# A Method of Discriminating Between Power Swings and Faults Based on Principal Component Analysis
## 4 Simulation Tests
### Comparison and Discussion  
Some comparisons are made here. In terms of comparison with traditional methods, the method introduced in [5] is chosen as a representative traditional method for comparison with the proposed method. For comparisons with novel methods, those found in references like [8; 9], which demand high sampling frequencies and encounter practical challenges, are not selected for comparison. Moreover, methods akin to reference [28] that rely on phasor data are also excluded from the comparison, as the distinct features between these methods and the proposed methods have already been highlighted when contrasted with traditional methods. The method introduced in reference [27], which utilizes instantaneous values and has minimal frequency requirements, resembles the proposed method. Hence, it is selected as representative of the novel methods for comparison with the proposed method. In addition to the judgment results of these three methods, the operating speed and the effect of the measuring noise on them are also discussed here. Different disturbance scenarios were simulated based on the two-terminal system shown in Figure 6. Tables 7-11 show the comparison results. In these tables, \"SNR\" represents the signal-noise ratio, \"R\" represents the test result, \"DI\" is the index used in the method in [27], for which the corresponding setting value is set to 0.01 here, and \"\(k\)\" is the index used in the traditional method in [5], for which the corresponding setting value is set to 0.3 here. For the method in [27], if the DI is less than 0.01, the disturbance is judged to be a power swing. Otherwise, it is judged to be a fault. For the traditional method in [5], if \(k\) is greater than 0.3, the PSB unit will deblock the protection relay. The symbol \"T\" in Tables 7 and 8 represents the time required from the start of the method to obtain the corresponding value of the judgment index. \"T\" in Tables 9-11 represents the time from the moment at which the fault occurs to the moment at which the corresponding value of the judgment index is obtained. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Distance (km)** & \(\delta\) & \(\Delta\mathbf{P}\) & **Result** \\ \hline
1 & 10 & 50\({}^{\circ}\) & 1279.13 & F \\
2 & 70 & 50\({}^{\circ}\) & 1068.65 & F \\
3 & 125 & 50\({}^{\circ}\) & 873.70 & F \\
4 & 180 & 50\({}^{\circ}\) & 683.19 & F \\
5 & 240 & 50\({}^{\circ}\) & 533.45 & F \\
6 & 240 & 80\({}^{\circ}\) & 676.34 & F \\
7 & 240 & 120\({}^{\circ}\) & 769.19 & F \\
8 & 240 & 150\({}^{\circ}\) & 711.32 & F \\
9 & 240 & 180\({}^{\circ}\) & 560.03 & F \\
10 & 240 & 220\({}^{\circ}\) & 413.78 & F \\
11 & 240 & 260\({}^{\circ}\) & 388.03 & F \\
12 & 240 & 330\({}^{\circ}\) & 374.51 & F \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Test results of PSD for nine-bus system. Since the traditional method is applied for detecting faults during power swings in [5], Tables 7 and 8 only show the performance of the proposed method and the method introduced in [27] for discriminating between power swings and faults. Table 7 shows the test results under different power swing situations and Table 8 shows the test results under different fault situations. From Tables 7 and 8, it can be seen that both methods can