Fault Classification in Transmission Line Using Soft Computing Tool

The investigation of fault classification in transmission lines is an essential aspect of this subject, as it guarantees the dependability and steadiness of power systems. The use of artificial neural networks (ANNs), in particular, as soft computing tools for efficient fault classification in trans...

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Bibliographic Details
Published in:	2024 International Conference on Innovations and Challenges in Emerging Technologies (ICICET) pp. 1 - 6
Main Authors:	Tembhurne, Pranita Suresh, Daigavane, Prema, Umathe, Shradha, Pathe, Kartik
Format:	Conference Proceeding
Language:	English
Published:	IEEE 07-06-2024
Subjects:	Artificial neural networks Computational modeling Data models Data preprocessing Fault classification Feature extraction IEEE 5 Bus system MATLAB simulation Power systems Power transmission lines Soft computing Stability Technological innovation Training Transmission lines
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Summary:	The investigation of fault classification in transmission lines is an essential aspect of this subject, as it guarantees the dependability and steadiness of power systems. The use of artificial neural networks (ANNs), in particular, as soft computing tools for efficient fault classification in transmission lines is investigated in this work. The research focuses on assessing the effectiveness of ANN-based fault classification algorithms using the IEEE 5 Bus system as a benchmark. The study is to show the efficacy and dependability of ANNs in precisely recognising various fault types in transmission lines through extensive experimentation and analysis. Preprocessing the data, extracting features, training ANN models, and evaluating performance with a MATLAB simulation environment are all part of the suggested methodology. The obtained results demonstrate the potential of soft computing technologies in improving the classification of transmission line faults.
DOI:	10.1109/ICICET59348.2024.10616290