Abstract
Electric transmission systems are complex mesh networks that direct large amounts of energy from the point of generation to the point of consumption. Electric faults can cripple a system as power flows must be directed around the fault therefore leading to numerous potential issues such as overloading, customer service interruptions, or cascading failures. Therefore, identifying the classification and location of these faults as quickly and efficiently as possible is crucial. This work aims to utilize artificial neural networks to determine fault type and location based on measured voltages and currents. Eventually, once developed, this solution could be utilized for fault detection and classification on several transmission circuit topologies as well as with different fault types and resistances.
Original language | English |
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Title of host publication | 2019 International Energy and Sustainability Conference, IESC 2019 |
ISBN (Electronic) | 9781728132914 |
DOIs | |
State | Published - Oct 2019 |
Event | 2019 International Energy and Sustainability Conference, IESC 2019 - Farmingdale, United States Duration: Oct 17 2019 → Oct 18 2019 |
Publication series
Name | 2019 International Energy and Sustainability Conference, IESC 2019 |
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Conference
Conference | 2019 International Energy and Sustainability Conference, IESC 2019 |
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Country/Territory | United States |
City | Farmingdale |
Period | 10/17/19 → 10/18/19 |
Bibliographical note
Publisher Copyright:© 2019 IEEE.
Keywords
- artificial neural networks
- fault classification
- fault location
- transmission system
ASJC Scopus subject areas
- Computer Networks and Communications
- Energy Engineering and Power Technology
- Renewable Energy, Sustainability and the Environment