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.

电力系统的可靠性，安全性和弹性的提高取决于快速，准确和稳健的动态状态估计器的可用性。这些估计器应该对测量值和模型参数值进行总体错误，同时即使在存在大型动态系统模型不确定性以及非高斯厚尾过程和观察声的情况下，也提供了良好的状态估计值。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遭受了几个重要的缺点，从而使它们无法被Power公用事业采用用于实际应用。具体而言，他们无法处理（i）动态模型不确定性和参数错误；（ii）系统非线性动态模型的非高斯过程和观察噪声；（iii）由冲动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，以引用一些；（iv）所有类型的网络攻击。为了应对这些挑战，该项目将诉诸强大的统计理论和强大的控制理论，以开发出强大的动态状态和参数估计的一般理论框架。这个新的一般框架将为电力系统监视，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，它将通过同步测量和对网络攻击的强大探测器的重新设计有助于下一代在线状态估计器。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。该项目将开拓一个一般理论框架，该框架将强大的统计理论和强大的控制理论集成了对稳健的动态状态和稳健的控制理论的稳健动力学和参数估计的估算。具体而言，广义的最大可能性型型（GM）估计器，无气味的卡尔曼滤波器和H-含量过滤器将集成到统一的框架中，以产生各种集中式和分散的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察噪声下实现良好的统计效率。包括观察，创新和结构异常值在内的三种类型的异常值是由不可靠的动态模型或具有数据质量问题的实时同步测量值引起的，这些质量问题在电源系统中通常可以看到。此外，鲁棒统计的理论将扩展到结构化的非线性回归模型。也就是说，线性结构化回归中的崩溃点理论将扩展到以稀疏的雅各布矩阵为特征的非线性动力学模型，这正是功率系统的情况。为此，将研究所有拟议方法的全球和本地分解点。最后，将在两个实用的电力系统上实施和测试开发的方法，包括南部巴西电力系统和Dominion Virginia Power 500-KV传输系统，这是通过一组冗余实时同步量测量值观察到的。