BIGDATA: IA: Collaborative Research: From Bytes to Watts - A Data Science Solution to Improve Wind Energy Reliability and Operation

BIGDATA：IA：协作研究：从字节到瓦特 - 提高风能可靠性和运行的数据科学解决方案

基本信息

批准号：
1741166
负责人：
Eunshin Byon
金额：
$ 27.5万
依托单位：
Regents of the University of Michigan - Ann Arbor
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-10-01 至 2022-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1741166&HistoricalAwards=false
关键词：
BIGDATA IA Collaborative Research Bytes

项目摘要

The collective efforts in aerospace, civil, electrical, and mechanical engineering areas have led to remarkable progresses in wind energy. Larger turbines are designed and installed, and wind farms are nowadays built at locations where wind is even more intermittent and maintenance equipment is less accessible. This adds new challenges to ensuring operational reliability. To cope with these challenges, along with the rapid advancement in microelectronics, modern wind farms are equipped with a large number and variety of sensors, including, at the turbine level, anemometers, tachometers, accelerometers, thermometers, strain sensors, and power meters, and at the farm level, anemometers, vanes, sonars, thermometers, humidity meters, pressure meters, among others. It is worth noting that all these data are currently analyzed/utilized only in their respective domains. The big data challenges in this project include how to best use spatio-temporal data for wind forecast, how to use data of different nature (wind, power, load etc.) and data of different sources (physical data versus computer simulation data) for power production assessment in a computationally efficient manner, and finally how to integrate these three sets of solutions into a reliable and efficient computational platform. The proposed research and education activities will make a paradigm shift in the wind industry by demonstrating how dramatically data science innovations can benefit the industry. The PIs will disseminate the research findings through classroom teaching, journal/conference publications, industry workshops, and data/software sharing. The summer internship opportunities and undergraduate research help train the next generation workforce to be better versed with data science methodologies.The critical barrier to cost effective wind power and its general adoption is partly rooted in wind stochasticity, severely complicating wind power production optimization and cost reduction. The long-term viability of wind energy hinges upon a good understanding of its production reliability, which is affected in turn by the predictability of wind and power productivity of wind turbines. Furthermore, the productivity of a wind turbine comprises two aspects: its ability of converting wind into power during its operation and the availability of wind turbines. Three inter-related research efforts will enhance wind energy reliability and productivity): (1) spatio-temporal analysis (for wind forecast) (2) conditional density estimation (for wind-to-power conversion assessment); and (3) importance sampling (for turbine reliability assessment and improvement). Significant data resourced provided by industry partners in the research, coupled with models and computational resources, will enable better prediction of wind profiles and utilization. In addition, the team will develop dedicated reconfigurable field programmable gate array (FPGA) processors that will be 50 to 500 times faster than general-purpose CPUs for both on-site and central control processing and have small form-factor, low cost and energy efficient to enable agile development under severe outdoor conditions at wind farms.

航空航天，民用，电气和机械工程领域的集体努力导致了风能的显着进步。较大的涡轮机是设计和安装的，如今，风电场是在风更间歇性且维护设备较低的地方建造的。这增加了确保运营可靠性的新挑战。 To cope with these challenges, along with the rapid advancement in microelectronics, modern wind farms are equipped with a large number and variety of sensors, including, at the turbine level, anemometers, tachometers, accelerometers, thermometers, strain sensors, and power meters, and at the farm level, anemometers, vanes, sonars, thermometers, humidity meters, pressure meters, among others.值得注意的是，目前仅在其各自的域中对所有这些数据进行分析/使用。该项目中的大数据挑战包括如何最好地使用时空数据进行风预测，如何使用不同性质（风，功率，负载等）以及不同源（物理数据与计算机模拟数据）的数据以计算有效的方式进行功率生产评估，以及如何将这三组解决方案集成到可靠的计算和有效的计算平台中。拟议的研究和教育活动将通过证明数据科学创新如何使该行业受益，从而在风能行业发生范式转变。 PI将通过课堂教学，期刊/会议出版物，行业研讨会和数据/软件共享来传播研究结果。暑期实习机会和本科研究有助于培训下一代劳动力，以更好地使用数据科学方法。具有成本效益的风能及其一般采用的关键障碍部分植根于风力随机性，严重复杂风力发电的优化和降低成本。风能的长期生存能力取决于对其生产可靠性的充分了解，这反过来又受风力涡轮机的风能和电力生产力的可预测性影响。此外，风力涡轮机的生产力包括两个方面：在操作过程中将风转换为动力的能力和风力涡轮机的可用性。三项相关研究工作将提高风能的可靠性和生产率）：（1）时空分析（用于风向预测）（2）条件密度估计（用于风向功率转换评估）；（3）重要性采样（用于涡轮的可靠性评估和改进）。行业合作伙伴在研究中提供的大量数据资源，再加上模型和计算资源，可以更好地预测风概况和利用率。此外，该团队将开发专用的可编程可编程栅极阵列（FPGA）处理器，该处理器将比通用CPU快50至500倍，用于现场和中央控制处理，并且在风力场的严重户外情况下，在严重的户外情况下，较小的形式因子，低成本和能源效率低。