## 1 Introduction
### Literature Review  
This literature review examines the latest advances and innovative techniques in PD classification and detection, emphasizing DL and signal processing methodologies. [PERSON] et al. [12] applied t-SNE to extract robust features across sensors, addressing amplitude-frequency variability. [PERSON] et al. [13] combined adaptive kernel TF representations with a ResNet-18 network, achieving 98% accuracy under signal attenuation conditions. [PERSON] et al. [14] compared 13 deep learning architectures, concluding that 2D CNNs with continuous wavelet transform (CWT) inputs outperform FFT and STFT, especially in low-SNR scenarios. Similarly, [PERSON] et al. [15] used transformer encoders to learn implicit TF features, attaining 98.6% accuracy without explicit transforms. [PERSON] et al. [16] introduced a CNN-RNN hybrid (TFMT) model with probabilistic TF maps, achieving 99.49% accuracy for cable systems. [PERSON] et al. [17] enhanced TF resolution with a Wavelet Time-Reassigned Synchrosqueezed Transform (WTST) coupled with LSTM-FCN for noisy acoustic environments. [PERSON] et al. [18] proposed an IFCNN with 1D and 2D CNN channels to fuse time-domain and wavelet-based features, reaching 95.8% accuracy across four PD types. [PERSON] et al. [19] developed a transformer-based model leveraging a Time-Reassigned Multi-Synchrosqueezing Transform (TMSST) and prior localization, achieving 98.7% mean average precision (mAP). [PERSON] et al. [20] demonstrated 100% classification using CNNs with CWT scalograms, outperforming RF classifiers. Their work also emphasized hyperparameter tuning (ELU, Adam optimizer with lr = 0.001) for optimal performance. [PERSON] et al. [21] achieved 1.9 m mean localization error using a Wavelet Packet Transform (WPT) with ensemble ML models in noisy environments. For pattern recognition, [PERSON] et al. [22] used STFT scalograms with CNNs for XLEp cable PDs, reaching 97.44% accuracy and fast inference suitable for real-time use. [PERSON] et al. developed ensemble stacking neural networks, significantly enhancing PD detection precision in covered conductors [23]. Similarly, [PERSON] et al. utilized DBN to identify and differentiate corona, surface,and internal discharges [24]. [PERSON] et al. introduced a novel DL-based framework capable of precisely identifying and locating either single or multiple PD events [25]. [PERSON] demonstrated deep CNN efficacy in classifying PD images [26]. [PERSON] et al. employed VMD and CWD spectra to improve PD recognition by optimizing CNN models [27]. [PERSON] et al. implemented a hybrid CNN-RNN model to effectively classify UHF PD patterns in transformers, outperforming conventional techniques [28]. [PERSON] et al. introduced a Bi-LSTM model with an attention mechanism to classify PD signals in insulated overhead conductors, showing enhanced accuracy and lower computational complexity [29]. [PERSON] et al. utilized LSTM networks to detect PD in aerial covered conductors, demonstrating the efficacy of time-series decomposition for signal analysis [30]. [PERSON] et al. developed CNN-LSTM models incorporating attention mechanisms to improve detection precision [31]. [PERSON] et al. showed that LSTM networks outperformed traditional methods like SVMs in classifying PRPD signals in GIS [32]. [PERSON] et al. [28] investigated CNN-LSTM models for recognizing UHF PD patterns in transformers, whereas [PERSON] et al. [33] highlighted multiresolution CNN architectures for detailed PD classification. [PERSON] et al. [34] and [PERSON] et al. [35] demonstrated that attention mechanisms can enhance classification performance while lowering computational demands.