Table 2 Summary of related works-based wavelet analysis with machine learning techniques.

Ref no.	Year	Feature extraction combinations	System typology	Rated voltage simulation via	Fault type	Accuracy (%)	Training time (S)
R⁴⁷	2021	WPT + SVM	Eskom- power line	132 kV/MATLAB	LIFs	98.85	Nr
R⁴⁸	2021	DWT + SVM	IEEE-33 bus	24.90 kV/DIgSILENT	LIFs	99.03	Nr
R⁴⁹	2022	DWT + SVM	IEEE-33 bus	12.66 kV/MATLAB	LIFs	100	12.246
R⁵⁰	2022	DWT + SVM	IEEE-33 bus	12.66 kV/MATLAB	LIFs	87.10	0.400
R⁵¹	2023	CNN + SVM	IEEE-123 bus	4.16 kV/MATLAB	LIFs	98.36	Nr
R⁵²	2023	DWT + SVM	IEEE-9 bus	MATLAB/SIMULINK	LIFs	99.30	0.910
R⁵³	2024	DWT + DNN	11-kV, Two-area cluster	MATLAB/SIMULINK	LIFs	100	Nr
R⁵⁴	2024	DWT + RBFNN	IEEE-5 bus	MATLAB/SIMULINK	LIFs	100	Nr
R⁵⁵	2024	RL + CNN (DML)	IEEE-33 bus	12.66 kV/MATLAB	HIFs	98.69	0.03261
R⁵⁶	2024	ST + CNN (DML)	IEEE-13 bus	4.16 kV/MATLAB	LIFs + HIFs	99.73	Nr
R⁵⁷	2024	DWT + DTE and ANN	Genuine isolated MV distribution	35 kV/MATLAB	LIFs	100	Nr

CNN convolutional neural network, DTE decision tree ensemble, DNN deep neural network, DWT discrete wavelet transform, EML ensembled machine learning, DML deep machine learning, Nr not reported, RL reinforcement learning, RBFNN radial basis function neural network, WPT wavelet packet transform, ST stockwell transform.

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