STTR Phase I: Extremum Seeking Control of Wind Turbines and Wind Farms

STTR第一阶段：风力发电机和风电场的极值寻求控制

• 批准号: 2126855
• 负责人: Neal Fine
• 金额: $ 25.6万
• 依托单位: ARCTURA, INC.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2126855&HistoricalAwards=false
• 关键词: STTR; Phase; Extremum; Seeking; Control

The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase I project is the introduction of a new wind farm control strategy that is expected to increase the power generated by a typical wind farm by 3-10%. Wind farm owners and operators face continuous pressure to reduce the price per megawatt-hour of the grid energy that they produce. Improving the energy yield of existing wind farms by optimizing array yaw angles (known as “wake steering”) may lower the cost of energy, increase our customer’s profits, and promote greater wind energy penetration in domestic and global grid energy markets. This STTR Phase I project proposes to develop a new model-free approach to maximizing the annual energy produced by wind farms. On most wind farms, the turbines are operated independently, relying only on their individual wind sensors to determine the wind direction and to make control decisions to orient themselves to generate maximum power. The proposed approach will allow arrays of turbines to work together to minimize losses by steering the wakes of upstream turbines away from the downwind rows, increasing net power production. While this technique has been proposed in the past, the methods employed have relied on control algorithms that use models to determine the key operating parameters. Since every wind farm is different, those parameters must be tuned on-site using expensive and time-consuming calibration campaigns. Moreover, as the turbines age, those parameters can end up far from their optimal values. The new proposed scheme takes a different, model-free approach that may eliminate the need for parameter calibration while ensuring that the array continuously operates at its peak performance even as atmospheric and other environmental conditions change and the physical conditions of the turbines deteriorate.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

小型企业技术转让(STTR)第一阶段项目的更广泛影响/商业潜力是引入新的风电场控制策略，预计将使典型风电场的发电量增加3%-10%。风电场所有者和运营商面临着持续不断的压力，要求他们降低每兆瓦时的电网能源价格。通过优化阵列偏航角度(称为“尾流转向”)来提高现有风电场的发电量，可能会降低能源成本，增加我们客户的利润，并促进风能在国内和全球电网能源市场的更大渗透。这个STTR第一阶段项目建议开发一种新的无模型方法来最大化风力发电场的年发电量。在大多数风力发电场，涡轮机都是独立运行的，只依靠各自的风力传感器来确定风向，并做出控制决策，以定位自己以产生最大功率。拟议的方法将允许涡轮机阵列协同工作，通过引导上游涡轮机的尾迹远离顺风排，增加净发电量，从而将损失降至最低。虽然这种技术在过去已经被提出，但所采用的方法依赖于使用模型来确定关键操作参数的控制算法。由于每个风电场都是不同的，这些参数必须通过昂贵且耗时的校准活动在现场进行调整。此外，随着涡轮机的老化，这些参数最终可能会远远偏离其最佳值。新提出的方案采用了一种不同的、无模型的方法，可以消除参数校准的需要，同时确保阵列在大气和其他环境条件变化以及涡轮机的物理条件恶化时继续以其峰值性能运行。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。