## 3 Experimental Results and Discussion
### Comparative Analysis with Literature
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Without Noise**} & \multicolumn{3}{c}{**20 dB Noise**} & \multicolumn{3}{c}{**30 dB Noise**} & \multicolumn{3}{c}{**Random Noise**} \\ \cline{2-13}  & & & & & & & & & & & & & & & \\ \hline \multicolumn{13}{c}{**1-D Signals**} \\ FDST+MFA\_LGBM [44] & 99.71 & - & - & - & 96.85 & - & - & 98.45 & - & - & - & - & - & - & - \\ SE with (LR+NN+48 DD [30]) & 91 & 91.5 & 91 & 91.10 & - & - & - & - & - & - & - & - & 89.33 & 89.60 & 89.3 \\ DR with (RNN, SVM, NB, RE) [88] & - & - & - & - & 99.72 & - & - & - & 99.48 & - & - & - & 99.65 & - & - \\ Bagging-LSTM [24] & - & - & - & - & 98.67 & - & - & - & 99.20 & - & - & - & - & - \\ HT+DAOM [25] & 99.44 & 99.24 & 99.15 & 99.19 & - & - & - & - & 98.95 & 98.58 & 98.05 & 98.31 & - & - & - \\ \multicolumn{13}{c}{**2-D Images**} \\ Pre-trained deep Networks [28] & 99.80 & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - \\ ST+CNN [29] & 99.12 & - & - & - & 98.57 & - & - & - & - & 98.14 & - & - & - & 83.45 & - & - \\
1-D+2-D CNN [33] & 99.71 & - & 99.53 & 99.80 & - & - & - & - & - & - & - & - & - & - \\
**Proposed Approach** & **99.98** & **99.97** & **99.80** & **99.85** & **99.73** & **98.23** & 97.23 & 97.78 & 99.90 & 99.83 & 99.65 & 99.80 & 99.88 & 98.68 & 98.10 & 98.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance comparison of the proposed approach with the literature. ResNet-50, VGG-16, and ResNet-50 with SA mechanisms show higher accuracy compared to other deep models; however, relatively high training times are required, as these are deep and computationally intensive models compared to AlexNet and SqueezeNet. Moreover, a relatively long time is also taken to process the testing images. The training time is not provided for the ensemble model as it relies on the performance of the base models. Due to the complexity involved in combining multiple models, the voting ensemble with the highest classification accuracy requires 2.75 s to process a batch of fifty test images. For a single test sample, the test time is computed as 55.1 ms. The stated inference time illustrates that the proposed system can detect and classify disturbances rapidly, with each model's testing performance for a single test sample making them suitable for real-time grid monitoring. The ability to process each sample in milliseconds provides sufficient time for automated control systems to initiate protective or corrective actions within the necessary dynamics of a power grid. Furthermore, by leveraging edge computing devices equipped with GPUs or TPUs, the low-latency classification can be implemented locally, minimizing communication delays and further enhancing real-time responsiveness. Therefore, the proposed model has real-time application importance. In addition to this, several strategies can be incorporated to meet the rigorous time constraint, including modern hardware such as GPU/TPU and utilizing a sliding window technique, and model pruning and quantization can further enhance the inference speed without sacrificing classification accuracy. The deep CNN-based ensemble approach used in this study for PQD classification can be effectively integrated into a power grid through a three-layered architecture consisting of data acquisition and preprocessing, disturbance detection, and a communication interface. In the first layer, smart sensors and Phasor Measurement Units (PMUs) are deployed across the grid to continuously monitor electrical parameters by capturing voltage and current signals at various locations. These signals are then preprocessed and converted from 1-D time domain signals into time-frequency images using CWT. Depending on the grid's setup and communication capabilities, this preprocessing can be performed either on local edge devices near the measurement points or at centralized data hubs. In the next layer, the preprocessed time-frequency images are fed into the deep CNN-based ensemble model for disturbance classification.