## 5 Case Studies: Part II: Real-World Measured Data Analysis  
### Engineering Effectiveness Analysis of the Proposed PQD Classification Algorithm  
To verify the effectiveness of the proposed algorithm in practical engineering applications, classification performance tests are conducted using measured disturbance signal data collected from PQ monitoring sensors at a 10 kV substation in the southern Jiangsu power grid, China. The nominal frequency of the system is 50 Hz, and the sampling rate of the PQ monitoring sensor is 12.8 kHz, meaning that one sample point is recorded every 78 \(\upmu\)s. To verify the classification performance of the proposed algorithm for continuous sliding window sample slices, each collected signal waveform sample has a duration of 0.24 s (12 cycles), resulting in a total of 69 signal samples. Since slicing is performed every four cycles, a total of 207 disturbance signal samples are included in the test. To meet the input requirements of the proposed image classification model, each waveform sample is normalized and sliced every four cycles. Meanwhile, according to the LT conversion process in Section 2.2, the ideal reference signal is constructed through (2) and combined with the target slice sample, which are respectively used as the Y-axis and X-axis of the Cartesian coordinate system for Lissajous curve synthesis, thereby generating the LT representations of the measured disturbance signals. The real-world measured waveforms and their sliced LT representation for typical PQDs are shown in Figure 9. It can be observed that the LT corresponding to the measured disturbance signals still exhibits morphological characteristics similar to the trajectories of standard signals, demonstrating that LT conversion maintains excellent feature representation capability for measured signal data. To better reflect real-world measurement conditions, we retain the optimal L-DenseNet-CBAM trained model in the 60 dB SNR environment and classify all LT samples corresponding to the measured signals, with the results presented in Table 9.  
Figure 9: Measured waveforms and their Lissajous trajectories for typical PQDs. The red lines represent the LT corresponding to PQDs with waveform distortion, while the dotted lines indicate the LT corresponding to normal signal waveforms. It can be seen in Table 9 that in terms of single disturbance classification performance, the proposed algorithm demonstrates excellent accuracy for disturbance types C2, C3, C4, and C5, while partial confusion occurs for C1 and C7. Specifically, for the C1-Sag type, two samples are misclassified as interruptions due to their large voltage drop amplitude, which is close to the critical threshold between sag and interruption. Meanwhile, due to the influence of environmental noise, four groups of samples are determined to contain oscillatory transients. Furthermore, due to the widespread integration of power electronic devices and nonlinear loads in practical power systems, high-order harmonics are continuously injected during operation, resulting in certain distortions in voltage waveforms. Consequently, one sample is identified as Sag + Notch. Similarly, for the C7-Flicker, the introduction of high-order harmonics leads to three samples being recognized as double disturbances involving harmonic components. Moreover, one sample is detected as containing a pulse component due to the presence of a severe noise point. In terms of the classification of compound disturbances with more complex signal fluctuation characteristics, due to the distortion interference from environmental noise and injected harmonics on the PQD signal waveform, there are a total of five groups of samples in the C1 + C4/C1 + C5 types with determination confusion, resulting in certain classification deviations. In the case of C3 + C4 + C5, one sample with a voltage drop amplitude near the critical threshold and a low-magnitude, short-duration oscillatory component is misclassified as C1 + C4.