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Data-Driven Dynamic State-Estimation for Modern Power Systems

现代电力系统的数据驱动动态状态估计

基本信息

批准号：
2221784
负责人：
Javad Khazaei
金额：
$ 43.15万
依托单位：
Lehigh University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-01 至 2025-08-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2221784&HistoricalAwards=false
关键词：
Data Driven Dynamic State Estimation

项目摘要

This NSF project aims to develop data-driven algorithms for dynamic state-estimation of modern power systems with uncertainties of distributed energy resources. The project will bring transformative change to the state-of-the-art dynamic state-estimators that heavily rely on accurate physics-based models and are challenged by model uncertainties and disturbances. A combination of machine learning and signal processing techniques will be used to convert available measurements into accurate dynamic models. The intellectual merits of the project include generating new knowledge on monitoring and situational awareness in modern power systems as well as developing a novel data-driven paradigm to improve smart grid resilience through dynamic state estimation. The broader impacts of the project include addressing grid outages and resolving cascading failures by better tracking power system asset dynamics in real-time. A number of educational and outreach activities are also used to address underrepresentation drivers in electrical engineering, such as summer research activities for underrepresented students through STEM Summer Institute (STEM-SI), custom-designed senior projects, and open-source materials available to the scientific community.It is important to develop efficient dynamic estimation techniques to better monitor and control inverter-dominated grids, but there is a technical gap in modeling distributed energy resources (DERs) that is not fully understood. To address this challenge, this project will develop advanced data-driven approaches leveraging statistical machine learning theory and available measurements to identify nonlinear mathematical models of inverter-based DERs. Using the developed models, uncertainty-aware decentralized data-driven dynamic state estimation of DER states will be designed without the need for complex physics-based models or simulations. In addition to providing accurate and state-aware dynamic models for various types of DERs (such as storage, solar panels and wind generators), the research results from the proposed framework will reduce the current complexity of implementing dynamic state estimation.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.

这个NSF项目旨在开发数据驱动的算法，用于具有分布式能源不确定性的现代电力系统的动态状态估计。该项目将为最先进的动态状态估计器带来革命性的变化，这些估计器严重依赖精确的基于物理的模型，并受到模型不确定性和干扰的挑战。机器学习和信号处理技术的结合将用于将可用的测量转换为准确的动态模型。该项目的智力优势包括产生关于现代电力系统监测和态势感知的新知识，以及开发一种新的数据驱动范式，通过动态状态估计来提高智能电网的弹性。该项目的更广泛影响包括通过更好地实时跟踪电力系统资产动态来解决电网中断和连锁故障。许多教育和推广活动也被用来解决电气工程中代表性不足的驱动因素，例如通过STEM夏季研究所（STEM-SI）为代表性不足的学生开展夏季研究活动，定制设计的高级项目，以及科学界可用的开源材料。重要的是要开发有效的动态估计技术，以更好地监测和控制逆变器主导的电网，但是在对分布式能源（DER）进行建模方面存在尚未完全理解的技术差距。为了应对这一挑战，该项目将开发先进的数据驱动方法，利用统计机器学习理论和可用的测量来识别基于逆变器的DER的非线性数学模型。使用开发的模型，不确定性感知分散的数据驱动的DER状态的动态状态估计将被设计，而不需要复杂的基于物理的模型或仿真。除了为各种类型的DER（如存储、太阳能电池板和风力发电机）提供准确和状态感知的动态模型外，拟议框架的研究成果将降低当前实施动态状态估计的复杂性。该奖项反映了NSF的法定使命，并被认为值得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来支持。