## 3 Experimental Results and Discussion
### Experimental Setup  
In this sub-section, the PQD dataset of different noise levels is presented. In addition, the parameter settings of the DCNN models are also given in this part. The performance of the presented model is assessed on the synthetic database comprising sixteen different PQD signals, including the single and composite disturbances derived from [36] given in Section 2. The generated dataset consists of a total of 8000 signals, including 500 signals for each class. It is instrumental in adding noise to the PQD signals to create a more realistic dataset. In light of this, various noise levels such as 20 dB noise, 30 dB noise, and random noise are added to the original PQD signals of each class. Afterward, the 1-D signals are converted into time-frequency 2-D images by employing CWT. Thus, for each noise level, an image dataset of 500 \(\times\) 16 is prepared, out of which 7200 images are used for training and validation and the remaining 800 images are reserved for testing the proposed framework. Various experiments with different training options have been performed to achieve the optimal performance of each deep model. The best settings of the parameters for each deep model are given in Table 3.  
In deep learning, hyperparameter tuning plays a critical role in optimizing the performance of models. The minimum batch size, max epochs, and learning rate were considered for hyperparameter optimization for the different models in this study, as shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & & & & & & & & \\ \cline{3-8} \multirow{2}{*}{DCNN **Model**} & \multirow{2}{*}{**Optimizer**} & \multicolumn{6}{c}{**Hyperparameter with**} & \multicolumn{6}{c}{**Optimized Value**} \\ \cline{3-8}  & & & **Search Space** & & **Range** & **Size** & **Epoch** & **Number of** & **Input Image** \\ \cline{3-8}  & & & **Learning Rate** & **Batch Size** & **Epoch** & **Rate** & **Size** & **Epoch** & **Layers** & **Size (Pixel)** \\ \hline ResNet-50 & SCD & & & & & & 177 & 224 \(\times\) 224 \\ VGG-16 & SCD & & & & & & 41 & 224 \(\times\) 224 \\ AlexNet & SCD & [0.01, 0.001, 0.00015] & [16; 32; 48] & [10; 20; 30] & 0.0001 & 32 & 30 & 25 & 227 \(\times\) 227 \\ SqueezeNet & SGD & & & & & & 68 & 227 \(\times\) 227 \\ ResNet-50 & & & & & & & & 177 & 224 \(\times\) 224 \\ with attention mechanism & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Training options for DCNN models.  
A random search algorithm was performed, starting with a default learning rate of 0.001, and fine-tuned through multiple iterations, eventually selecting 0.0001 to ensure stable convergence without overshooting during training, while the mini batch size varied between 16 and 48 with steps of 16. Similarly, the model was tested with 10 epochs to observe the performance and convergence trends. After analyzing the validation performance, 30 epochs were settled on to avoid overfitting, as further training did not significantly improve accuracy. By using pre-trained CNN architectures and fine-tuning only the final layers, the robustness of these models was utilized to avoid overfitting the dataset. By following this comprehensive hyperparameter tuning and avoiding an overfitting strategy, optimal performance for PQD classification was ensured, exhibiting a balance between performance and generalization. The proposed technique is implemented in MATLAB using a PC model Intel Core i9-9820X CPU (3.3.0 GHz), DDR4 32 GB RAM, and NVIDIA GeForce RTX 2080 8G GPU.