Wind Energy最新文献

{"title":"Statistical modeling of fully nonlinear hydrodynamic loads on offshore wind turbine monopile foundations using wave episodes and targeted CFD simulations through active sampling","authors":"Stephen Guth, Eirini Katsidoniotaki, Themistoklis P. Sapsis","doi":"10.1002/we.2880","DOIUrl":"https://doi.org/10.1002/we.2880","url":null,"abstract":"Abstract Accurately determining hydrodynamic force statistics is crucial for designing offshore engineering structures, including offshore wind turbine foundations, due to the significant impact of nonlinear wave–structure interactions. However, obtaining precise load statistics often involves computationally intensive simulations. Furthermore, the estimation of statistics using current practices is subject to ongoing discussion due to the inherent uncertainty involved. To address these challenges, we present a novel machine learning framework that leverages data‐driven surrogate modeling to predict hydrodynamic loads on monopile foundations while reducing reliance on costly simulations and facilitate the load statistics reconstruction. The primary advantage of our approach is the significant reduction in evaluation time compared to traditional modeling methods. The novelty of our framework lies in its efficient construction of the surrogate model, utilizing the Gaussian process regression machine learning technique and a Bayesian active learning method to sequentially sample wave episodes that contribute to accurate predictions of extreme hydrodynamic forces. Additionally, a spectrum transfer technique combines computational fluid dynamics (CFD) results from both quiescent and extreme waves, further reducing data requirements. This study focuses on reducing the dimensionality of stochastic irregular wave episodes and their associated hydrodynamic force time series. Although the dimensionality reduction is linear, Gaussian process regression successfully captures high‐order correlations. Furthermore, our framework incorporates built‐in uncertainty quantification capabilities, facilitating efficient parameter sampling using traditional CFD tools. This paper provides comprehensive implementation details and demonstrates the effectiveness of our approach in delivering reliable statistics for hydrodynamic loads while overcoming the computational cost constraints associated with classical modeling methods.","PeriodicalId":23689,"journal":{"name":"Wind Energy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134993145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Numerical simulations for a parametric study of blockage effect on offshore wind farms","authors":"Pawel Flaszyński, Filip Wasilczuk, Michal Piotrowicz, Janusz Telega, Karol Mitraszewski, Kurt Schaldemose Hansen","doi":"10.1002/we.2878","DOIUrl":"https://doi.org/10.1002/we.2878","url":null,"abstract":"Abstract The paper presents a study of the upstream influence of wind farms on the wind speed, which is called blockage effect. A Reynolds Averaged Navier–Stokes (RANS) numerical model using an actuator disc method was devised and validated using the SCADA data from a Horns Rev 1 wind farm. The maximum difference between the average power in the first row for SCADA and the numerical model was 7.8%. The model was used to determine the impact of blockage effect on the wind farm parameters and the extent to which the wind speed and the power generation were reduced. A reference wind farm was defined, with a modified size, spacing, turbine height, and diameter that were used for comparison with other wind farm configurations. The results of the investigation of the wind farm parameter effects on the upstream wind speed reduction are presented in the paper. It has been established that increasing the turbine spacing from 5D to 6.7D reduces the power loss due to blockage by two. Blockage losses are almost eliminated when the spacing is increased two times. Similarly, the wind turbine thrust coefficient (C T ) has a large impact on blockage, which is more pronounced, when C T is higher. In fact, the velocity deficit due to blockage is proportional to C T . The turbine tower height has small impact on blockage effect—the power reduction was changed by 0.3% due to blockage for the investigated range. The number of turbines in a row (with a constant number of turbines in a row) does not affect blockage significantly.","PeriodicalId":23689,"journal":{"name":"Wind Energy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135242873","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Profiling wind LiDAR measurements to quantify blockage for onshore wind turbines","authors":"Coleman Moss, Matteo Puccioni, Romit Maulik, Clément Jacquet, Dale Apgar, Giacomo Valerio Iungo","doi":"10.1002/we.2877","DOIUrl":"https://doi.org/10.1002/we.2877","url":null,"abstract":"Abstract Flow modifications induced by wind turbine rotors on the incoming atmospheric boundary layer (ABL), such as blockage and speedups, can be important factors affecting the power performance and annual energy production (AEP) of a wind farm. Further, these rotor‐induced effects on the incoming ABL can vary significantly with the characteristics of the incoming wind, such as wind shear, veer, and turbulence intensity, and turbine operative conditions. To better characterize the complex flow physics underpinning the interaction between turbine rotors and the ABL, a field campaign was performed by deploying profiling wind LiDARs both before and after the construction of an onshore wind turbine array. Considering that the magnitude of these rotor‐induced flow modifications represents a small percentage of the incoming wind speed ( ), high accuracy needs to be achieved for the analysis of the experimental data and generation of flow predictions. Further, flow distortions induced by the site topography and effects of the local climatology need to be quantified and differentiated from those induced by wind turbine rotors. To this aim, a suite of statistical and machine learning models, such as k‐means cluster analysis coupled with random forest predictions, are used to quantify and predict flow modifications for different wind and atmospheric conditions. The experimental results show that wind velocity reductions of up to 3% can be observed at an upstream distance of 1.5 rotor diameter from the leading wind turbine rotor, with more significant effects occurring for larger positive wind shear. For more complex wind conditions, such as negative shear and low‐level jet, the rotor induction becomes highly complex entailing either velocity reductions (down to 9%) below hub height and velocity increases (up to 3%) above hub height. The effects of the rotor induction on the incoming wind velocity field seem to be already roughly negligible at an upstream distance of three rotor diameters. The results from this field experiment will inform models to simulate wind‐turbine and wind‐farm operations with improved accuracy for flow predictions in the proximity of the rotor area, which will be instrumental for more accurate quantification of wind farm blockage and relative effects on AEP.","PeriodicalId":23689,"journal":{"name":"Wind Energy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136381382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting wind farm operations with machine learning and the P2D‐RANS model: A case study for an AWAKEN site","authors":"Coleman Moss, Romit Maulik, Patrick Moriarty, Giacomo Valerio Iungo","doi":"10.1002/we.2874","DOIUrl":"https://doi.org/10.1002/we.2874","url":null,"abstract":"Abstract The power performance and the wind velocity field of an onshore wind farm are predicted with machine learning models and the pseudo‐2D RANS model, then assessed against SCADA data. The wind farm under investigation is one of the sites involved with the American WAKE experimeNt (AWAKEN). The performed simulations enable predictions of the power capture at the farm and turbine levels while providing insights into the effects on power capture associated with wake interactions that operating upstream turbines induce, as well as the variability caused by atmospheric stability. The machine learning models show improved accuracy compared to the pseudo‐2D RANS model in the predictions of turbine power capture and farm power capture with roughly half the normalized error. The machine learning models also entail lower computational costs upon training. Further, the machine learning models provide predictions of the wind turbulence intensity at the turbine level for different wind and atmospheric conditions with very good accuracy, which is difficult to achieve through RANS modeling. Additionally, farm‐to‐farm interactions are noted, with adverse impacts on power predictions from both models.","PeriodicalId":23689,"journal":{"name":"Wind Energy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135405331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mechanical–electrical‐grid model for the doubly fed induction generator wind turbine system considering oscillation frequency coupling characteristics","authors":"Zheng Wang, Yimin Lu","doi":"10.1002/we.2873","DOIUrl":"https://doi.org/10.1002/we.2873","url":null,"abstract":"Abstract With the evolution of renewable energies, many doubly fed induction generators (DFIGs) are being connected to the power grid, whose operation and grid‐connection stability have a major impact on the power grid. Currently, most studies focus on either modeling the mechanical–electrical section or the electrical‐grid section, and discussions have been limited to shaft oscillation or frequency coupling problems. In this study, a mechanical–electrical‐grid model of a DFIG was established to examine the impacts of wind speed and system control parameters on electrical damping and grid‐connection stability. The accuracy of the proposed model and validity of the analyses were verified using simulations. The following were observed: (1) In the case of changing wind speeds, the wind speed and the applied control model determine the shaft oscillation of DFIG, whereas the grid‐connected impedance on the rotor side is dependent on the wind speed. (2) At a constant wind speed, changes in control parameters under different control modes affect the dynamic characteristics of the drive train differently, whereas the grid‐connected impedance on the rotor side is primarily determined by the proportional gain of the inner/outer loop of the control system. The conclusions drawn from this study can further improve the safe and stable operation of DFIG wind power generation systems as well as their connection to the power grid.","PeriodicalId":23689,"journal":{"name":"Wind Energy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135413029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel collaborative control algorithm for maximum power point tracking of wind energy hydraulic conversion system","authors":"Lijuan Chen, Jingbin Li, Lin Zhang, Wei Gao, Chao Ai, Beichen Ding","doi":"10.1002/we.2870","DOIUrl":"https://doi.org/10.1002/we.2870","url":null,"abstract":"Abstract Wind has been admitted as one of the most promising renewable energy resources in multinational regionalization policies. However, the energy conversion and utilization are challenging due to the technique reliability and cost issues. Hydraulic wind turbine (HWT) may solve the above problems. HWT is taken as a research object, and the maximum power point tracking (MPPT) control strategy is proposed collaborating with active disturbance rejection control (ADRC) and linear quadratic regulator (LQR) control methods, to solve multiplicative nonlinearity problems in the plant models and the influence of external disturbance on control performance in the MPPT control process. A nonlinear simulation model is built to explain the main findings from the experiments and obtain a better understanding of the effect of time‐varying system parameters and random fluctuation in wind speed. The collaborative control algorithm is experimentally verified on a 24‐kW HWT semi‐physical test platform that results in a promising energy conversion rate, plus the hydraulic parameters can satisfy the demand, accordingly. Ultimately, the potential challenges of implementing this technique in a smart wind energy conversion system are discussed to give a further design guidance, either theoretically or practically.","PeriodicalId":23689,"journal":{"name":"Wind Energy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135569852","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}

{"title":"Norwegian offshore wind power—Spatial planning using multi‐criteria decision analysis","authors":"Ida Marie Solbrekke, Asgeir Sorteberg","doi":"10.1002/we.2871","DOIUrl":"https://doi.org/10.1002/we.2871","url":null,"abstract":"Abstract The Norwegian government recently agreed on the goal 30by40 , which involves opening Norwegian offshore areas to host 30 GW of installed wind power by 2040. We address this goal by presenting a first mapping of wind power suitability scores (WPSS) for the entire Norwegian economic zone (NEZ) using a multi‐criteria decision analysis framework (MCDA), namely, the analytical hierarchical process (AHP) approach. We obtain WPSS considering relevant criteria like wind resources, techno‐economic aspects, social acceptance, environmental considerations, and met‐ocean constraints such as wind and wave conditions. The results starts with a baseline scenario, where the criterion importance is pairwise compared in the context of balancing economic incentives and conflicting interests. Additionally, to reveal regions that are robust to changes in criterion importance, we carry out a sensitivity analysis by introducing three additional scenarios. These scenarios represent stereotypical actors with distinct preferences for siting of wind farms: the investor , the environmentalist , and the fisherman . The results show that the southern part of the NEZ is the most suitable and robust region for offshore wind power deployment. This region receives the highest suitability category (“very high” suitability for wind power application) throughout all the scenarios. Areas in the Norwegian part of the Barents Sea and the near‐coastal areas outside mid‐Norway are also well suited regions, but these are more sensitive to the choice of criterion importance. The use of AHP within the framework of MCDA is shown to be a promising tool for pinpointing the best Norwegian offshore areas for wind power application.","PeriodicalId":23689,"journal":{"name":"Wind Energy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135858620","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}

{"title":"Convex economic model predictive control for blade loads mitigation on wind turbines","authors":"Atindriyo Kusumo Pamososuryo, Yichao Liu, Tobias Gybel Hovgaard, Riccardo Ferrari, Jan‐Willem van Wingerden","doi":"10.1002/we.2869","DOIUrl":"https://doi.org/10.1002/we.2869","url":null,"abstract":"Abstract Economic model predictive control (EMPC) has received increasing attention in the wind energy community due to its ability to trade‐off economic objectives with ease. However, for wind turbine applications, inherent nonlinearities, such as from aerodynamics, pose difficulties in attaining a convex optimal control problem (OCP), by which real‐time deployment is not only possible but also a globally optimal solution is guaranteed. A variable transformation can be utilized to obtain a convex OCP, where nominal variables, such as rotational speed, pitch angle, and torque, are exchanged with an alternative set in terms of power and energy. The ensuing convex EMPC (CEMPC) possesses linear dynamics, convex constraints, and concave economic objectives and has been successfully employed to address power control and tower fatigue alleviation. This work focuses on extending the blade loads mitigation aspect of the CEMPC framework by exploiting its individual pitch control (IPC) capabilities, resulting in a novel CEMPC‐IPC technique. This extension is made possible by reformulating static blade and rotor moments in terms of individual blade aerodynamic powers and rotational kinetic energy of the drivetrain. The effectiveness of the proposed method is showcased in a mid‐fidelity wind turbine simulation environment in various wind cases, in which comparisons with a basic CEMPC without load mitigation capability and a baseline IPC are made.","PeriodicalId":23689,"journal":{"name":"Wind Energy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136255103","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}

{"title":"Generalized filtered lifting line theory for arbitrary chord lengths and application to wind turbine blades","authors":"Luis A. Martínez‐Tossas, Philip Sakievich, Matthew J. Churchfield, Charles Meneveau","doi":"10.1002/we.2872","DOIUrl":"https://doi.org/10.1002/we.2872","url":null,"abstract":"Abstract The filtered lifting line theory is an analytical approach used to solve the equations of flow subjected to body forces with a Gaussian distribution, such as used in the actuator line model. In the original formulation, the changes in chord length along the blade were assumed to be small. This assumption can lead to errors in the induced velocities predicted by the theory compared to full solutions of the equations. In this work, we revisit the original derivation and provide a more general formulation that can account for significant changes in chord along the blade. The revised formulation can be applied to wings with significant changes in chord along the span, such as wind turbine blades.","PeriodicalId":23689,"journal":{"name":"Wind Energy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135045778","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}

{"title":"Coupling wind LiDAR fixed and volumetric scans for enhanced characterization of wind turbulence and flow three‐dimensionality","authors":"Matteo Puccioni, Coleman Moss, Giacomo Valerio Iungo","doi":"10.1002/we.2865","DOIUrl":"https://doi.org/10.1002/we.2865","url":null,"abstract":"Summary Over the last decades, pulsed light detection and ranging (LiDAR) anemometry has gained growing attention in probing the marine atmospheric boundary layer (MABL) due to its ease of use combined with compelling spatio‐temporal resolution. Among several scanning strategies, fixed scans represent the most prominent choice when high‐frequency resolution is required; however, no information is provided about the spatial heterogeneity of the wind field. On the other hand, volumetric scans allow for the characterization of the spatial variability of the wind field with much lower temporal resolution than fixed scans. In this work, the recently developed “LiDAR Statistical Barnes Objective Analysis” (LiSBOA) algorithm for the optimal design of LiDAR scans and retrieval of wind velocity statistics is tailored for applications in the MABL. The LiDAR data, collected during a recent experimental campaign over Lake Lavon in Texas, show a good consistency of mean velocity profiles between fixed and LiSBOA‐interpolated volumetric data, thus further encouraging the use of coupled fixed and volumetric scans for simultaneous characterizations of wind turbulence statistics along the vertical direction and volumetric heterogeneity of the wind field.","PeriodicalId":23689,"journal":{"name":"Wind Energy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134943639","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}