line modeling of wind turbines requires the definition of a free‐stream velocity in a computational mesh and a regularization kernel to project the computed body forces onto the domain. Both choices strongly influence the results. In this work, a novel velocity sampling method—the so‐called effective velocity model (EVM)—is implemented in the CFD software SOWFA, validated, and compared to pre‐existing approaches. Results show superior method robustness with respect to the regularization kernel width (\u0000) choice while preserving acceptable accuracy. In particular, the power predicted by the EVM is nearly independent of the \u0000 value.","PeriodicalId":506912,"journal":{"name":"Wind Energy","volume":"28 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140259682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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