## 1 Introduction
The literature [11] proposes a time-domain pilot protection method based on two-dimensional projections of dual differential currents, constructing an internal fault index from trajectory features. While it accounts for CT saturation and fault resistance, it requires high-resolution sampling and added hardware. The literature [12] presents a differential scheme for offshore wind farm collection lines with MMC-HVDC transmission, estimating fault-point negative-sequence voltage via open-circuit characteristics and applying torsion angle correction. The method is suited for low-fault-current conditions but involves considerable computational effort. The literature [13] introduces a current variation-based technique for unipolar ground fault detection in DC networks, independent of fault current direction and effective for weak faults, though limited by system configuration. The literature [14] proposes a unit protection strategy for zonal DC microgrids using superimposed impedance at both ends. It detects high-resistance and simultaneous faults but depends on precise thresholds and IEC 61850 communication. The literature [15] improves high-impedance differential protection by analyzing voltage waveforms under CT saturation, enhancing low-current fault detection at the cost of higher signal processing complexity. The second type of method utilizes deep learning techniques, which do not rely on predefined criteria or mathematical models but instead process raw data to extract fault features through intelligent learning. The literature [16] combines Hilbert-Huang Transform with CNN to classify ten short-circuit fault types in small-current grounding systems using time-frequency energy images, achieving high accuracy but requiring complex preprocessing. In [17], a DWT-CNN model extracts statistical features from wavelet coefficients for transformer differential protection, reaching 98% accuracy, though it may be sensitive to feature selection and data quality. The literature [18] proposes a two-terminal fault location method based on adaptive CNN with pooling optimization, offering faster and more accurate diagnosis than DBN, but its performance under varying topology remains to be validated. The literature [19] uses a 1D-CNN to map three-phase voltage/current signals to fault locations in distribution networks with distributed generation, but its scalability to large systems is limited. In [20], a CNN-based classifier achieves over 99% accuracy for short-circuit and ground fault types, though its generality under CT saturation and complex scenarios remains unclear. The literature [21] introduces an SVM regression model aided by CEEMDAN decomposition and active pulse injection for single-pole-to-ground fault location in MMC-HVDC systems, achieving good accuracy but requiring high-frequency data acquisition. In [22], a CNN-based approach utilizing wavelet features detects and classifies faults in MMC-HVDC lines, with high accuracy but computational demands that may hinder real-time deployment. An improved deep belief network (IDBN) is proposed in [23], using statistical features like standard deviation and entropy from DC, achieving rapid and accurate fault detection, though it depends on prior preprocessing. The literature [24] presents a CA-CNN-based protection method using differential current trajectory images, integrating channel attention and adaptive learning to enhance internal/external fault discrimination, though a 5 ms delay may affect ultra-fast protection needs. Traditional fiber differential protection (FDP) schemes face increasing reliability challenges in PV-connected transmission systems due to the low magnitude and variability of fault currents, brief fault durations, and nonlinear behavior of current transformers (CTs) under saturation. These factors can lead to misoperations or failure to trip during critical fault conditions.