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Overview of normal behavior modeling approaches for SCADA-based wind turbine condition monitoring demonstrated on data from operational wind farms 基于scada的风力涡轮机状态监测的正常行为建模方法概述,并通过运行风电场的数据进行了演示
Q3 GREEN & SUSTAINABLE SCIENCE & TECHNOLOGY Pub Date : 2023-06-05 DOI: 10.5194/wes-8-893-2023
Xavier Chesterman, Timothy Verstraeten, Pieter-Jan Daems, Ann Nowé, Jan Helsen
Abstract. Condition monitoring and failure prediction for wind turbines currently comprise a hot research topic. This follows from the fact that investments in the wind energy sector have increased dramatically due to the transition to renewable energy production. This paper reviews and implements several techniques from state-of-the-art research on condition monitoring for wind turbines using SCADA data and the normal behavior modeling framework. The first part of the paper consists of an in-depth overview of the current state of the art. In the second part, several techniques from the overview are implemented and compared using data (SCADA and failure data) from five operational wind farms. To this end, six demonstration experiments are designed. The first five experiments test different techniques for the modeling of normal behavior. The sixth experiment compares several techniques that can be used for identifying anomalous patterns in the prediction error. The selection of the tested techniques is driven by requirements from industrial partners, e.g., a limited number of training data and low training and maintenance costs of the models. The paper concludes with several directions for future work.
摘要风力发电机组的状态监测与故障预测是目前研究的热点。这是因为,由于向可再生能源生产的过渡,风能领域的投资大幅增加。本文回顾并实现了利用SCADA数据和正常行为建模框架进行风力涡轮机状态监测的最新研究中的几种技术。本文的第一部分包括对当前技术状况的深入概述。在第二部分,从概述中实施了几种技术,并使用来自五个运行风电场的数据(SCADA和故障数据)进行了比较。为此,设计了六个示范实验。前五个实验测试了正常行为建模的不同技术。第六项实验比较了几种可用于识别预测误差中异常模式的技术。测试技术的选择是由工业合作伙伴的需求驱动的,例如,有限数量的培训数据和模型的低培训和维护成本。最后,提出了今后工作的几个方向。
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引用次数: 2
A comparison of eight optimization methods applied to a wind farm layout optimization problem 八种优化方法在某风电场布局优化问题中的应用比较
IF 4 Q3 GREEN & SUSTAINABLE SCIENCE & TECHNOLOGY Pub Date : 2023-06-01 DOI: 10.5194/wes-8-865-2023
Jared J. Thomas, Nicholas F. Baker, P. Malisani, Erik Quaeghebeur, Sebastian Sanchez Perez-Moreno, John P. Jasa, C. Bay, F. Tilli, David Bieniek, N. Robinson, A. Stanley, Wesley Holt, A. Ning
Abstract. Selecting a wind farm layout optimization method is difficult. Comparisons between optimization methods in different papers can be uncertain due to the difficulty of exactly reproducing the objective function. Comparisons by just a few authors in one paper can be uncertain if the authors do not have experience using each algorithm. In this work we provide an algorithm comparison for a wind farm layout optimization case study between eight optimization methods applied, or directed, by researchers who developed those algorithms or who had other experience using them. We provided the objective function to each researcher to avoid ambiguity about relative performance due to a difference in objective function. While these comparisons are not perfect, we try to treat each algorithm more fairly by having researchers with experience using each algorithm apply each algorithm and by having a common objective function provided for analysis. The case study is from the International Energy Association (IEA) Wind Task 37, based on the Borssele III and IV wind farms with 81 turbines. Of particular interest in this case study is the presence of disconnected boundary regions and concave boundary features. The optimization methods studied represent a wide range of approaches, including gradient-free, gradient-based, and hybrid methods; discrete and continuous problem formulations; single-run and multi-start approaches; and mathematical and heuristic algorithms. We provide descriptions and references (where applicable) for each optimization method, as well as lists of pros and cons, to help readers determine an appropriate method for their use case. All the optimization methods perform similarly, with optimized wake loss values between 15.48 % and 15.70 % as compared to 17.28 % for the unoptimized provided layout. Each of the layouts found were different, but all layouts exhibited similar characteristics. Strong similarities across all the layouts include tightly packing wind turbines along the outer borders, loosely spacing turbines in the internal regions, and allocating similar numbers of turbines to each discrete boundary region. The best layout by annual energy production (AEP) was found using a new sequential allocation method, discrete exploration-based optimization (DEBO). Based on the results in this study, it appears that using an optimization algorithm can significantly improve wind farm performance, but there are many optimization methods that can perform well on the wind farm layout optimization problem, given that they are applied correctly.
摘要风电场布局优化方法的选择是一个难点。由于难以精确地再现目标函数,不同论文中优化方法之间的比较可能不确定。如果作者没有使用每种算法的经验,那么一篇论文中只有几个作者的比较可能是不确定的。在这项工作中,我们为风电场布局优化案例研究提供了八种优化方法的算法比较,这些优化方法由开发这些算法的研究人员或有其他使用这些算法的经验的研究人员应用或指导。我们为每个研究人员提供了目标函数,以避免由于目标函数的差异而导致相对性能的歧义。虽然这些比较并不完美,但我们试图通过让具有使用每种算法经验的研究人员应用每种算法以及提供用于分析的共同目标函数来更公平地对待每种算法。案例研究来自国际能源协会(IEA)的第37项风能任务,基于拥有81台涡轮机的Borssele III和IV风力发电场。在这个案例研究中,特别感兴趣的是不连通边界区域和凹边界特征的存在。所研究的优化方法包括无梯度、基于梯度和混合方法;离散和连续问题的表述;单次运行和多次启动方式;以及数学和启发式算法。我们为每种优化方法提供了描述和参考(在适用的情况下),以及优缺点列表,以帮助读者确定适合其用例的方法。所有优化方法的性能都相似,优化后的尾迹损失值在15.48%到15.70%之间,而未优化的尾迹损失值为17.28%。每一种布局都是不同的,但所有的布局都表现出相似的特征。所有布局的相似性包括沿外部边界紧密排列风力涡轮机,在内部区域松散间隔涡轮机,以及在每个离散边界区域分配相似数量的涡轮机。采用一种新的顺序分配方法——基于离散勘探的优化方法(DEBO),找到了以年发电量(AEP)为目标的最佳布局。从本研究的结果来看,使用优化算法可以显著提高风电场的性能,但有许多优化方法只要应用得当,就可以很好地解决风电场布局优化问题。
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引用次数: 4
The dynamic coupling between the pulse wake mixing strategy and floating wind turbines 脉冲尾流混合策略与浮动式风力涡轮机之间的动态耦合
IF 4 Q3 GREEN & SUSTAINABLE SCIENCE & TECHNOLOGY Pub Date : 2023-05-31 DOI: 10.5194/wes-8-849-2023
Daniël Van Den Berg, D. De Tavernier, J. van Wingerden
Abstract. In recent years, control techniques such as dynamic induction control (often referred to as “the pulse”) have shown great potential in increasing wake mixing, with the goal of minimising turbine-to-turbine interaction within a wind farm. Dynamic induction control disturbs the wake by varying the thrust of the turbine over time, which results in a time-varying induction zone. If applied to a floating wind turbine, this time-varying thrust force will, besides changing the wake, change the motion of the platform. In light of the expected movement, this work investigates if applying the pulse to a floating wind turbine yields similar results to that of the pulse applied to bottom-fixed turbines. This is done by considering first the magnitude of motions of the floating wind turbine due to the application of a time-varying thrust force and secondly the effect of these motions on the wake mixing. A frequency response experiment shows that the movement of the floating turbine is heavily frequency dependent, as is the thrust force. Time domain simulations, using a free-wake vortex method with uniform inflow, show that the expected gain in average wind speed at a distance of 5 rotor diameters downstream is more sensitive to the excitation frequency compared to a bottom-fixed turbine with the same pulse applied. This is due to the fact that, at certain frequencies, platform motion decreases the thrust force variation and thus reduces the onset of wake mixing.
摘要近年来,诸如动态感应控制(通常称为“脉冲”)等控制技术在增加尾流混合方面显示出巨大的潜力,其目标是最小化风力发电场内涡轮与涡轮之间的相互作用。动态感应控制通过改变涡轮推力随时间的变化来干扰尾迹,从而导致时变感应区。如果应用于浮动风力涡轮机,这种时变推力除了改变尾流外,还会改变平台的运动。鉴于预期的运动,这项工作研究了将脉冲应用于浮动风力涡轮机是否会产生与应用于底部固定涡轮机相似的结果。这是通过首先考虑由于施加时变推力而引起的浮动风力涡轮机运动的大小,其次考虑这些运动对尾迹混合的影响来实现的。频率响应实验表明,浮动涡轮的运动与推力的频率密切相关。采用均匀入流的自由尾迹涡旋方法进行时域仿真,结果表明,在相同脉冲下,在下游5转子直径处的平均风速预期增益比底部固定涡轮对激励频率更为敏感。这是因为在一定频率下,平台运动减少了推力变化,从而减少了尾流混合的发生。
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引用次数: 1
A new RANS-based wind farm parameterization and inflow model for wind farm cluster modeling 一种新的基于ranss的风电场参数化及入流模型用于风电场集群建模
IF 4 Q3 GREEN & SUSTAINABLE SCIENCE & TECHNOLOGY Pub Date : 2023-05-26 DOI: 10.5194/wes-8-819-2023
M. P. van der Laan, O. García-Santiago, M. Kelly, A. M. Meyer Forsting, C. Dubreuil-Boisclair, Knut Sponheim Seim, Marc Imberger, A. Peña, N. Sørensen, P. Réthoré
Abstract. Offshore wind farms are more commonly installed in wind farm clusters, where wind farm interaction can lead to energy losses; hence, there is a need for numerical models that can properly simulate wind farm interaction.This work proposes a Reynolds-averaged Navier–Stokes (RANS) method to efficiently simulate the effect of neighboring wind farms on wind farm power and annual energy production. First, a novel steady-state atmospheric inflow is proposed and tested for the application of RANS simulations of large wind farms. Second, a RANS-based wind farm parameterization is introduced, the actuator wind farm (AWF) model, which represents the wind farm as a forest canopy and allows to use of coarser grids compared to modeling all wind turbines as actuator disks (ADs). When the horizontal resolution of the RANS-AWF model is increased, the model results approach the results of the RANS-AD model. A double wind farm case is simulated with RANS to show that replacing an upstream wind farm with an AWF model only causes a deviation of less than 1 % in terms of the wind farm power of the downstream wind farm. Most importantly, a reduction in CPU hours of 75.1 % is achieved, provided that the AWF inputs are known, namely, wind farm thrust and power coefficients. The reduction in CPU hours is further reduced when all wind farms are represented by AWF models, namely, 92.3 % and 99.9 % for the double wind farm case and for a wind farm cluster case consisting of three wind farms, respectively. If the wind farm thrust and power coefficient inputs are derived from RANS-AD simulations, then the CPU time reduction is still 82.7 % for the wind farm cluster case.For the double wind farm case, the RANS models predict different wind speed flow fields compared to output from simulations performed with the mesoscale Weather Research and Forecasting model, but the models are in agreement with the inflow wind speed of the downstream wind farm. The RANS-AD-AWF model is also validated with measurements in terms of wind farm wake shape; the model captures the trend of the measurements for a wide range of wind directions, although the measurements indicate more pronounced wind farm wake shapes for certain wind directions.
摘要海上风电场通常安装在风电场集群中,风电场的相互作用可能导致能源损失;因此,需要能够正确模拟风电场相互作用的数值模型。这项工作提出了一种雷诺平均Navier-Stokes(RANS)方法,以有效模拟相邻风电场对风电场功率和年能源生产的影响。首先,提出了一种新的稳态大气入流,并对其在大型风电场RANS模拟中的应用进行了测试。其次,引入了一种基于RANS的风电场参数化,即致动器风电场(AWF)模型,该模型将风电场表示为林冠,与将所有风力涡轮机建模为致动器盘(AD)相比,可以使用更粗糙的网格。当RANS-AWF模型的水平分辨率增加时,模型结果接近RANS-AD模型的结果。用RANS模拟了一个双风电场的情况,表明用AWF模型代替上游风电场只会导致小于1的偏差 % 就下游风电场的风电场功率而言。最重要的是,CPU小时数减少了75.1 % 假设AWF输入是已知的,即风电场推力和功率系数。当所有风电场都用AWF模型表示时,CPU小时数的减少进一步减少,即92.3 % 和99.9 % 对于双风电场情况和对于分别由三个风电场组成的风电场集群情况。如果风电场推力和功率系数输入来自RANS-AD模拟,则CPU时间减少仍然为82.7 % 风电场集群案例。对于双风电场的情况,与中尺度天气研究和预测模型进行的模拟输出相比,RANS模型预测了不同的风速流场,但这些模型与下游风电场的流入风速一致。RANS-AD-AWF模型也通过风电场尾流形状的测量进行了验证;该模型捕捉了大范围风向的测量趋势,尽管测量结果表明某些风向的风电场尾流形状更明显。
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引用次数: 2
Design optimization of offshore wind jacket piles by assessing support structure orientation relative to metocean conditions 基于海、空条件下支撑结构取向的海上风管桩设计优化
IF 4 Q3 GREEN & SUSTAINABLE SCIENCE & TECHNOLOGY Pub Date : 2023-05-25 DOI: 10.5194/wes-8-807-2023
Maciej M. Mroczek, S. Arwade, M. Lackner
Abstract. The orientation of a three-legged offshore wind jacket structure in 60 m water depth, supporting the IEA 15 MW reference turbine, has been assessed for optimizing the jacket pile design. A reference site off the coast of Massachusetts was considered, including site-specific metocean conditions and realistically plausible geotechnical conditions. Soil–structure interaction was modeled using three-dimensional finite-element (FE) ground–structure simulations to obtain equivalent mudline springs, which were subsequently used in nonlinear elastic simulations, considering aerodynamic and hydrodynamic loading of extreme sea states in the time domain. Jacket pile loads were found to be sensitive to the maximum 50-year wave direction, as opposed to the wind direction, indicating that the jacket orientation should be considered relative to the dominant wave direction. The results further demonstrated that the jacket orientation has a substantial impact on the overall jacket pile mass and maximum pile embedment depth and therefore represents an important opportunity for project cost and risk reductions. Finally, this research highlights the importance of detailed knowledge of the full global model behavior (both turbine and foundation) for capturing this optimization potential, particularly due to the influence of wind–wave misalignment on pile loads. Close collaboration between the turbine supplier and foundation designer, at the appropriate design stages, is essential.
摘要60年三腿海上风电导管架结构的定向 m水深,支持IEA 15 MW参考涡轮机已被评估用于优化导管架桩设计。考虑了马萨诸塞州海岸外的参考场地,包括特定地点的海洋气象条件和现实可行的岩土工程条件。使用三维有限元(FE)地面-结构模拟对土壤-结构相互作用进行建模,以获得等效泥线弹簧,随后将其用于非线性弹性模拟,考虑时域中极端海况的空气动力学和水动力载荷。与风向相反,导管架桩荷载对最大50年波浪方向敏感,这表明导管架方向应相对于主导波浪方向考虑。结果进一步表明,导管架方向对导管架桩的整体质量和最大埋深有很大影响,因此为降低项目成本和风险提供了重要机会。最后,这项研究强调了完整的全局模型行为(包括涡轮机和基础)的详细知识对于捕捉这种优化潜力的重要性,特别是由于风浪偏差对桩荷载的影响。在适当的设计阶段,涡轮机供应商和基础设计师之间的密切合作至关重要。
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引用次数: 1
Investigating energy production and wake losses of multi-gigawatt offshore wind farms with atmospheric large-eddy simulation 利用大气大涡模拟研究数十亿瓦海上风电场的能量产生和尾流损失
IF 4 Q3 GREEN & SUSTAINABLE SCIENCE & TECHNOLOGY Pub Date : 2023-05-22 DOI: 10.5194/wes-8-787-2023
P. Baas, R. Verzijlbergh, Pim van Dorp, Harm J. J. Jonker
Abstract. As a consequence of the rapid growth of the globally installed offshore wind energy capacity, the size of individual wind farms is increasing. This poses a challenge to models that predict energy production. For instance, the current generation of wake models has mostly been calibrated on existing wind farms of much smaller size. This work analyzes annual energy production and wake losses for future, multi-gigawatt wind farms with atmospheric large-eddy simulation. To that end, 1 year of actual weather has been simulated for a suite of hypothetical 4 GW offshore wind farm scenarios. The scenarios differ in terms of applied turbine type, installed capacity density, and layout. The results suggest that production numbers increase significantly when the rated power of the individual turbines is larger while keeping the total installed capacity the same. Even for turbine types with similar rated power but slightly different power curves, significant differences in production were found. Although wind speed was identified as the most dominant factor determining the aerodynamic losses, a clear impact of atmospheric stability and boundary layer height has been identified. By analyzing losses of the first-row turbines, the yearly average global-blockage effect is estimated to between 2 and 3 %, but it can reach levels over 10 % for stably stratified conditions and wind speeds around 8 m s−1. Using a high-fidelity modeling technique, the present work provides insights into the performance of future, multi-gigawatt wind farms for a full year of realistic weather conditions.
摘要由于全球海上风能装机容量的快速增长,单个风电场的规模正在增加。这对预测能源生产的模型提出了挑战。例如,当前一代的尾流模型大多是在现有规模小得多的风电场上进行校准的。这项工作通过大气大涡模拟分析了未来数十亿瓦风电场的年发电量和尾流损失。为此,我们模拟了一组假设的4年实际天气 GW海上风电场场景。应用的涡轮机类型、装机容量密度和布局不同。结果表明,在保持总装机容量不变的情况下,当单个涡轮机的额定功率较大时,产量显著增加。即使对于额定功率相似但功率曲线略有不同的涡轮机类型,也发现产量存在显著差异。尽管风速被确定为决定空气动力学损失的最主要因素,但已经确定了大气稳定性和边界层高度的明显影响。通过分析第一排涡轮机的损失,估计年平均全球阻塞效应在2到3之间 %, 但它可以达到10以上的水平 % 对于稳定的分层条件和大约8的风速 m s−1。利用高保真建模技术,本工作深入了解了未来数十亿瓦风电场在全年真实天气条件下的性能。
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引用次数: 2
Gaussian mixture models for the optimal sparse sampling of offshore wind resource 海上风电资源最优稀疏采样的高斯混合模型
IF 4 Q3 GREEN & SUSTAINABLE SCIENCE & TECHNOLOGY Pub Date : 2023-05-17 DOI: 10.5194/wes-8-771-2023
Robin Marcille, M. Thiébaut, P. Tandeo, J. Filipot
Abstract. Wind resource assessment is a crucial step for the development of offshore wind energy. It relies on the installation of measurement devices, whose placement is an open challenge for developers. Indeed, the optimal sensor placement for field reconstruction is an open challenge in the field of sparse sampling. As for the application to offshore wind field reconstruction, no similar study was found, and standard strategies are based on semi-empirical choices. In this paper, a sparse sampling method using a Gaussian mixture model on numerical weather prediction data is developed for offshore wind reconstruction. It is applied to France's main offshore wind energy development areas: Normandy, southern Brittany and the Mediterranean Sea. The study is based on 3 years of Météo-France AROME's data, available through the MeteoNet data set. Using a Gaussian mixture model for data clustering, it leads to optimal sensor locations with regards to wind field reconstruction error. The proposed workflow is described and compared to state-of-the-art methods for sparse sampling. It constitutes a robust yet simple method for the definition of optimal sensor siting for offshore wind field reconstruction. The described method applied to the study area output sensor arrays of respectively seven, four and four sensors for Normandy, southern Brittany and the Mediterranean Sea. Those sensor arrays perform approximately 20 % better than the median Monte Carlo case and more than 30 % better than state-of-the-art methods with regards to wind field reconstruction error.
摘要风资源评估是海上风能开发的关键环节。它依赖于测量设备的安装,而测量设备的放置对开发人员来说是一个公开的挑战。事实上,用于场重建的最佳传感器放置在稀疏采样领域是一个公开的挑战。至于在海上风场重建中的应用,没有发现类似的研究,标准策略基于半经验选择。本文提出了一种利用高斯混合模型对数值天气预测数据进行稀疏采样的方法,用于海上风电重建。它适用于法国主要的海上风能开发地区:诺曼底、布列塔尼南部和地中海。该研究基于美泰-法国AROME三年的数据,可通过MeteoNet数据集获得。使用高斯混合模型进行数据聚类,可以得到风场重建误差方面的最佳传感器位置。描述了所提出的工作流程,并将其与最先进的稀疏采样方法进行了比较。它构成了一种稳健而简单的方法,用于定义海上风场重建的最佳传感器选址。所描述的方法应用于研究区域,分别为诺曼底、布列塔尼南部和地中海的七个、四个和四个传感器的输出传感器阵列。这些传感器阵列执行大约20 % 比蒙特卡罗中值情况好,并且超过30 % 在风场重建误差方面优于现有技术的方法。
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引用次数: 0
Validation of an interpretable data-driven wake model using lidar measurements from a field wake steering experiment 利用现场尾流转向实验的激光雷达测量验证可解释的数据驱动尾流模型
IF 4 Q3 GREEN & SUSTAINABLE SCIENCE & TECHNOLOGY Pub Date : 2023-05-11 DOI: 10.5194/wes-8-747-2023
B.A.M. Sengers, G. Steinfeld, P. Hulsman, M. Kühn
Abstract. Data-driven wake models have recently shown a high accuracy in reproducing wake characteristics from numerical data sets. This study used wake measurements from a lidar-equipped commercial wind turbine and inflow measurements from a nearby meteorological mast to validate an interpretable data-driven surrogate wake model. The trained data-driven model was then compared to a state-of-the-art analytical wake model. A multi-plane lidar measurement strategy captured the occurrence of the wake curl during yaw misalignment, which had not yet conclusively been observed in the field. The comparison between the wake models showed that the available power estimations of a virtual turbine situated four rotor diameters downstream were significantly more accurate with the data-driven model than with the analytical model. The mean absolute percentage error was reduced by 19 % to 36 %, depending on the input variables used. Especially under turbine yaw misalignment and high vertical shear, the data-driven model performed better. Further analysis suggested that the accuracy of the data-driven model is hardly affected when using only supervisory control and data acquisition (SCADA) data as input. Although the results are only obtained for a single turbine type, downstream distance and range of yaw misalignments, the outcome of this study is believed to demonstrate the potential of data-driven wake models.
摘要数据驱动的尾流模型最近在从数值数据集再现尾流特征方面显示出很高的精度。本研究使用配备激光雷达的商用风力涡轮机的尾流测量和附近气象桅杆的入流测量来验证可解释数据驱动的替代尾流模型。经过训练的数据驱动模型然后与最先进的分析尾流模型进行比较。一种多平面激光雷达测量策略捕获了在偏航失调期间尾流旋度的发生,这在现场尚未得到确切的观察。两种尾迹模型的比较表明,数据驱动模型对位于下游4个转子直径处的虚拟涡轮可用功率的估计明显优于分析模型。根据所使用的输入变量,平均绝对百分比误差减少了19%至36%。特别是在涡轮偏航失调和高垂直剪切情况下,数据驱动模型的性能更好。进一步分析表明,当仅使用监控和数据采集(SCADA)数据作为输入时,数据驱动模型的准确性几乎不受影响。虽然结果仅针对单一涡轮类型,下游距离和偏航失调范围,但本研究的结果被认为证明了数据驱动尾流模型的潜力。
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引用次数: 2
Comparison of optimal power production and operation of unmoored floating offshore wind turbines and energy ships 非系泊浮式海上风力发电机与能源船最优发电与运行比较
IF 4 Q3 GREEN & SUSTAINABLE SCIENCE & TECHNOLOGY Pub Date : 2023-05-08 DOI: 10.5194/wes-8-725-2023
Patrick Connolly, C. Crawford
Abstract. As the need to transition from global reliance on fossil fuels grows, solutions for producing green alternative fuels are necessary.These fuels will be especially important for hard-to-decarbonize sectors such as shipping.Mobile offshore wind energy systems (MOWESs) have been proposed as one such solution.These systems aim to harness the far-offshore wind resource, which is abundant and yet untapped because of installation and grid-connection limitations.Two classes of MOWES have been proposed in the literature: unmoored floating offshore wind turbines (UFOWTs) and energy ships (ESs).Both systems operate as autonomous power-to-X (PtX) plants, powered entirely by wind energy, and so can be used to produce synthetic green fuels such as hydrogen or ammonia, or for other energy-intensive applications such as direct air carbon capture.The two technologies differ in form; UFOWTs are based on a conventional FOWT but include propellers in place of mooring lines for course keeping, while ESs operate like a sailing ship and generate power via hydro-turbines mounted on the underside of the hull.Though much research and development is necessary for these systems to be feasible, the promise of harnessing strong winds far offshore, as well as the potential to avoid siting regulatory challenges, is enticing. This paper develops models of each MOWES concept to compare their power production on a consistent basis.The performance of the technologies is examined at steady-state operating points across relative wind speeds and angles.An optimization scheme is used to determine the values of the control variables which define the operating point for each set of environmental conditions.Results for each model show good agreement with published results for both UFOWTs and ESs.Model results suggest that UFOWTs can generate more power than ESs under ideal environmental conditions but are very sensitive to off-design operating conditions.In above-rated wind speeds, the UFOWT is able to produce as much power as a conventional, moored FOWT, whereas the ES cannot, since some power is always consumed to spin the Flettner rotors.The models developed here and their results may both be useful in future works that focus on the routing of UFOWTs or holistically designing a mobile UFOWT.Although differences in the performance of the systems have been identified, more work is necessary to discern which is a more viable producer of green electrofuels (e-fuels).
摘要随着从全球依赖化石燃料过渡的需求增加,生产绿色替代燃料的解决方案是必要的。这些燃料对航运等难以脱碳的行业尤其重要。移动式海上风能系统(MOWES)已被提议作为这样的解决方案之一。这些系统旨在利用遥远的海上风电资源,由于安装和电网连接的限制,该资源丰富但尚未开发。文献中提出了两类MOWES:无人操纵的浮动海上风力涡轮机(UFOWT)和能源船(ES)。这两种系统都是完全由风能驱动的自主发电厂,因此可以用于生产合成绿色燃料,如氢气或氨,或用于其他能源密集型应用,如直接空气碳捕获。这两种技术在形式上不同;UFOWT基于传统的FOWT,但包括螺旋桨代替系泊缆以保持航向,而ES则像帆船一样运行,并通过安装在船体下侧的水轮机发电。尽管这些系统的可行性需要大量的研究和开发,但利用离岸强风的前景以及避免选址监管挑战的潜力是诱人的。本文开发了每个MOWES概念的模型,以在一致的基础上比较它们的发电量。这些技术的性能是在相对风速和角度的稳态运行点进行检查的。优化方案用于确定控制变量的值,该控制变量定义了每组环境条件的操作点。每个模型的结果都与UFOWT和ES的已发表结果一致。模型结果表明,在理想的环境条件下,UFOWT可以产生比ES更多的功率,但对非设计运行条件非常敏感。在高于额定风速的情况下,UFOWT能够产生与传统系泊FOWT一样多的功率,而ES则不能,因为旋转Flettner转子总是需要消耗一些功率。这里开发的模型及其结果可能在未来的工作中都很有用,这些工作侧重于不明飞行物的路由或整体设计移动不明飞行物。尽管已经发现了系统性能的差异,但还需要做更多的工作来辨别哪种是更可行的绿色电燃料(电子燃料)生产商。
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引用次数: 0
The wind farm as a sensor: learning and explaining orographic and plant-induced flow heterogeneities from operational data 作为传感器的风电场:从运行数据中学习和解释地形和植物诱导的流异质性
IF 4 Q3 GREEN & SUSTAINABLE SCIENCE & TECHNOLOGY Pub Date : 2023-05-05 DOI: 10.5194/wes-8-691-2023
R. Braunbehrens, A. Vad, C. Bottasso
Abstract. This paper describes a method to identify the heterogenous flowcharacteristics that develop within a wind farm in its interaction with theatmospheric boundary layer. The whole farm is used as a distributed sensor,which gauges through its wind turbines the flow field developing withinits boundaries. The proposed method is based on augmenting an engineeringwake model with an unknown correction field, which results in a hybrid(grey-box) model. Operational SCADA (supervisory control and data acquisition) data are then used to simultaneously learn the parameters that describe the correction field and to tune the ones of the engineering wake model. The resulting monolithic maximum likelihood estimation is in general ill-conditioned because of the collinearity and low observability of the redundant parameters. This problem is solved by a singular value decomposition, which discards parameter combinations that are not identifiable given the informational content of the dataset and solves only for the identifiable ones. The farm-as-a-sensor approach is demonstrated on two wind plants with verydifferent characteristics: a relatively small onshore farm at a site withmoderate terrain complexity and a large offshore one in close proximity tothe coastline. In both cases, the data-driven correction and tuning of thegrey-box model results in much improved prediction capabilities. Theidentified flow fields reveal the presence of significant terrain-inducedeffects in the onshore case and of large direction and ambient-condition-dependent intra-plant effects in the offshore one. Analysis of the coordinate transformation and mode shapes generated by the singular value decomposition help explain relevant characteristics of the solution, as well as couplings among modeling parameters. Computational fluid dynamics (CFD) simulations are used for confirming the plausibility of the identified flow fields.
摘要本文描述了一种识别风电场与大气边界层相互作用中发展的非均质流动特性的方法。整个农场被用作分布式传感器,通过其风力涡轮机测量边界内发展的流场。该方法是基于一个未知修正场的工程尾流模型,从而得到一个混合(灰盒)模型。然后使用操作SCADA(监控和数据采集)数据同时学习描述校正场的参数并调整工程尾流模型的参数。由于冗余参数的共线性和低可观测性,所得到的整体最大似然估计通常是病态的。这个问题是通过奇异值分解来解决的,奇异值分解会丢弃给定数据集的信息内容无法识别的参数组合,只解决可识别的参数组合。这种“农场即传感器”的方法在两个特点截然不同的风电场上得到了验证:一个是相对较小的陆上风电场,位于地形中等复杂的地点,另一个是靠近海岸线的大型海上风电场。在这两种情况下,数据驱动的修正和灰盒模型的调优都大大提高了预测能力。所识别的流场表明,在陆上情况下存在显著的地形诱导效应,在海上情况下存在较大的方向和环境条件依赖的植物内效应。分析奇异值分解产生的坐标变换和模态振型,有助于解释解的相关特征,以及建模参数之间的耦合关系。计算流体力学(CFD)模拟用于验证识别流场的合理性。
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引用次数: 2
期刊
Wind Energy Science
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