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A comprehensive guide to selecting suitable wavelet decomposition level and functions in discrete wavelet transform
Esraa M Shalby1,2, Almoataz Y Abdelaziz3,4, Eman S Ahmed5
1Faculty of Engineering, Ain Shams University, Cairo, 11517, Egypt. 2200859@eng.asu.edu.eg.
Scientific Reports
|January 8, 2025
Summary
This study enhances power distribution network fault detection using wavelet analysis on the IEEE-16 bus system. Wavelet transform and SVM classifiers accurately identify various faults and transient conditions, even with renewable energy integration.
Area of Science:
- Electrical Engineering
- Signal Processing
Background:
- Power distribution networks require robust fault detection methods.
- Non-stationary signals from faults and switching events pose analysis challenges.
Purpose of the Study:
- To enhance power distribution network fault detection using wavelet analysis.
- To identify optimal wavelet parameters and classifiers for transient fault signal processing.
Main Methods:
- Simulation of the IEEE-16 bus system using MATLAB/Simulink.
- Analysis of transient fault current signals with MATLAB Wavelet Toolbox.
- Evaluation of decomposition levels and mother wavelets (e.g., sym3) for SVM classifiers.
Main Results:
- Optimal decomposition level identified by energy concentration (D8 scale).
- Sym3 wavelet function demonstrated superior performance for SVM classifiers.
- Nearly 100% accuracy in detecting/discriminating all fault types and transient conditions, including with a 9 MW wind farm integration.
Conclusions:
- Wavelet transform combined with SVM classifiers provides highly accurate fault detection.
- The proposed method is effective for complex power systems with renewable energy sources.
- Optimized wavelet parameters significantly improve fault detection performance.
Keywords:
Decomposition level selectionDiscrete wavelet transform (DWT)Distribution networksFault detectionFeature extractionMATLAB classifier learnerMATLAB wavelets toolboxMother wavelets selectionSupport vector machineMore Related Videos
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