Stability Prediction Techniques for Electric Power Systems based on Identification Models and Gramians

作     者：Bakhtadze, N. Yadykin, I. 

作者机构：V.A. Trapeznikov Institute of Control Sciences of the Russian Academy of Science Moscow Russia 65 Profsoyuznaya Moscow117997 Russia 

出 版 物：《IFAC-PapersOnLine》 

年 卷 期：2019年第52卷第13期

页      面：481-485页

核心收录：

主　　题：Predictive analytics Decision making Electric power system stability Knowledge based systems Lyapunov functions Matrix algebra Number theory Wavelet analysis Associative Search Decision making support Gramians Identification model Intelligent Algorithms Knowledge base Non stationary objects Process identification 

摘      要：The methods for developing predictive models in control systems and decision-making support for nonlinear non-stationary objects are proposed. The methods are based on the application of associative search procedure to virtual model identification as well as Gramian techniques. The associative search methods use intelligent process knowledge analysis. The knowledge base is created and extended in real-time process operation. Intelligent algorithms are offered for predicting power plant dynamics in optimization tasks. Gramian technique of stability analysis for discrete system is used for investigating linear virtual model stability. It is shown that the bilinear Lyapunov equation solutions can be calculated as an infinite sum of the matrix quadratic forms made up by the products of the Faddeev matrices obtained by decomposing of linear subsystem dynamic matrix resolvents. © 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.