Assessment of Dominant Oscillatory modes in Power Systems using Advanced Signal Processing and Artificial Intelligence Techniques

Abstract

In recent years, the power system network has undergone dramatic infrastructural and newlineoperational developments that have altered the way the industry operates. Rapid newlineadvancements in the signal processing and communication infrastructure have made way newlinefor tremendous research activities on power system utilities in its wide area monitoring newlinestructure. As a result of the ongoing changes in the power industry, electromechanical newlineoscillations have become more prominent. It is believed that electromechanical newlineoscillations were first caused by automatic voltage controllers designed to overcome newlinecomplications with grid voltage stability. With the installation of large-scale renewable newlineresources connected to the grid through power electronic converters, the mechanical newlineinertia of the system got reduced and became more susceptible to stability issues which newlineled to low frequency oscillations. Use of synchrophasor measurements to monitor newlineelectromechanical oscillations in real-time is highly valuable for power system operators. newlineThus, several algorithms based on synchrophasor measurements have been developed to newlineachieve this purpose. Oscillatory mode estimation is widely accepted as one of the newlineessential applications of wide area measurement systems. The current research trends newlineinvestigate various approaches for improving the mode estimation process by offering newlinenew methods and understanding different stages in the mode estimation process. Most of newlinethe power system transients are non-linear and cannot be analyzed using traditional linear newlineanalysis techniques. Multi-scale processes govern these transient phenomena and are newlinefundamentally nonstationary due to non-linear dynamics and time-dependent control newlineactions. Recently, non-linear nonstationary algorithms have been applied to analyze newlinecomplex oscillatory properties in the power system. This thesis principally focuses on the newlineidentification of dominant oscillatory modes in power systems using nonlinear newlinenonstationary methods.