Proceedings of the International Conference on Intelligent Systems for a Sustainable Future (ISSF 2026)

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Prediction of Transmission Line Fault Using Wavelet Network

Authors
Nandhini1, *, Karthika2, Gnanavel3, Selva Ganesh4
1UG Student, KIT-Kalaignarkarunanidhi institute of technology, Coimbatore, Tamil Nadu, India
2Assistant professor, KIT-Kalaignarkarunanidhi institute of technology, Coimbatore, Tamil Nadu, India
3UG Student, KIT-Kalaignarkarunanidhi institute of technology, Coimbatore, Tamil Nadu, India
4UG Student, KIT-Kalaignarkarunanidhi institute of technology, Coimbatore, Tamil Nadu, India
*Corresponding author. Email: kit26.eee32@gmail.com
Corresponding Author
Nandhini
Available Online 16 June 2026.
DOI
10.2991/978-94-6239-693-7_36How to use a DOI?
Keywords
Transmission line fault detection; Wavelet transform; Wavelet neural network; Optimization; Fault classification; Power system protection
Abstract

Reliable and efficient fault detection in transmission lines is vital for ensuring the stability and safety of modern power systems. Conventional fault detection techniques often face challenges in accurately identifying fault types and locations under dynamic operating conditions and noise interference. To address these limitations, this research focuses on the optimization of transmission line fault detection using a wavelet network. The proposed approach integrates the time frequency localization capability of the wavelet transform with the adaptive learning strength of neural networks, enabling precise feature extraction and intelligent fault classification. By optimizing network parameters such as the mother wavelet selection, decomposition levels, and learning rate, the system achieves faster convergence and improved accuracy. Simulation results demonstrate that the optimized wavelet network outperforms traditional detection methods in terms of fault localization accuracy, computational efficiency, and noise resilience. This optimized model provides an effective and intelligent framework for enhancing power system protection and minimizing fault recovery time.

Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Intelligent Systems for a Sustainable Future (ISSF 2026)
Series
Atlantis Highlights in Intelligent Systems
Publication Date
16 June 2026
ISBN
978-94-6239-693-7
ISSN
2589-4919
DOI
10.2991/978-94-6239-693-7_36How to use a DOI?
Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Nandhini
AU  - Karthika
AU  - Gnanavel
AU  - Selva Ganesh
PY  - 2026
DA  - 2026/06/16
TI  - Prediction of Transmission Line Fault Using Wavelet Network
BT  - Proceedings of the International Conference on Intelligent Systems for a Sustainable Future (ISSF 2026)
PB  - Atlantis Press
SP  - 358
EP  - 364
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6239-693-7_36
DO  - 10.2991/978-94-6239-693-7_36
ID  - 2026
ER  -