## 5 Real Signal Classification Verification  
To further verify the feasibility of the proposed method in the actual signal, in this section, a set of actual signals is used to test the effectiveness of the H-ELM. The data set is provided by the IEEE Power Engineering Society database [23; 24] for PQD classification. This data set has been tested in reference [25] for power quality classification effects to meet the needs of the experiment. The sampling rate of the supplied signal is 256 points per cycle. Each signal has a length of 1536. The obtained waveforms are determined label by label, and the data set is processed according to the data enhancement method. The actual disturbance signal is shown in Figure 7.  
The actual power quality disturbance data has an unbalanced feature. For example, the type of disturbance of voltage sag accounts for more than 80% of all disturbances. There is no relevant research \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Disturbance Type**} & \multicolumn{4}{c}{**Classification Accuracy (\%)**} \\ \cline{2-5}  & **50 dB** & **40 dB** & **30 dB** & **20 dB** \\ \hline Normal & 100 & 100 & 100 & 98 \\ Sag & 96 & 96 & 94 & 94 \\ Swell & 88 & 86 & 88 & 90 \\ Interruption & 100 & 100 & 100 & 100 \\ Harmonics & 100 & 100 & 100 & 100 \\ Oscillatory transient & 100 & 100 & 100 & 100 \\ Spike & 100 & 100 & 100 & 100 \\ Flicker & 96 & 94 & 96 & 84 \\ Periodic notch & 100 & 100 & 100 & 100 \\ Sag with harmonics & 98 & 98 & 92 & 82 \\ Swell with harmonics & 100 & 100 & 100 & 98 \\ Interruption with harmonics & 100 & 100 & 100 & 100 \\ Flicker with Harmonic & 100 & 100 & 100 & 100 \\ Flicker with sag & 98 & 98 & 96 & 84 \\ Flicker with swell & 100 & 100 & 100 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Classification effect of each disturbance type of PSO-H-ELM. Figure 7: Power quality measured signal (**a**) transient oscillation; (**b**) sag; (**c**) harmonics. on this problem. This paper proposes a data enhancement method to preprocess data. In computer vision, data enhancement is often used to increase the number of training samples to enhance the generalization performance of the classifier. In this paper, for the problem of data imbalance, the data enhancement method is adopted, the amount of data is equalized and the data enhancement operation is performed for disturbances such as flicker with less data volume. The data enhancement operation mainly adopts random cropping, moderately increases random noise, reverses the signal, etc., and performs random extraction verification on all the operation signals to ensure that the data after data enhancement belongs to the disturbance type data. The data enhancement operation is shown in Figure 8.  
The measured data set is classified and verified according to the method in this paper. The optimal feature combination and classifier parameters are shown in Table 7.  
We verified the real signals by five methods. The classification accuracy rate and algorithm running time of the five methods are shown in Table 8. It can be seen from the table that the proposed method achieves better classification performance on the measured data. Since the amount of real signal data is small, the samples of various types of signals are small, and even after the data enhancement operation, the amount of data is only 1000 sets. Therefore, the improvement of classification performance by the best features is affected. Since the real signal is more complex than the simulated signal, the real signal accuracy is reduced compared to the simulated signal. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Numerical Value** \\ \hline Best feature set & \(\mu\_3\), \(\mu\_7\),\(\sigma\_2\), \(\sigma\_7\),RMS \(\_4\),RMS \(\_6\),KT\(\_6\),KT\(\_7\),KT\(\_9\),Ent\(\_2\), \\  & Ent\(\_6\),Ent\(\_7\),Ent\(\_8\),RG\(\_1\) \\ H-ELM hidden layer node & N1=N2=10, N3=290 \\ L2 penalty P on the last layer of ELM & 2\({}^{\text{-}}\)30 \\ Scale factor S & 0.8 \\ \hline \hline \end{tabular}
\end{table}