Data–Driven Wake Steering Control for a Simulated Wind Farm Model

S. Simani, S. Farsoni, P. Castaldi
{"title":"Data–Driven Wake Steering Control for a Simulated Wind Farm Model","authors":"S. Simani, S. Farsoni, P. Castaldi","doi":"10.31875/2409-9694.2023.10.02","DOIUrl":null,"url":null,"abstract":"Abstract: Upstream wind turbines yaw to divert their wakes away from downstream turbines, increasing the power produced. Nevertheless, the majority of wake steering techniques rely on offline lookup tables that translate a set of parameters, including wind speed and direction, to yaw angles for each turbine in a farm. These charts assume that every turbine is working well, however they may not be very accurate if one or more turbines are not producing their rated power due to low wind speed, malfunctions, scheduled maintenance, or emergency maintenance. This study provides an intelligent wake steering technique that, when calculating yaw angles, responds to the actual operating conditions of the turbine. A neural network is trained live to determine yaw angles from operating conditions, including turbine status, using a hybrid model and a learning-based method, i.e. an active control. The proposed control solution does not need to solve optimization problems for each combination of the turbines’ non-optimal working conditions in a farm; instead, the integration of learning strategy in the control design enables the creation of an active control scheme, in contrast to purely model-based approaches that use lookup tables provided by the wind turbine manufacturer or generated offline. The suggested methodology does not necessitate a substantial amount of training samples, unlike purely learning-based approaches like model-free reinforcement learning. In actuality, by taking use of the model during back propagation, the suggested approach learns more from each sample. Based on the flow redirection and induction in the steady state code, results are reported for both normal (nominal) wake steering with all turbines operating as well as defective conditions. It is a free tool for optimizing wind farms that The National Renewable Energy Laboratory (USA) offers. These yaw angles are contrasted and checked with those discovered through the resolution of an optimization issue. Active wake steering is made possible by the suggested solution, which employs a hybrid model and learning-based methodology, through sample efficient training and quick online evaluation. Finally, a hardware-in-the-loop test-bed is taken into consideration for assessing and confirming the performance of the suggested solutions in a more practical setting.","PeriodicalId":234563,"journal":{"name":"International Journal of Robotics and Automation Technology","volume":"30 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Robotics and Automation Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.31875/2409-9694.2023.10.02","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Abstract: Upstream wind turbines yaw to divert their wakes away from downstream turbines, increasing the power produced. Nevertheless, the majority of wake steering techniques rely on offline lookup tables that translate a set of parameters, including wind speed and direction, to yaw angles for each turbine in a farm. These charts assume that every turbine is working well, however they may not be very accurate if one or more turbines are not producing their rated power due to low wind speed, malfunctions, scheduled maintenance, or emergency maintenance. This study provides an intelligent wake steering technique that, when calculating yaw angles, responds to the actual operating conditions of the turbine. A neural network is trained live to determine yaw angles from operating conditions, including turbine status, using a hybrid model and a learning-based method, i.e. an active control. The proposed control solution does not need to solve optimization problems for each combination of the turbines’ non-optimal working conditions in a farm; instead, the integration of learning strategy in the control design enables the creation of an active control scheme, in contrast to purely model-based approaches that use lookup tables provided by the wind turbine manufacturer or generated offline. The suggested methodology does not necessitate a substantial amount of training samples, unlike purely learning-based approaches like model-free reinforcement learning. In actuality, by taking use of the model during back propagation, the suggested approach learns more from each sample. Based on the flow redirection and induction in the steady state code, results are reported for both normal (nominal) wake steering with all turbines operating as well as defective conditions. It is a free tool for optimizing wind farms that The National Renewable Energy Laboratory (USA) offers. These yaw angles are contrasted and checked with those discovered through the resolution of an optimization issue. Active wake steering is made possible by the suggested solution, which employs a hybrid model and learning-based methodology, through sample efficient training and quick online evaluation. Finally, a hardware-in-the-loop test-bed is taken into consideration for assessing and confirming the performance of the suggested solutions in a more practical setting.
模拟风电场模型的数据驱动尾流转向控制
摘要:上游风力发电机偏航以使尾迹远离下游风力发电机,从而增加发电量。然而,大多数尾流转向技术依赖于离线查找表,将一组参数(包括风速和风向)转换为农场中每个涡轮机的偏航角。这些图表假设每个涡轮机都工作良好,但是,如果一个或多个涡轮机由于低风速,故障,定期维护或紧急维护而不能产生额定功率,则它们可能不是很准确。本研究提供了一种智能尾迹转向技术,该技术在计算偏航角时能够响应涡轮的实际运行情况。使用混合模型和基于学习的方法(即主动控制),实时训练神经网络以确定运行条件(包括涡轮机状态)的偏航角。所提出的控制方案不需要解决电场中每个涡轮机非最优工况组合的优化问题;相反,在控制设计中集成学习策略可以创建主动控制方案,而不是纯粹基于模型的方法,使用风力涡轮机制造商提供的查找表或离线生成。建议的方法不需要大量的训练样本,不像纯粹基于学习的方法,如无模型强化学习。实际上,通过在反向传播过程中使用该模型,该方法可以从每个样本中学习到更多的东西。基于稳态代码中的流重定向和诱导,报告了所有涡轮机正常(标称)尾流转向和缺陷条件下的结果。这是美国国家可再生能源实验室提供的一个免费的优化风力发电场的工具。将这些偏航角与通过解决优化问题发现的偏航角进行对比和检查。该解决方案采用混合模型和基于学习的方法,通过样本高效训练和快速在线评估,使主动尾流转向成为可能。最后,考虑了硬件在环测试平台,以在更实际的环境中评估和确认所建议的解决方案的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信