Conference proceeding
The Use of Multilayer Perceptron to Classify and Locate Power Transmission Line Faults
ARTIFICIAL INTELLIGENCE AND EVOLUTIONARY COMPUTATIONS IN ENGINEERING SYSTEMS, ICAIECES 2017, v 668, pp 51-58
01 Jan 2018
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
This paper investigates the use of multilayer perceptron (MLP) technique for locating and detecting faults in a power transmission line. MLP was used twice in this paper to locate and to detect faults. The experiments were conducted on a 600-km-length, three-phase power transmission line data which include the required faults to detect and locate the fault. Matlab was used to perform the experiments. Results show that MLP achieved high prediction accuracy for fault type detection of 98% and a prediction accuracy of 78% for fault location.
Metrics
Details
- Title
- The Use of Multilayer Perceptron to Classify and Locate Power Transmission Line Faults
- Creators
- P. S. Pouabe Eboule - University of JohannesburgAli N. Hasan - University of JohannesburgBhekisipho Twala - University of Johannesburg
- Contributors
- S S Dash (Editor)PCB Naidu (Editor)R Bayindir (Editor)S Das (Editor)
- Publication Details
- ARTIFICIAL INTELLIGENCE AND EVOLUTIONARY COMPUTATIONS IN ENGINEERING SYSTEMS, ICAIECES 2017, v 668, pp 51-58
- Series
- Advances in Intelligent Systems and Computing
- Publisher
- Springer Nature
- Number of pages
- 8
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Engineering Leadership and Society/Engineering Technology
- Web of Science ID
- WOS:000550312800005
- Scopus ID
- 2-s2.0-85044431122
- Other Identifier
- 991022004780404721
UN Sustainable Development Goals (SDGs)
This publication has contributed to the advancement of the following goals:
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Information Systems
- Computer Science, Interdisciplinary Applications
- Computer Science, Software Engineering
- Engineering, Electrical & Electronic
- Mathematics, Applied