Advanced Signal Processing for Smard Grid and Renewable Energy Sources

适用于智能电网和可再生能源的高级信号处理

• 批准号: 1405327
• 负责人: Xiaodong Wang
• 金额: $ 30万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1405327&HistoricalAwards=false
• 关键词: Advanced; Signal; Processing; Smard; Grid

Modernizing the electric power grid has become a major national priority for many countries across the globe. With the increasing penetration of renewable and distributed energy sources along with the necessary means of energy storage technologies, it is envisioned that the so-called "smart grids" will make the production and delivery of electricity more reliable and more cost-effective, and will allow consumers to make more informed decisions about their energy consumption. The smart grid transforms the legacy grid that provides a one-way centrally generated power flow to end users into a more distributed and dynamic system of two-way flow of power and information. The essential concept of the smart grid, where the intelligence will be to a large extent distributed, is the integration of power electronics, real-time metering, digital communications, signal processing, and control technologies into the power system. Communications and information technology play a critical role in the smart grid. As the power grid becomes more complex, more interconnected, and more intelligent, large amount of data will be generated by meters, sensors and synchrophasors. Advanced techniques for managing, analyzing and acting on such data need to be developed. Further, as more and more renewable energy sources, such as photovoltaic (PV) solar arrays and wind turbine arrays are deployed, novel techniques are needed to optimize and monitor the energy generation performance. The numerous technical challenges that accompany the future smart-grid systems call for novel solutions. Hence it is important at this time to perform research that addresses the theoretical aspects of smart grid and renewable energy sources, and to acquire insights and theoretical tools that may help propel significant advances in this field. This project focuses on three major topics that are related to the distributed intelligence for smart grid and renewable energy sources: (1) to develop distributed and secure nonlinear state estimation methods for both power transmission and power distribution grids; (2) to develop decentralized sequential joint change detection and estimation algorithms for real-time detection and mitigation of cyber attacks in smart grid; and (3) to develop decentralized model-free adaptive algorithms for online optimization and monitoring of solar PV arrays, and for controlling of wind turbine arrays, respectively. Smart grid and renewable energy sources bring profound changes to the society and the proposed research will lead to new and powerful techniques for grid state estimation, cyber attack detection and mitigation, and efficient utilization of renewable energy sources. In addition to conducting theoretical analysis, computational procedures will be developed to facilitate the analytical work. The new concepts and algorithms developed under ideal conditions will be tailored to operate in practical systems with various constraints. It is expected that the proposed research will not only enhance our understanding of the fundamental underpinnings of the complex smart-grid systems and renewable energy sources, but also produce new and powerful tools for future electrical power systems. By coordinating with an established outreach program, this project will actively engage K-12 students and traditionally under-represented groups and inspire these students to pursue STEM (science, technology, engineering and mathematics) education and careers.

电网现代化已成为全球许多国家的主要国家优先事项。随着可再生能源和分布式能源的日益普及以及储能技术的必要手段，预计所谓的“智能电网”将使电力的生产和输送更可靠和更具成本效益，并将使消费者能够更明智地决定他们的能源消耗。智能电网将向终端用户提供单向集中发电潮流的传统电网转变为一个更分布式、更动态的电力和信息双向流动系统。智能电网的基本概念是将电力电子、实时计量、数字通信、信号处理和控制技术集成到电力系统中，智能电网的智能在很大程度上将是分布式的。通信和信息技术在智能电网中起着至关重要的作用。随着电网变得更加复杂、互联和智能化，仪表、传感器和同步器将产生大量的数据。需要开发管理、分析和处理此类数据的先进技术。此外，随着越来越多的可再生能源，如光伏(PV)太阳能电池阵列和风力涡轮机阵列的部署，需要新的技术来优化和监测发电性能。伴随着未来智能电网系统的众多技术挑战，需要新的解决方案。因此，目前重要的是开展研究，解决智能电网和可再生能源的理论问题，并获得可能有助于推动该领域重大进展的见解和理论工具。本项目致力于与智能电网和可再生能源的分布式智能相关的三个主要主题：(1)开发用于输电和配电网的分布式和安全的非线性状态估计方法；(2)开发用于实时检测和缓解智能电网中的网络攻击的分散序贯联合变化检测和估计算法；(3)分别开发用于太阳能光伏阵列在线优化和监测的分布式无模型自适应算法和用于风力机阵列控制的分布式无模型自适应算法。智能电网和可再生能源给社会带来了深刻的变化，提出的研究将为电网状态估计、网络攻击检测和缓解以及可再生能源的高效利用带来新的强大技术。除了进行理论分析外，还将开发计算程序以促进分析工作。在理想条件下开发的新概念和算法将被量身定做，以适用于具有各种约束的实际系统。预计拟议的研究不仅将增进我们对复杂智能电网系统和可再生能源的基本基础的了解，而且还将为未来的电力系统提供新的强大工具。通过与既定的外展计划协调，该项目将积极吸引K-12学生和传统上代表性不足的群体，并激励这些学生继续STEM(科学、技术、工程和数学)教育和职业生涯。