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CAREER: Integrated Dynamic State Estimation for Monitoring Power Systems under High Uncertainty and Variation

职业：在高不确定性和变化下监测电力系统的综合动态估计

基本信息

批准号：
1845523
负责人：
Ning Zhou
金额：
$ 50万
依托单位：
SUNY at Binghamton
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-03-01 至 2025-02-28
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1845523&HistoricalAwards=false
关键词：
CAREER Integrated Dynamic State Estimation

项目摘要

To make well-informed decisions, power system operators need a robust accurate real-time estimator of the state of the operational conditions of the power grid. Up to the present time, conventional static state estimators have been widely deployed in utility control centers to improve the estimation accuracy and expand the monitoring areas. However, these estimators are no longer sufficient for monitoring the modern power grid, which is experiencing increasing uncertainty and variation driven by the high penetration of intermittent renewable (especially solar and wind) generation. In fact, conventional static state estimation methods for power grids often fail in providing any useful information during transmission-line tripping and cascading grid failures when the power system rapidly changes, and state estimation results are crucially needed. There is a technical gap in modeling a complex system, which is not fully understood, and whose behaviors can change rapidly. To bridge the gap, the project team will develop a data-fusion framework for an integrated dynamic state estimator (iDSE) that can not only estimate current operational conditions but also predict their future trends, and quantify their uncertainty. Because the framework addresses the fundamental issue in the situational awareness of a complex system, the research results will shed light on that research challenge in other complex infrastructures, which are time-varying, and with high uncertainty. The project team will disseminate the new theory and methods to industry and academia, train college students, and historically underrepresented middle/high-school students. Thus the project will increase diversity and improve the preparation of future power system engineers so that the power grid can be modernized to host more renewable generation.The goal of the project is to develop a data-fusion framework for an integrated dynamic state estimator (iDSE) to estimate and predict power system states by integrating signal processing theory and statistical inference theory. Encouraged by preliminary results that multiple-hypothesis filtering algorithms can track rapid variation in states, and the observation that belief function theory can more efficiently handle the uncertainty from incomplete and conflicting information than Bayesian probability theory, the new iDSEs will be created by integrating belief function theory and multiple-hypothesis testing with multiple models to assimilate heterogeneous data and gain the following three capabilities: (1) Leveraging dynamical models together with static power flow models, the new iDSEs will achieve additional robustness through increased spatial and temporal redundancy; (2) Leveraging the capability of belief function theory to explicitly model incomplete and conflicting information, the new iDSEs will efficiently quantify and mitigate the negative impacts of both aleatory and epistemic uncertainty inherent in the power system; (3) Leveraging multiple dissimilar estimation criteria and models, the new iDSEs will effectively deal with quick dynamical changes in the power system and predict future states using multiple-hypothesis testing. It is expected that the new iDSE will significantly increase the situational awareness of an operator and lay the groundwork for transforming state estimation and power system operations from the current static paradigm into a future dynamic paradigm.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.

为了做出明智的决策，电力系统运营商需要一个对电网运行状态的鲁棒准确的实时估计器。目前，传统的静态估计器已广泛应用于电力控制中心，以提高估计精度，扩大监测范围。然而，这些估算器已不足以监测现代电网，由于间歇性可再生能源（特别是太阳能和风能）发电的高度渗透，现代电网正经历越来越多的不确定性和变化。事实上，当电力系统发生快速变化时，传统的电网静态估计方法往往不能提供任何有用的信息，而状态估计结果是至关重要的。在对复杂系统建模方面存在技术上的差距，这些系统的行为可能会迅速变化，而且尚未被完全理解。为了弥补这一差距，项目团队将为集成动态状态估计器（iDSE）开发一个数据融合框架，该框架不仅可以估计当前的操作条件，还可以预测其未来趋势，并量化其不确定性。由于该框架解决了复杂系统态势感知的基本问题，因此研究结果将为其他时变且具有高度不确定性的复杂基础设施的研究挑战提供启示。项目团队将向工业界和学术界传播新的理论和方法，培训大学生和历史上代表性不足的中学生。因此，该项目将增加多样性，改善未来电力系统工程师的准备工作，使电网能够现代化，以容纳更多的可再生能源发电。该项目的目标是开发一个集成动态状态估计器（iDSE）的数据融合框架，通过集成信号处理理论和统计推断理论来估计和预测电力系统的状态。基于多假设滤波算法可以跟踪状态快速变化的初步结果，以及信念函数理论比贝叶斯概率论更有效地处理不完全信息和冲突信息的不确定性，将信念函数理论与多模型的多假设检验相结合，形成新的集成决策系统，以吸收异构数据，并获得以下三个能力：(1)利用动态模型和静态潮流模型，新的ids将通过增加空间和时间冗余来实现额外的鲁棒性；(2)利用信念函数理论对不完全信息和冲突信息进行明确建模的能力，新的决策决策模型将有效地量化和减轻电力系统固有的不确定性和认知不确定性的负面影响；(3)利用多个不同的估计准则和模型，新的idse能够有效地处理电力系统的快速动态变化，并利用多假设检验预测未来状态。预计新的iDSE将显著提高运营商的态势感知能力，并为将状态估计和电力系统运行从当前的静态范式转变为未来的动态范式奠定基础。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。