Dynamic State and Parameter Estimation based on Robust Unscented Kalman Filters for Power System Monitoring and Control

电力系统可靠性、安全性和恢复力的提高依赖于快速、准确和稳健的动态状态估计器的可用性。这些估值器应该对测量和模型参数值的粗差具有鲁棒性，同时即使在存在较大的动态系统模型不确定性和非高斯厚尾过程和观测噪声的情况下也能提供良好的状态估计。结果表明，目前文献中给出的基于卡尔曼滤波的动态状态估计器存在着几个重要的缺陷，阻碍了它们在实际应用中的应用。具体地说，它们不能处理(I)动态模型不确定性和参数误差；(Ii)系统非线性动态模型的非高斯过程和观测噪声；(Iii)由脉冲测量和系统过程噪声引起的任何类型的离群值，或错误的系统参数值，仅举几例；以及(Iv)所有类型的网络攻击。为了应对这些挑战，本项目将求助于稳健统计理论和稳健控制理论来开发稳健动态状态和参数估计的一般理论框架。这种新的通用框架将为电力系统监测、控制、保护和安全分析提供可靠的实时状态和参数估计。此外，它还将有助于使用同步相量测量的下一代在线状态估计器，以及重新设计针对网络攻击的稳健检测器。该项目还包含了一个面向对STEM(科学、技术、工程和数学)领域感兴趣的K-12学生、本科生和研究生的综合教育议程。该项目将开创一个综合了稳健统计理论和稳健控制理论的一般理论框架，用于网络物理系统的稳健动态和参数估计。具体地说，广义最大似然(GM)型估值器、无迹卡尔曼滤波器和H无穷大滤波器将被集成到一个统一的框架中，以产生各种集中式和分散式鲁棒动态状态估值器。这些新的估值器将能够处理大的系统不确定性以及抑制三种类型的异常值，同时在大范围的非高斯过程和观测噪声下获得良好的统计效率。这三种类型的异常值，包括观察值、新颖值和结构异常值，要么是由不可靠的动态模型引起的，要么是由存在数据质量问题的实时同步相量测量引起的，这在电力系统中很常见。此外，稳健统计的理论将扩展到结构化的非线性回归模型。也就是说，线性结构回归中的故障点理论将推广到以稀疏雅可比矩阵为特征的非线性动态模型，这正是电力系统的情况。为此，将调查所有拟议方法的全局和局部故障点。最后，开发的方法将在两个实际电力系统上实施和测试，包括巴西南部电力系统和道明弗吉尼亚电力500千伏输电系统，这是通过一组冗余的实时同步相量测量进行观察的。

The enhancement of the reliability, security, and resiliency of electric power systems depends on the availability of fast, accurate, and robust dynamic state estimators. These estimators should be robust to gross errors on the measurements and the model parameter values while providing good state estimates even in the presence of large dynamical system model uncertainties and non-Gaussian thick-tailed process and observation noises. It turns out that the current Kalman filter-based dynamic state estimators given in the literature suffer from several important shortcomings, precluding them from being adopted by power utilities for practical applications. To be specific, they cannot handle (i) dynamic model uncertainty and parameter errors; (ii) non-Gaussian process and observation noise of the system nonlinear dynamic models; (iii) any type of outliers that are induced by impulsive measurement and system process noises, or incorrect system parameter values, to cite a few; and (iv) all types of cyber attacks. To address these challenges, this project will resort to both robust statistical theory and robust control theory to develop a general theoretical framework for robust dynamic state and parameter estimation. This new general framework will provide reliable real-time state and parameter estimates for power system monitoring, control, protection, and security analysis. In addition, it will contribute to the next generation of online state estimators with synchrophasor measurements and the redesign of robust detectors against cyber attacks. The project also contains an integrated educational agenda for K-12 students, undergraduates and graduate students who are interested in the STEM (Science Technology Engineering and Mathematics) area.This project will pioneer a general theoretical framework that integrates both robust statistical theory and robust control theory for robust dynamic state and parameter estimation of a cyber-physical system. Specifically, the generalized maximum-likelihood-type (GM)-estimator, the unscented Kalman filter, and the H-infinity filter will be integrated into a unified framework to yield various centralized and decentralized robust dynamic state estimators. These new estimators will be able to handle large system uncertainties as well as suppress three types of outliers while achieving good statistical efficiency under a broad range of non-Gaussian process and observation noise. The three types of outliers, including observation, innovation, and structural outliers are caused by either an unreliable dynamical model or real-time synchrophasor measurements with data quality issues, which are commonly seen in the power system. Furthermore, the theories of robust statistics will be extended to structured nonlinear regression models. That is, the theory of breakdown point in linear structured regression will be extended to nonlinear dynamical models characterized by sparse Jacobian matrices, which is precisely the case for power systems. To this end, the global and local breakdown points of all the proposed methods will be investigated. Finally, the developed methods will be implemented and tested on two practical power systems, including the Southern Brazil power system and the Dominion Virginia Power 500-KV transmission system, which is observed through a set of redundant real-time synchrophasor measurements.