## 4 Case Studies: Part I: Simulation Analysis
### Performance Comparison with Advanced PQD Classification Methods
#### 4.4.2 Performance Comparison with Advanced PQD Classification Algorithms  
To further validate the superiority of the proposed algorithm framework in PQD classification, it is compared with eight published advanced classification algorithms, which are divided into machine learning-based algorithms and deep learning-based algorithms. The machine learning-based algorithms include DWT&SVM [6], ST&RF [7], and VMD&LGBM [8], which were originally designed for 10, 13, and 15 types of PQDs, respectively. The deep learning-based algorithms comprise FCN&LSTM [9] and DNN [10], which take one-dimensional signals as model inputs, as well as GAF&CNN [11], TC&ResNet18 [12], and ITC&ResNet50 [13], which convert one-dimensional signal sequences into two-dimensional images and then combine them with image recognition networks. These five deep learning-based algorithms target 12, 16, 17, 26, and 28 types of PQDs, respectively. To better align with practical measurement conditions in engineering applications, the performance of each algorithm is tested in a noise environment of 60 dB. To ensure the fairness of each algorithm in the classification performance test, based on the same 41-type PQD signal dataset, each algorithm is used for signal processing and model training. Meanwhile, a hyperparameter optimization method based on grid search is adopted to solve the problem of parameter tuning difficulty caused by the complexity of the dataset to improve the overall algorithm performance. The sequential grid search, as detailed in [20], is used to determine the optimal parameters in turn. Compared with the global grid search, this approach significantly improves efficiency while maintaining the search quality. Accuracy is selected as the performance evaluation metric. The grid search results for the optimal hyperparameters are shown in Table 6. The comprehensive performance of each trained classification model on the test set is shown in Table 7. Here, time refers to the average time taken by each algorithm to classify a signal sample in the test set, including signal processing time and model classification time. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Hyperparameters**} \\ \cline{2-4}  & **Item** & **Range** & **Values** \\ \hline \multirow{3}{*}{SVM} & Kernel & linear, poly, rbf, sigmoid & rbf \\  & Regulation parameter \(c\) & 0.5, 1, 10, 100 & 1 \\  & Gamma & Scale, auto & scale \\ \hline \multirow{3}{*}{RF} & Criterion & gini, entropy & gini \\  & Decision tree number & 400, 500, 600, 700 & 600 \\  & The max depth & None, 5, 10, 20 & None \\ \hline \multirow{3}{*}{LGBM} & Estimator court & 100, 200, 300, 400 & 200 \\  & Max tree depth & 1, 20, 50, 80 & 20 \\ \cline{1-1}  & Leave number & 100, 200, 300, 400 & 100 \\ \cline{1-1}  & lambda & 0, 100, 200, 300 & 200 \\ \hline \multirow{3}{*}{FCN-LSTM} & Batch size & 16, 32, 64, 128 & 128 \\  & Convolutional kernel size & (1,1), (3,3), (5,5) & (3,3) \\ \cline{1-1}  & Number of neurons & 128, 256, 512, 1024 & 512 \\ \cline{1-1} \cline{2-4}  & Optimizer & SGD, Adam, Nadam & Adam \\ \cline{1-1}  & Dropout rate & 0, 0.2, 0.4, 0.6 & 0.4 \\ \cline{1-1}  & Hidden activation function & ReLU, Tanh, Sigmoid & ReLU \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Grid search results of optimal hyperparameters. It can be seen in Table 7 that for 41 types of complex PQD datasets, which incorporate up to four distinct and intertwined single disturbances within the same waveform, the accuracy of machine learning-based algorithms does not exceed 90%. This is mainly because, for highly nonlinear and dynamically changing complex composite disturbance signals, the manual feature extraction methods designed based on expert experience cannot fully extract high-order coupling features, resulting in insufficient generalization performance and limiting its accuracy performance.