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

为了做出明智的决策，电力系统操作员需要一个稳健、准确的电网运行状态实时估计器。目前，常规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，这些估计器不再足以监测现代电网，由于间歇性可再生能源(特别是太阳能和风能)发电的高渗透率，电网正在经历越来越大的不确定性和变化。事实上，当电力系统快速变化时，传统的电网静态状态估计方法往往不能在输电线路跳闸和电网连锁故障时提供任何有用的信息，因此迫切需要状态估计结果。在对复杂系统进行建模方面存在技术差距，这一点还没有被完全理解，而且其行为可能会迅速变化。为了弥补这一差距，项目组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营状况，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态势感知方面的根本问题，因此研究结果将揭示其他复杂基础设施中的这一研究挑战，这些基础设施是时变的，具有高度不确定性。该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初中/高中学生。因此，该项目将增加多样性，并改善未来电力系统工程师的准备，从而使电网能够现代化，以容纳更多的可再生能源发电。该项目的目标是开发一个数据融合框架，用于集成动态状态估计器(IDSE)，通过结合信号处理理论和统计推理理论来估计和预测电力系统状态。受多假设过滤算法能够跟踪状态快速变化的初步结果的鼓舞，以及信任函数理论比贝叶斯概率理论能更有效地处理不完全和冲突信息的不确定性，新的IDSE将通过将信任函数理论和多假设检验与多个模型相结合来同化异质数据，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IDSE将通过增加空间和时间冗余来获得额外的稳健性；(2)利用信念函数理论对不完全和冲突信息进行显式建模的能力，新的IDSE将有效地量化和缓解电力系统固有的不确定性和认知不确定性的负面影响；(3)利用多种不同的估计准则和模型，新的IDSE将有效地处理电力系统的快速动态变化，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意识，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。

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.