Data-Driven Dynamic State-Estimation for Modern Power Systems

该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。该项目将给最先进的动态状态估计器带来革命性的变化，这些动态状态估计器严重依赖基于物理的准确模型，并受到模型不确定性和干扰的挑战。将使用机器学习和信号处理技术的组合来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在现代电力系统的监测和态势感知方面产生新的知识，以及开发一种新的数据驱动范例，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解决连锁故障。一些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暑期研究所(STEM-SI)为代表性不足的学生举办的暑期研究活动、定制的高级项目以及科学界可用的开源材料。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非常重要，但在建模分布式能源(DER)方面存在技术差距，目前尚不完全了解。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。利用所开发的模型，将不需要复杂的基于物理的模型或仿真来设计DER状态的不确定性感知分散数据驱动的动态状态估计。除了为各种类型的DER(如储能、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还将降低目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，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.