Date of Award

December 2020

Document Type


Degree Name

Master of Science (MS)


Electrical Engineering

Committee Member

Sukumar SB Brahma

Committee Member

E.Randolph RC Collins

Committee Member

Ramtin RH Hadidi


Traditional distribution systems, which are single-sourced and radial, are mostly protectedby fuses, reclosers, and overcurrent relays. Due to the penetration of distributed energy resources, the topology changes to multi-sourced. Fuses and reclosers fail to coordinate for bidirectional fault currents flowing in such a system, jeopardizing the selectivity of protection. When these resources are Inverter Based Resources (IBRs), even detection and classification of faults becomes an issue due to lack of negative and zero sequence currents and severely restricted positive sequence fault currents contributed by IBRs. This issue is most prominent in an islanded distribution system fed completely by IBRs. Recognizing that even in such an island where sequence currents are not generated by IBRs, sequence voltages will always be created by the physics of the fault and hence will be available at the IBR terminals, this thesis proposes to use these sequence voltages for detection and classification of faults locally at the IBR terminal. It also explores the possibility of using machine learning to approximately locate the faulted section based on the signatures provided by the local sequence voltages at the inverter terminal. IEEE 13-bus distribution feeder is modeled as an island in the time domain, fed by one grid forming and three grid following inverters to analyze the properties of such an unbalanced island in normal and faulted conditions. Based on the simulation results, insights are developed, and methodologies are formed and tested.



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