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

基于鲁棒无迹卡尔曼滤波器的电力系统监测与控制动态状态和参数估计

基本信息

批准号：
1711191
负责人：
Lamine Mili
金额：
$ 32.56万
依托单位：
Virginia Polytechnic Institute and State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-07-01 至 2020-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1711191&HistoricalAwards=false
关键词：
Dynamic State Parameter Estimation based

项目摘要

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.

电力系统可靠性、安全性和弹性的增强取决于快速、准确和鲁棒的动态状态估计器的可用性。这些估计器应该对测量和模型参数值的粗差具有鲁棒性，同时即使在存在大的动力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。事实证明，文献中给出的当前基于卡尔曼滤波器的动态状态估计器存在几个重要的缺点，阻碍了电力公司在实际应用中采用它们。具体来说，它们无法处理（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或不正确的系统参数值引起的任何类型的异常值； (iv) 所有类型的网络攻击。为了应对这些挑战，该项目将利用鲁棒统计理论和鲁棒控制理论来开发鲁棒动态和参数估计的通用理论框架。这个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，它将为具有同步相量测量的下一代在线状态估计器以及针对网络攻击的鲁棒探测器的重新设计做出贡献。该项目还包含针对对 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。该项目将开创一个通用理论框架，该框架集成了鲁棒统计理论和鲁棒控制理论，用于网络物理系统的鲁棒动态状态和参数估计。具体来说，广义最大似然型（GM）估计器、无迹卡尔曼滤波器和H无穷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计器。这些新的估计器将能够处理大型系统不确定性并抑制三种类型的异常值，同时在广泛的非高斯过程和观测噪声下实现良好的统计效率。这三种类型的异常值，包括观测异常值、创新异常值和结构异常值，都是由不可靠的动态模型或实时同步相量测量带来的数据质量问题引起的，这在电力系统中很常见。此外，稳健统计理论将扩展到结构化非线性回归模型。也就是说，线性结构化回归中的击穿点理论将扩展到以稀疏雅可比矩阵为特征的非线性动力学模型，这正是电力系统的情况。为此，将对所有提出的方法的全局和局部故障点进行研究。最后，所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Dominion Virginia Power 500-KV输电系统，通过一组冗余实时同步相量测量来观察。