RESEARCH PAPER
Fault Detection in Power Transmission Lines: Comparison of Chirp-Z Algorithm and Machine Learning Based Prediction Models
1
Electricity and Energy, Yozgat Bozok University, Turkey
Submission date: 2025-02-19
Final revision date: 2025-03-10
Acceptance date: 2025-04-10
Online publication date: 2025-04-17
Publication date: 2025-04-17
Corresponding author
Feyyaz Alpsalaz
Electricity and Energy, Yozgat Bozok University, Gültepe Mahallesi barbaros caddesi Akdağmadeni MYO, 66100, Yozgat, Turkey
Electricity and Energy, Yozgat Bozok University, Gültepe Mahallesi barbaros caddesi Akdağmadeni MYO, 66100, Yozgat, Turkey
Eksploatacja i Niezawodność – Maintenance and Reliability 2025;27(4):203949
HIGHLIGHTS
- The Chirp-Z algorithm provided high-resolution spectral analysis of fault signals
- The integration of ML and signal processing ensured fast, reliable fault detection
- Transient events were seamlessly integrated with modal transformation and Chirp-Z
- Fault detection integrated with machine learning significantly reduced error rates
- The GBE model stood out with the lowest error rate and the highest accuracy.
KEYWORDS
TOPICS
ABSTRACT
Fast and accurate detection of faults in power transmission lines is of great importance for the safety and continuity of power systems. This study develops a predictive model using chirp-z transform and machine learning algorithms to locate single-phase-ground faults. During the study, 39 different fault locations were modelled, current and voltage signals of these locations were analysed and frequency spectra were obtained. The fault signals were decomposed into their components using the modal transformation matrix and then spectral analysis was performed using the Chirp-Z algorithm. The resulting spectra were used as input data for the prediction algorithms. Gradient Boosting Ensemble, Support Vector Regression and Random Forests algorithms were used for fault prediction and the performance of the models was compared. The accuracy of the models was evaluated using various metrics. The results show that the Gradient Boosting Ensemble model has the lowest error rates and the highest accuracy, which is important for early fault detection, maintenance and repair processes.
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