Year
2014
Degree Name
Doctor of Philosophy
Department
School of Electrical, Computer and Telecommunications Engineering
Recommended Citation
Alam, Mollah R., Islanding detection of distributed generation and classification of voltage sags/swells using machine learning techniques, Doctor of Philosophy thesis, School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, 2014. https://ro.uow.edu.au/theses/4374
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.
FoR codes (2008)
0906 ELECTRICAL AND ELECTRONIC ENGINEERING
Unless otherwise indicated, the views expressed in this thesis are those of the author and do not necessarily represent the views of the University of Wollongong.