Year

2014

Degree Name

Doctor of Philosophy

Department

School of Electrical, Computer and Telecommunications Engineering

Abstract

This thesis presents innovative approaches for detection, classification and characterization of abnormal events in electricity networks. Due to disturbances and/or faults in electricity networks, the abnormal events are created; one such abnormal event is the formation of power system island containing distributed generating resources and the other is the voltage dips and/or swells. This thesis proposes a Support Vector Machine (SVM) based approach for detection and classification of islanding events in a distribution network embedded with Distributed Generation (DG). Furthermore, two innovative approaches, which include three-phase voltage ellipse method and 3D polarization ellipse technique, are proposed to detect, classify and characterize voltage dips and/or swells in electricity networks.

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