Table 23 Comparison of the proposed scheme with other research studies in different aspects.

Schemes	Accuracy and Error (%)			Analysis of different Parameters’ Impact	Data Transfer Type	Studied Network	The ML Methods Used	The Investigated Fault Types
Schemes	Fault Detection	FTP Identification (Average of FP and FT)	Fault Location Error	Analysis of different Parameters’ Impact	Data Transfer Type	Studied Network	The ML Methods Used	The Investigated Fault Types
Proposed Scheme	99.895	99.79	1.575	MN, LN, DNNS, DCT, DTT, etc. Analyses	Local, Com	ACMG	H-DNNs	All Types
Superimposed voltage energy-based²⁸ (2023)	96.60 (Monitor 3)	96.60 (Monitor 3)	6.5 (GRNN3)	Load Change Analysis	Local	DN	GRNNs	SPG Faults and Capacitor Switching
Feature extraction-based²⁹ (2023)	99.022	99.20	-	F-IAL, LCS, and FT Analysis	Local	ACMG	-	All Types
Customized ANN-based³⁰ (2024)	96.08	96.08	4.92	No	Com	DN	Customized ANNs	All Types
Multi-layer perceptron-based³¹ (2024)	93.89	-	-	OS, PT, and RO Analysis	N/A	Fault-recording curve files	MLP	Single-Phase Earth Faults
DNN-based multi-agent protection scheme³² (2024)	99.25	99.78	3.20	No	Local	ACMG	DNNs	All Types
DT and RNN-based scheme⁴² (2023)	99.11	-	1.69 (Average of 5 test data)	-	Com	DCMG	DT and RNNs	DC faults
SVM and RBFNN-based scheme⁴³ (2023)	96.18	-	2.85 (Average of 6 test data)	-	Com	DCMG	SVM and RBFNNs	DC faults
Two DTs and FFNN-based scheme⁴⁴ (2023)	96.62 (Output of two DTs classifiers)	-	4.01 (Average of 8 test data)	-	Com	DCMG	Two DTs and FFNNs	DC faults

MN: Measurement Number, LN: Lateral Number, DNNS: DNNs Structures, DCT: Data Collection Type, Com: Communication-based, Local: Local-based, ACMG: AC Microgrid, DN: Distribution Network, DCMG: DC Microgrid, H-DNNs: Hybrid DNNs, ANNs: Artificial Neural Networks, GRNNs: General Regression Neural Networks, SPG: Single-phase to Ground, MLP: Multi-layer Perceptron, OS: Optimization Strategy, Parameter Tuning, Regularization Optimization, F-IAL: Fault Impedance, Angle, and Location, LCS: Load and Capacitor Switching, FT: Feeder Tripping. RNN: Recurrent Neural Network, DT: Decision Tree, RBFNN: Radial Basis Function Neural Network, SVM: Support Vector Machine, FFNN: Feed Forward Neural Network.

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