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、土木、电气和机械工程领域的集体努力导致风能取得了显着进展。如今，人们设计和安装了更大的涡轮机，而风电场则建在风力更加间歇性且维护设备更难到达的地方。这给确保运行可靠性带来了新的挑战。为了应对这些挑战，随着微电子技术的快速发展，现代风电场配备了大量且种类繁多的传感器，包括涡轮级的风速计、转速计、加速度计、温度计、应变传感器和功率计，在农场层面，有风速计、风向标、声纳、温度计、湿度计、压力计等。值得注意的是，所有这些数据目前仅在各自的领域进行分析/利用。该项目中的大数据挑战包括如何最好地利用时空数据进行风预报，如何利用不同性质的数据（风、电力、负荷等）和不同来源的数据（物理数据与计算机模拟数据）进行风预报。以计算高效的方式进行发电评估，最后如何将这三套解决方案集成到一个可靠且高效的计算平台中。拟议的研究和教育活动将通过展示数据科学创新如何使风能行业受益匪浅，从而实现风能行业的范式转变。 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成果。暑期实习机会和本科生研究有助于培训下一代劳动力，使其更好地掌握数据科学方法。风电成本效益及其普遍采用的关键障碍部分源于风的随机性，使风电生产优化和成本降低严重复杂化。风能的长期可行性取决于对其生产可靠性的充分了解，而生产可靠性又受到风能的可预测性和风力涡轮机发电效率的影响。此外，风力涡轮机的生产力包括两个方面：其在运行期间将风转化为电力的能力以及风力涡轮机的可用性。三项相互关联的研究工作将提高风能的可靠性和生产力）：（1）时空分析（用于风预报）（2）条件密度估计（用于风电转换评估）； (3) 重要性抽样（用于涡轮机可靠性评估和改进）。行业合作伙伴在研究中提供的重要数据资源，加上模型和计算资源，将能够更好地预测风廓线和利用。此外，该团队还将开发专用的可重构现场可编程门阵列（FPGA）处理器，该处理器的速度比用于现场和中央控制处理的通用CPU快50至500倍，并且具有体积小、成本低和能耗低的特点。高效实现风电场恶劣室外条件下的敏捷开发。