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

作为现代社会的重要基础，电网是世界上最复杂的人工动态系统之一。产生大量不同类型、不同成分、不同位置的实时数据，对电网进行监测和控制。目前，大型电网的控制仍主要基于物理系统模型，而上述大容量数据中隐藏的知识尚未得到充分利用。本课题选取智能电网中一个典型的控制功能——低频振荡控制，探索利用隐性知识增强智能电网控制的潜力。低频振荡是大型电力系统运行中的常见现象。如果控制不当，这些振荡可能会降低电力系统的安全性，使大量用户失去电力。该项目旨在开发一种智能控制器，利用数据和机器学习技术来减轻这些低频振荡。如果成功，将推动智能电网技术的发展，并为机器学习技术在智能电网控制中的应用扫除障碍。拟议中的方法将有助于美国电网更加安全、可靠和经济地运行。例如，可以大大降低停电的风险；因此可以节省停电成本，例如，1996年美国西部电网崩溃造成的损失超过10亿美元。拟议的项目还与广泛传播研究成果和强有力的教育组成部分相结合，以吸引来自代表性不足群体的学生。提出了一种基于数据驱动模型的智能振动阻尼控制的全新设计方法。这些数据驱动的电网模型来源于使用机器学习技术的同步测量数据，并结合电网领域知识。具体而言，本项目将：(1)基于不同振荡情景下的历史测量数据，构建自进化的动态知识库；(2)从历史和实时数据中提取关键特征，选择最优特征，提高数据驱动模型预测精度；(3)开发机器学习算法来预测振荡阻尼控制设计的数据驱动模型；(4)通过计算机仿真和硬件实验验证了所提出的方法。这种先进的方法将有助于美国电网的能源安全和效率。该项目将使本科生和研究生都能接触到最先进的机器学习教育和劳动力培训计划。通过与现有的美国国家科学基金会/美国能源部工程研究中心的既定外展计划协调，研究成果将整合到每周研讨会和短期课程中，这些课程将向四所合作大学、九所附属大学和超过35个行业合作伙伴开放。此外，该项目将鼓励学生在大学预科阶段尽早参与STEM（科学、技术、工程和数学）课程，为STEM职业生涯做好准备。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.