## 4 Simulation Analysis and Result Verification
SNR, signal-to-noise ratio; PNN, probabilistic neural networks. Table 4 shows that the classification performance of the H-ELM algorithm is best when using the best feature set selected by PSO for classification. The performance of the pure H-ELM algorithm is better than that of other machine learning algorithms for the classification of power quality disturbances. It can be clearly seen from the results of the first four sets of experiments that the classification performance of the H-ELM classifier is better under the same data volume and the same feature set. Compared with the existing methods, the classification effect of the model proposed in this paper is obviously improved under various signal-to-noise ratios. It is proved that H-ELM has better classification performance for power quality disturbance data. In the simulation analysis, in order to verify that the features selected by the PSO feature selection promoted the classification results, the experiments using the original 63 features for classification and the classification using the best feature set were analyzed. The experimental results are shown in Table 5. From the four aspects of training time, training accuracy, test time and test accuracy, it can be clearly seen that the features determined by the PSO feature selection are used for classification, which has better classification accuracy and faster classification speed. Feature selection is only the determination phase of the initial feature, and is not embedded in the program. Just like the expert determines the feature, it only implements this process through the optimization method, so as to get a better classification effect. Figure 5 shows a comparison of training and test times for categorizing the best features selected using the PSO feature selection and classifying them with the original feature set. It can be seen from the figure that the classification is performed using the best feature set, and the training speed and test speed are significantly better than the original features. Figure 4: Five methods of classification accuracy rate histogram. PCAVSM—principal component analysis and support vector machines. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**SNR** & **Classifier** & **Train Time (s)** & **Train Accuracy (\%)** & **Test Time (s)** & **Test Accuracy (\%)** \\ \hline
20 dB & PSO-H-ELM & 0.155 & 95.55 & 0.150 & 95.20 \\  & H-ELM & 0.204 & 95.54 & 0.164 & 93.20 \\
30 dB & PSO-H-ELM & 0.101 & 97.57 & 0.140 & 97.67 \\  & H-ELM & 0.194 & 97.25 & 0.144 & 96.27 \\  & PSO-H-ELM & 0.120 & 98.61 & 0.163 & 98.10 \\
40 dB & H-ELM & 0.202 & 98.53 & 0.170 & 97.60 \\  & PSO-H-ELM & 0.169 & 99.24 & 0.120 & 98.60 \\
50 dB & H-ELM & 0.200 & 98.93 & 0.115 & 98.10 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison between the PSO-H-ELM and H-ELM algorithms. Figure 6 shows a comparison of the classification accuracy using the best feature set and the original feature set. As can be seen from the figure, the blue histogram is significantly higher than the orange histogram. It shows that the classifier's classification accuracy is significantly improved after using the best feature set. Table 6 shows the classification accuracy rate of each disturbance, and that the overall disturbance classification accuracy rate is above 95%, which satisfies the actual classification needs. The misclassified samples are mainly concentrated in the 20 dB signal, with and without harmonic disturbance, indicating that high noise has greater interference with the identification of signal harmonics. Figure 5: Test and training time histogram. Figure 6: Test and training accuracy histogram.