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

为了做出明智的决策，电力系统运营商需要一个强大的准确的实时估计的状态的电网的操作条件。到目前为止，传统的静态估计器已被广泛部署在公用事业控制中心，以提高估计精度和扩大监测区域。然而，这些估计量不再足以监测现代电网，现代电网正在经历由间歇性可再生能源（特别是太阳能和风能）发电的高渗透率所驱动的日益增加的不确定性和变化。事实上，传统的静态状态估计方法往往无法提供任何有用的信息，在输电线路跳闸和连锁电网故障时，电力系统的快速变化，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差距，这是不完全理解的，并且其行为可以快速变化。为了弥合这一差距，项目团队将为综合动态状态估计器（iDSE）开发一个数据融合框架，该框架不仅可以估计当前的运行状况，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的态势感知中的基本问题，因此研究结果将揭示其他复杂基础设施中的研究挑战，这些基础设施是时变的，并且具有高度的不确定性。该项目小组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初中/高中学生。因此，该项目将增加多样性，提高未来的电力系统工程师的准备，使电网可以现代化，以容纳更多的可再生能源发电。该项目的目标是开发一个数据融合框架的综合动态状态估计器（iDSE）估计和预测电力系统状态，通过集成信号处理理论和统计推断理论。受多假设过滤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可以比贝叶斯概率理论更有效地处理不完整和冲突信息的不确定性的观察结果的鼓舞，新的iDSE将通过整合信念函数理论和多假设测试与多个模型来创建，以吸收异构数据并获得以下三种能力：（1）利用动态模型和静态潮流模型，新的iDSE将通过增加空间和时间冗余来实现额外的鲁棒性;（2）利用置信函数理论的能力来明确地建模不完整和冲突的信息，新的iDSE将有效地量化和减轻电力系统中固有的偶然和认知不确定性的负面影响;（3）利用多种不同的估计准则和模型，新的iDSE将有效地处理电力系统中的快速动态变化，并使用多假设检验来预测未来状态。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营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。

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.