EAGER: Real-Time: Intelligent Mitigation of Low-Frequency Oscillations in Smart Grid Using Real-time Learning

EAGER：实时：利用实时学习智能缓解智能电网中的低频振荡

• 批准号: 1839684
• 负责人: Yilu Liu
• 金额: $ 27.58万
• 依托单位: University of Tennessee Knoxville
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-15 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1839684&HistoricalAwards=false
• 关键词: EAGER; Real; Time; Intelligent; Mitigation

As a critical underpinning of modern society, the electric power grid is one of the most complex and man-made dynamic systems in the world. Numerous real-time data of different types, different components, and various locations are generated to monitor and control power grids. Currently, the control of large-scale power grids is still mainly based on the physical system model, while the hidden knowledge in the abovementioned large-volume data has not been fully exploited. This project selects one typical control function in smart grids, low-frequency oscillation control, to explore the potential to enhance smart grid controls using the hidden knowledge. Low-frequency oscillation is a common phenomenon in operation of large-scale power systems. If not controlled properly, these oscillations may degrade power system security and make a large number of customers lose their power. This project aims at developing an intelligent controller to mitigate these low-frequency oscillations using data and machine learning technologies. If successful, it will advance the technology in smart grid, and remove obstacles for application of machine learning technologies in smart grid control. The proposed approach will contribute to more secure, reliable and economic operations of U.S. power grids. For example, the risk of blackout can be significantly mitigated; and thus outage cost could be saved, e.g., more than $1 billion for U.S. western grid collapse in 1996. The proposed project is also coupled with a broad dissemination of research findings and a strong educational component to engage students from underrepresented groups. The proposed research effort focuses on a completely new design methodology of intelligent oscillation damping control using the data-driven models. These data-driven models of power grids derive from synchronized measurement data using machine learning technologies, in conjunction with power grid domain knowledge. Specifically, this project will: (1) build a self-evolving dynamic knowledge base based on historical measurement data under different oscillation scenarios; (2) extract the critical features from historical and real-time data, and select the optimal features to improve data-driven model prediction accuracy; (3) develop machine learning algorithms to predict data-driven models for oscillation damping control design; and (4) validate and demonstrate the proposed methodology via computer simulations and hardware testbed experiments. This advanced approach will contribute to the energy security and efficiency of the U.S. electric power grids. This project will expose both undergraduate and graduate students to the state-of-the-art machine learning education and workforce training program. By coordinating with an established outreach program in an existing NSF/DOE engineering research center, the research results will be integrated into weekly seminars and short courses that are accessible to four partner universities, nine affiliate universities and more than 35 industry partners. Moreover, this project will encourage students to get involved with STEM (Science, Technology, Engineering and Mathematics) courses early in their pre-college years to prepare for STEM careers.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

作为现代社会的关键基础，电网是世界上最复杂，人造的动态系统之一。生成不同类型，不同组件和各种位置的许多实时数据，以监视和控制电网。当前，大规模电网的控制仍然主要基于物理系统模型，而上述大容量数据中的隐藏知识尚未完全利用。该项目在智能电网（低频振荡控制）中选择一个典型的控制功能，以探索使用隐藏知识增强智能网格控制的潜力。低频振荡是大规模动力系统运行中的常见现象。如果无法正确控制，这些振荡可能会降低电力系统安全性，并使大量客户失去电力。该项目旨在开发一个智能控制器，以使用数据和机器学习技术来减轻这些低频振荡。如果成功，它将在智能电网上推进技术，并消除在智能电网控制中应用机器学习技术的障碍。拟议的方法将有助于美国电力电网的更安全，可靠和经济的运营。例如，停电的风险可以大大减轻；因此，可以节省停电成本，例如，1996年美国西部电网崩溃的10亿美元以上。拟议的项目还与广泛的研究发现和强大的教育成分相结合，以吸引来自代表性不足的团体的学生。拟议的研究工作着重于使用数据驱动模型的智能振荡阻尼控制的全新设计方法。这些数据驱动的电网模型与电网知识结合使用机器学习技术从同步的测量数据中得出。具体而言，该项目将：（1）基于在不同振荡方案下的历史测量数据建立一个自我发展的动态知识基础； （2）从历史和实时数据中提取关键特征，并选择最佳特征以提高数据驱动的模型预测准确性； （3）开发机器学习算法，以预测数据驱动的模型用于振荡阻尼控制设计； （4）通过计算机模拟和硬件测试台实验验证和证明所提出的方法。这种先进的方法将有助于美国电力电网的能源安全和效率。该项目将使本科生和研究生均接触到最先进的机器学习教育和劳动力培训计划。通过与现有NSF/DOE工程研究中心的既定外展计划进行协调，研究结果将融入每周的研讨会和短期课程中，这些课程和简短课程可供四所合作伙伴大学，九个会员大学和35多个行业合作伙伴访问。此外，该项目将鼓励学生在学前年代初期开始参与STEM（科学，技术，工程和数学）课程，以准备为STEM职业做准备。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的审查标准通过评估来通过评估来支持的。

项目成果

A Comprehensive Method to Mitigate Forced Oscillations in Large Interconnected Power Grids

缓解大型互连电网受迫振荡的综合方法

• DOI: 10.1109/access.2021.3056123
• 发表时间: 2021
• 期刊: IEEE Access
• 影响因子: 3.9
• 作者: Zhu, Lin;Yu, Wenpeng;Jiang, Zhihao;Zhang, Chengwen;Zhao, Yi;Dong, Jiaojiao;Wang, Weikang;Liu, Yilu;Farantatos, Evangelos;Ramasubramanian, Deepak
• 通讯作者: Ramasubramanian, Deepak

Dynamic Model Reduction for Large-Scale Power Systems Using Wide-Area Measurements

使用广域测量减少大型电力系统的动态模型

• DOI: 10.1109/access.2020.2992624
• 发表时间: 2020
• 期刊: IEEE Access
• 影响因子: 3.9
• 作者: Tong, Ning;Jiang, Zhihao;Zhu, Lin;Liu, Yilu
• 通讯作者: Liu, Yilu

Forced Oscillation Grid Vulnerability Analysis and Mitigation Using Inverter-Based Resources: Texas Grid Case Study

• DOI: 10.3390/en15082819
• 发表时间: 2022-04
• 期刊: Energies
• 影响因子: 3.2
• 作者: Khaled M. Alshuaibi;Yi Zhao;Lin Zhu;E. Farantatos;D. Ramasubramanian;Wenpeng Yu;Yilu Liu

Coordinated Control of Natural and Sub-Synchronous Oscillations via HVDC Links in Great Britain Power System

• DOI: 10.1109/td43745.2022.9816918
• 发表时间: 2022-04
• 期刊: 2022 IEEE/PES Transmission and Distribution Conference and Exposition (T&D)
• 作者: Yi Zhao;Yuqing Dong;Lin Zhu;Kaiqi Sun;Khaled M. Alshuaibi;Chengwen Zhang;Yilu Liu;Brian Graham;E. Farantatos;B. Marshall;Md Rahman;O. Adeuyi;S. Marshall;I. Cowan

Implementation and Hardware-In-the-Loop Testing of A Wide-Area Damping Controller Based on Measurement-Driven Models

基于测量驱动模型的广域阻尼控制器的实现和硬件在环测试

• DOI: 10.1109/pesgm46819.2021.9638055
• 发表时间: 2021
• 期刊: 2021 IEEE Power & Energy Society General Meeting (PESGM
• 作者: Zhang, Chengwen;Zhao, Yi;Zhu, Lin;Liu, Yilu;Farantatos, Evangelos;Patel, Mahendra;Hooshyar, Hossein;Pisani, Cosimo;Zaottini, Roberto;Giannuzzi, Giorgio
• 通讯作者: Giannuzzi, Giorgio