Fault Identification on Electrical Transmission Lines Using Artificial Neural Networks

Abstract

Transmission lines are built to span over long distances, to which they are frequently exposed to many different situations that can cause abnormal conditions known as electrical faults. Electrical faults, when isolated, can cripple the transmission system as power flows are directed around these faults therefore leading to other numerous potential issues such as thermal and voltage violations, customer interruptions, or cascading events. Accurate fault classification and location is essential in reducing outage times and enhancing system reliability. Diverse methods exist and have different strengths and weaknesses. This paper aims to investigate the use of an intelligent technique based on artificial neural networks. The neural networks will attempt to determine the fault classification and precise fault location. Different fault cases are analyzed on multiple transmission line configurations using various phasor measurement arrangements from the two substations connecting the transmission line. The results can provide guidance on choosing the most efficient neural network structure and input measurements for transmission line fault classification and location.

Keywords:

• artificial neural networks
• fault location
• fault type classification
• feed forward neural networks
• parallel transmission line
• phasor measurement
• single transmission line

Additional information

Notes on contributors

Chris Asbery

Chris Asbery is a Senior Electrical Engineer at Louisville Gas & Electric and Kentucky Utilities (LG&E/KU). He received his B.S., M.S., and Ph.D. degree in electrical and computer engineering from the University of Kentucky in 2008, 2012, and 2020 respectively and obtained his professional engineering (PE) certification within the State of Kentucky in 2015. He has twelve years of working experience which encompasses: four years at Inter County Energy Cooperative and eight years at LG&E/KU. His interests include power system transmission operations, analysis and planning, and renewable energy integration.

Yuan Liao

Yuan Liao is Professor in Electrical and Computer Engineering at the University of Kentucky. He received his Ph.D. degree in electrical engineering from Texas A&M University in 2000. He had five years of working experiences at ABB inc. before joining the University of Kentucky. His research interests include power system protection, analysis and planning, smart grid, and renewable energy integration.