Sensory Data Analytics for Securing Wind Farm Generation Against Disruptive Events

整合更高水平的风力发电是为国家建设安全和可持续能源基础设施的关键一步。虽然风力发电是绿色和免费的，但它可能相当不稳定和间歇性，这与以可靠和按需方式收集风能的巨大挑战背道而驰。该提案旨在通过开发在线和原位传感数据分析工具，解决确保风力发电场可靠输出的基本挑战。通过利用从广泛部署在风电场的分散气象和电力传感器收集的各种测量数据中包含的丰富信息，拟议项目开发的工具将有助于预防控制和决策，以应对即将发生的破坏性天气事件或极端电网状况，否则将导致剧烈的电力倾斜或振荡。通过将风力发电工程和传感数据分析结合起来进行研究和教育，提议的工作是跨学科和变革性的。拟议项目的综合研究和教育活动将为蓬勃发展的风能行业培养具有不同专业知识的未来工程师，并有助于实现美国能源部到2030年风能占比20%的目标，以及美国许多州立法的可再生能源投资组合标准。拟议项目研究风电场传感数据分析的两个重点领域：1)利用涡轮级功率测量检测和量化即将发生的前诱导斜坡事件；2)利用同步相量数据检测和分析次同步相互作用。其中，锋面诱导坡道事件检测是一类新的具有空间依赖性的多时间序列变化检测问题，该问题利用风电场的地理布局信息和从汽轮级功率测量中提取的信息对锋面和诱导风力坡道的运动进行量化。所提出的工作的一个智力优点来自于从多个相关涡轮级功率测量中发现前诱导斜坡事件特征的独特而重要的见解。提出的工作的另一个智力优点是设计了无模型和非参数方法，用于使用同步相量数据和同步压缩变换进行次同步交互检测。针对这两个问题提出的方法都是数据驱动的，因此为可靠的风电场运行提供了非侵入式状态监测和破坏性事件检测的新途径，这构成了拟议工作的变革方面。

Integrating higher levels of wind power is a critical step to building a secure and sustainable energy infrastructure for the nation. While being green and free, wind power generation can be quite volatile and intermittent, which opposes grand challenges for harvesting wind energy in a reliable and on-demand manner. This proposal aims to address the fundamental challenges of securing reliable power output from wind farms, through the development of online and in-situ sensory data analytics tools. By leveraging the rich information contained in the diverse measurements collected from dispersed meteorological and power sensors widely deployed at wind farms, the tools developed by the proposed project would assist in preventive controls and decision making against impending disruptive weather events or extreme grid conditions that would otherwise cause dramatic power ramps or oscillations. By bridging wind power engineering and sensory data analytics for research and education, the proposed work is interdisciplinary and transformative. The integrated research and education activities of the proposed project would train future engineers with diverse expertise for a thriving wind energy industry, and contribute to fulfilling the DOE's goal of 20% wind energy by 2030 as well as the renewable portfolio standards legislated in many states of the U.S.The proposed project studies wind farm sensory data analytics in two thrust areas: 1) detection and quantification of impending front-induced ramp events by using turbine-level power measurements, and 2) detection and mode analysis of sub-synchronous interactions by using synchrophasor data. Particularly, front-induced ramp event detection is formulated as a new class of change detection problems for multiple time series with spatial dependencies, in which the movement of weather front and the induced wind power ramp are quantified by using wind farm's geographical layout information together with the information extracted from turbine-level power measurements. One intellectual merit of the proposed work arises from the unique and significant insight into discovering the signatures of front-induced ramp events from multiple correlated turbine-level power measurements. Another intellectual merit of the proposed work is the design of model-less and non-parametric methods for sub-synchronous interaction detection using synchrophasor data and synchrosqueezing transform. The proposed approaches to both problems are data-driven, and thus provide novel avenues for non-intrusive condition monitoring and disruptive event detection for reliable wind farm operations, which constitutes the transformative aspect of the proposed work.