## 4 Simulation Analysis and Result Verification  
The proposed method uses the parametric equations of 15 PQD signals, including pure sine waves, to evaluate the classification performance of the proposed algorithm. The PQD simulation data set consists of nine single types, namely pure sinusoidal waveforms, sag, swell, interrupt, harmonics, Oscillatory transient, flicker, notch and spikes. The six complex PQD signals include sag with harmonics, swell with harmonics, interruption with harmonics, harmonic with flicker, flicker with sag and flicker with swell. The parameter variation of the power quality disturbance equation conforms to the parametric equation of the Institute of Electrical and Electronics Engineers 1159(IEEE-1159) standard [22]. The power quality disturbance signal specifications are: amplitude 1 pu, duration \(t=0.2\) s, total period \(T=10\), total sampling point 1280 and sampling frequency 6.4 kHz. Each power quality disturbance type simulation generates 300 signals, to give a total of 4500 signals. These signals are stored in a matrix of size \(4500\times 1280\). A similar matrix increases the Gaussian white noise ratio by 50, 40, 30 and 20 dB at the signal-to-noise ratio (SNR). Part of the simulation signal of power quality disturbance is shown in Figure 3.  
An important feature of the multilayer perceptron extreme learning machine is that the classification speed is fast and the algorithm runs for a short time. Compared with the machine learning algorithm, the algorithm has the advantages of high classification accuracy and fast classification speed. In order to verify that the multilayer perceptron also has this characteristic in the classification of power quality disturbance, this paper compares the speed of classification of the same dataset by three algorithms. Table 3 shows the total running time of the three algorithms. All the simulations were accomplished in MATLAB 2016a software on a laptop with Intel Core i5-M-520 processor at 2.40 GHz clock speed  
Figure 2: Classification flowchart based on the H-ELM algorithm. and 8 GB of RAM [18]. As is shown in Table 3, the operating speed of H-ELM is superior to the PNN algorithm. In order to verify the classification effect of the multilayer perceptron extreme learning machine, we compared it with five other existing methods. The comparison results are shown in Table 4 and Figure 4. It can be seen intuitively from Figure 4 that our method has a good classification effect compared to other methods. Using the original data set classification, the H-ELM algorithm has a higher classification performance than the other three machine learning algorithms. When using the best feature set for classification, under the 20 dB signal-to-noise ratio, the classification effect of the algorithm is significantly improved. The principal component analysis and support vector machines (PCASVM) algorithm also has good PQD recognition accuracy, but its training speed is slow, for the same amount of data, and its operating speed is 30 times that of the H-ELM algorithm. Therefore, the high performance of the H-ELM can be seen. \begin{table}
\begin{tabular}{c c c c c} \hline
**Classifier** & **50 dB(\%)** & **40 dB(\%)** & **30 dB(\%)** & **20 dB(\%)** \\ \hline PCASVM & 98.30 & 97.40 & 96.30 & 95.00 \\ ELM & 98.40 & 97.23 & 94.90 & 91.32 \\ PNN & 94.67 & 94.32 & 93.64 & 91.00 \\ H-ELM & 98.10 & 97.60 & 96.27 & 93.20 \\ PSO-H-ELM & 98.60 & 98.10 & 97.67 & 95.20 \\ \hline \end{tabular}
\end{table} 
Table 4: Comparison of classification accuracy of five methods. Figure 3: Power quality measured signal: (**a**) normal signal; (**b**) swell; (**c**) sag; (**d**) interruption; (**e**) harmonics; (**f**) flicker. \begin{table}
\begin{tabular}{c c c c} \hline
**SNR** & **PNN(s)** & **H-ELM(s)** & **PSO-H-ELM(s)** \\ \hline
20 dB & 3.106 & 0.864 & 0.325 \\
30 dB & 3.134 & 0.863 & 0.330 \\
40 dB & 3.109 & 0.738 & 0.338 \\
50 dB & 3.107 & 0.953 & 0.283 \\ \hline \end{tabular}
\end{table} 
Table 3: Algorithm running time comparison.