Conference proceeding
The Use of Machine Learning Techniques to Classify Power Transmission Line Fault Types and Locations
2017 INTERNATIONAL CONFERENCE ON OPTIMIZATION OF ELECTRICAL AND ELECTRONIC EQUIPMENT (OPTIM) & 2017 INTL AEGEAN CONFERENCE ON ELECTRICAL MACHINES AND POWER ELECTRONICS (ACEMP), pp.221-226
01 Jan 2017
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Transmission lines are very important component of the electric power system. Therefore it is necessary to predict and detect transmission lines fault types and locations to enhance the power system protection scheme and increase its reliability. This paper investigates the use of four powerful machine learning classifiers to detect and predict fault types and locations over a 750KV, 600km long power transmission line. Bagging, Boosting, radial basis functions and naive Bayesian classifiers were utilized for locating and detecting faults in a power transmission line. Findings exhibits that using machine learning technique could be feasible for such task and may represent a great opportunity to increase the power system protection and efficiency.
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Details
- Title
- The Use of Machine Learning Techniques to Classify Power Transmission Line Fault Types and Locations
- Creators
- Ali N. Hasan - University of JohannesburgP. S. Pouabe Eboule - University of JohannesburgBhekisipho Twala - University of Johannesburg
- Publication Details
- 2017 INTERNATIONAL CONFERENCE ON OPTIMIZATION OF ELECTRICAL AND ELECTRONIC EQUIPMENT (OPTIM) & 2017 INTL AEGEAN CONFERENCE ON ELECTRICAL MACHINES AND POWER ELECTRONICS (ACEMP), pp.221-226
- Publisher
- IEEE
- Number of pages
- 6
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Engineering Leadership and Society/Engineering Technology
- Identifiers
- 991022004765604721
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InCites Highlights
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- Web of Science research areas
- Engineering, Electrical & Electronic