Table 8.

Comparison with AI methods.

Protection method	Detection-method	Feature selection	Noise immunity (dB)	Fault resistance withstanding ability	Sampling-rate	Pre-training settings	Complexity in training setup time	Other remarks	Average-recognition rate
Ref.37	ANN	DWT	High (18.0 dB)	High (350.0 Ω)	Medium	Trial and error	Medium	A generalized linear model to set up hyperparameters of a non-linear model causes miss-convergence at a small window length	90.61%
Ref.16	ANN	Voltages	Low	Low (50.0 Ω)	Low	Trial and error	High	Due to the large window length, response time is slow	94.35%
Ref.38	SVM	WT	Not mentioned	Low	High	Not mentioned	High	Do not discuss noise immunity in the complicated and dynamic environment of the MT-HVDC networks	92.92%
Ref.39	ANN	Fast FT	Low (50.0 dB)	Low (100.0 Ω)	Medium	Trial and error	Medium	An in-depth analysis of noise immunity is missing, and the fault resistance range is -0.01–100-Ω-	88.85%
Proposed methodology	Improved LSTM	Norm	High (20.0 dB)	High (480.0 Ω)	Medium	Optimal settings (BO)	Low	Due to the optimal training solution, it is less prone to miss-convergence at a small window length	99.04%