Nonlinear Approaches for Dynamic Equivalencing in Power Systems

Abstract

This paper addresses innovative nonlinear approaches for dynamic equivalencing of machines for interconnected power systems. In contrast to the existing approaches that consider only fixed equivalencing steps without taking into consideration the machine parameters, these approaches reformulate the classical conditions incorporating real electromechanical model parameters and behaviours of the machines in the dynamic equivalencing. These aspects enable the integration of modern and intelligent techniques, such as the pattern recognition algorithms, Fuzzy concept as machine splitting factor and the system identification by dynamical artificial neural networks. The electromechanical-based approaches generate accurate robust, non-linear dynamic equivalents and thereby enhance significantly their consistency and practical application on network reliability, management and planning.Test of these approaches have been performed and evaluated in large-scale model of the European Interconnected Electric Power System (UCTE/CENTREL) and 16 multi machine system.

Non‐synchronous generation impact on power systems coherency

“…Some approaches applying machine learning and computational intelligence techniques for coherent identification have also been investigated in [20, 21].…”