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一项重大国家优先事项。随着可再生能源和分布式能源的日益普及以及必要的储能技术手段，预计所谓的“智能电网”将使电力的生产和输送更加可靠和更具成本效益，并将让消费者能够对其能源消耗做出更明智的决定。智能电网将向最终用户提供单向集中发电电力流的传统电网转变为更加分布式和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，智能在很大程度上是分布式的。通信和信息技术在智能电网中发挥着至关重要的作用。随着电网变得更加复杂、更加互联、更加智能，仪表、传感器和同步相量将产生大量数据。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，例如光伏（PV）太阳能阵列和风力涡轮机阵列的部署，需要新技术来优化和监控发电性能。未来智能电网系统面临的众多技术挑战需要新颖的解决方案。因此，目前重要的是开展解决智能电网和可再生能源理论方面的研究，并获得有助于推动该领域重大进步的见解和理论工具。该项目重点关注与智能电网和可再生能源分布式智能相关的三个主要主题：（1）开发输电和配电网的分布式安全非线性状态估计方法； (2) 开发分散式顺序联合变化检测和估计算法，用于智能电网中网络攻击的实时检测和缓解； (3) 开发分散式无模型自适应算法，分别用于太阳能光伏阵列的在线优化和监测以及风力涡轮机阵列的控制。智能电网和可再生能源给社会带来了深刻的变化，拟议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定制以在具有各种约束的实际系统中运行。预计所提出的研究不仅将增强我们对复杂智能电网系统和可再生能源的基本基础的理解，而且还将为未来电力系统提供新的强大工具。通过与既定的外展计划相协调，该项目将积极吸引 K-12 学生和传统上代表性不足的群体，并激励这些学生追求 STEM（科学、技术、工程和数学）教育和职业。