## 3 Experimental Results and Discussion
### Comparative Analysis with Literature  
In this sub-section, a comparison of the proposed approach with the literature is presented in Table 5 to highlight the standing of the developed model. For this purpose, references utilizing 1-D signal data and 2-D image data, both with and without noisy data, are considered. A thorough evaluation of the proposed model is presented in terms of accuracy, precision, recall, and F1-score. The reported work lacks detailed performance evaluation metrics, which may result in improper classification outcomes due to class imbalance. In [24; 26; 30; 38; 44], the authors use 1-D signals for the classification of PQDs  
Figure 15: ResNet-50 with SE mechanism’s training performance for noisy and noiseless datasets. using machine learning models, whereas in [28; 29; 33], the authors consider a 2-D image dataset for PQD classification using transfer learning. The highest classification accuracy of 99.71% is achieved in [44] for noiseless 1-D signal fast discrete S-transform with a memetic firefly algorithm-based light gradient boost machine. On the other end, the authors in [28] achieved a maximum accuracy of 99.80% based on a PQD image dataset using a transfer learning approach. In comparison with the literature, our voting ensemble model has outperformed the reported methods in all of the performance evaluation matrices. Thus, the proposed method can improve the accuracy of pre-trained models for classifying PQD signals using their images. To address the computational cost of our approach and its feasibility for real-time applications in smart grids, it is important to note that CNNs are deep architectures and can be computationally expensive. However, in this method, transfer learning approaches are employed, which significantly reduce training time and computational efforts by using pre-trained CNN architectures. This allows us to focus on fine-tuning the models for PQD classification rather than training from scratch, making the process more efficient and feasible for real-time applications. PQDs can be specified both in frequency and magnitude by different standards such as IEEE 1159, EN 50160, and IEC 61000, [45]. IEEE Std. 1159 focuses on power quality monitoring and classification of disturbances like voltage disturbances, including sags, swells, interruptions, flicker, harmonics, transients, and electrical noise, whereas EN 50160 defines voltage quality, including frequency, voltage variations, flicker, harmonics, and voltage dips and swells, as well as defining thresholds for acceptable voltage variations. IEC 61000 regulates electromagnetic compatibility, including voltage dips, surges, harmonics, flicker, transients, and high-frequency disturbances. The authors in [46; 47] were able to achieve 100% (three classes) and 99.78% (nine classes, Stockwell Transform and Decision Tree) classification accuracy, respectively, using the IEC 61000 standard; however, other PQDs are not incorporated in the work. A PQD classification accuracy of 99.26% is achieved for eight classes for the EN 50160 standard using a probabilistic neural network [48]. In another work [25], eight different types of PQDs based on the EN 50160 standard are classified using a CNN model, giving a 97.94% classification accuracy. The proposed approach utilized sixteen different PQDs based on all these standards and produced a 99.98% classification accuracy. Thus, the proposed approach is substantially adaptable for various power quality monitoring environments with a high classification accuracy. In smart grid applications, real-time performance is critical to ensure timely detection and classification of PQDs. Although the ensemble deep CNN approach shows strong accuracy and robustness in classifying PQDs, it is important to assess the time performance. The training and test times (single sample and batch of fifty samples) for each architecture were recorded on a PC model Intel Core i9-9820X CPU (3.3.0 GHz), DDR4 32 GB RAM and NVIDIA Ge-Force RTX 2080 8 G GPU and are presented in Table 6.