## 5 Case Studies: Part II: Real-World Measured Data Analysis
### Comprehensive Performance Comparison with the Existing PQD Classification Algorithms  
In order to further verify the superiority of the proposed algorithm in the PQD classification task of engineering practice, we select the top three existing PQD classification algorithms ranked by accuracy performance in Section 4.4.2: GAF + CNN [11], TC + ResNet-18 [12], and ITC + ResNet-50 [13]. These serve as benchmark algorithms for performance comparison. Among them, the size of the sequence visualization images remains consistent with the original settings, while the hyperparameters of the classification network are configured identically to those in this paper. Under the same experimental conditions, 41 types of PQDs are generated, with 1000 samples per type for the GAF, TC, and ITC image datasets, which are divided in the same proportions and used as inputs for the classification \begin{table}
\begin{tabular}{c c c c} \hline
**Label** & **Type of PQDs** & **Number of Samples** & **Classification Results** \\ \hline C1 & Sag & 68 & C1: 61/C3: 2/C1 + C5: 4/C1 + C8: 1 \\ C2 & Swell & 25 & C2: 25 \\ C3 & Interruption & 3 & C3: 3 \\ C4 & Harmonic & 24 & C4: 23/C4 + C5: 1 \\ C6 & Pulse & 5 & C6: 5 \\ C7 & Flicker & 34 & C7: 30/C4 + C7: 3/C6 + C7: 1 \\ C1 + C4 & Sag + Har & 18 & C1 + C4: 16/C1: 2 \\ C1 + C5 & Sag + Oscill & 12 & C1 + C5: 9/C1 + C4: 2/C1 + C4 + C5: 1 \\ C6 + C7 & Pulse + Flicker & 5 & C6 + C7: 5 \\ C2 + C9 & Swell + Spike & 5 & C2 + C9: 5 \\ C1 + C4 + C7 & Sag + Har + Flicker & 5 & C1 + C4 + C7: 5 \\ C3 + C4 + C5 & Interr + Har + Oscill & 3 & C3 + C4 + C5: 2/C1 + C4: 1 \\ \hline \end{tabular}
\end{table} 
Table 9: Classification results of PQD measured signals using LT+L-DenseNet-CBAM. network. After training the CNN, ResNet-18 and ResNet-50 classification networks and obtaining the models that perform best on the validation set, they are respectively applied to the GAF, TC, and ITC image datasets converted from real-world measured signals. The classification results are then comprehensively compared with those of the proposed algorithm, as shown in Figure 10 and Table 10. The times for visualization conversion and model classification are both the average durations obtained from 50 consecutive tests for a signal sample. It can be seen that in terms of classification accuracy, since GAF is prone to losing important feature information, such as signal amplitude, during the encoding process, and the real-time signal state changes are more complex, while the depth of the CNN network is relatively shallow and unable to capture the deep semantic information of the image. As a result, the overall classification accuracy is only 67.63%. During signal visualization, TC exhibits significant curve jumps and endpoint effects, which interfere with the classification network's ability to capture essential feature information, thereby reducing the model's classification performance. As an improved version of TC, ITC effectively eliminates curve jumps and endpoint effects using ESM. However, this method tends to cause the loss of envelope feature information during signal processing. As a result, its accuracy is also limited. In terms of classification efficiency, the proposed algorithm significantly outperforms other methods in visualization transformation efficiency due to the simplicity of the LT conversion process, which requires only the construction of an ideal reference signal. Additionally, due to its lightweight structural design, the model achieves higher classification efficiency compared to the larger-scale ResNet-18/ResNet-50. However, its performance is slightly inferior to that of CNN, as CNN has lower Params, FLOPs, and a smaller model size, making it structurally simpler. Nevertheless, the inherent accuracy limitations caused by its weaker feature extraction capability may render it unsuitable for practical engineering PQD classification tasks. Thus, the effectiveness and superiority of the proposed LT + L-DenseNet-CBAM framework in real-world measured PQD classification tasks have been fully validated.