Abstract
Identifying the fault location is usually the first step in system restoration. Accurate fault location is therefore essential in speeding up repair of faulted components and enhancing system reliability. Diverse fault location methods exist that have different strengths and weaknesses. This paper focuses on using artificial neural networks to classify and locate faults on single two-terminal transmission lines. There has not been sufficient existing work that elaborates how to choose the best neural network structures such as the number of hidden layers and the number of neurons in each layer. This paper aims to study the effects of various ANN structures (number of neurons in a single layer) and identify the most effective neural network structure. Study results based on simulated data are reported, which may provide guidance on designing efficient neural networks for classifying fault types and locating faults on single power transmission lines.
Original language | English |
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Title of host publication | Clemson University Power Systems Conference, PSC 2020 |
ISBN (Electronic) | 9781728193847 |
DOIs | |
State | Published - Mar 2020 |
Event | 2020 Clemson University Power Systems Conference, PSC 2020 - Clemson, United States Duration: Mar 10 2020 → Mar 13 2020 |
Publication series
Name | Clemson University Power Systems Conference, PSC 2020 |
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Conference
Conference | 2020 Clemson University Power Systems Conference, PSC 2020 |
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Country/Territory | United States |
City | Clemson |
Period | 3/10/20 → 3/13/20 |
Bibliographical note
Publisher Copyright:© 2020 IEEE.
Keywords
- artificial neural networks
- fault identification
- single transmission lines
ASJC Scopus subject areas
- Artificial Intelligence
- Information Systems and Management
- Energy Engineering and Power Technology
- Renewable Energy, Sustainability and the Environment
- Electrical and Electronic Engineering
- Control and Optimization