# Optimal Strategy to Select Load Identification Features by Using a Particle Resampling Algorithm
## 3 Experiments and Results
### Laboratory Validation
In order to describe the state clearly, Table 5 illustrates the fluctuation range of active power and reactive power. After resampling, the fluctuation range is lower than before resampling. So, the load feature can be obtained properly. Here, we used the difference of the center of resampling particles after the device was switched on and the center of the resampling particles before the device was switched on as the load feature. Finally, the obtained active power was 462.17 W and the reactive power was \(-\)150.55 Var. Referring to Table 1, it can be found that the value of the obtained active power and reactive power was in the range of air conditioning, rather than the range of other devices. So we can easily identify this device. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Devices** & **State** & **Change Point** & **Difference of P/W** & **Difference of Q/var** \\ \hline Air conditioner & on & 103 & 26.1 & \(-\)8.4 \\  & off & 774 & \(-\)500.07 & 146.66 \\  & on & 178 & 1562 & 51.2 \\ Induction cooker & off & 552 & \(-\)1635 & \(-\)86.51 \\  & on & 250 & 1566 & \(-\)14.1 \\ Kettle & off & 406 & \(-\)1211 & 18.5 \\  & on & 310 & 755 & \(-\)291.86 \\ Microwave oven & off & 608 & \(-\)1279 & 66.9 \\  & on & 362 & 523 & \(-\)2.7 \\ Rice cooker & off & 647 & \(-\)614.6 & 16.43 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Differences on the load switching point. Figure 7: _Cont._  
In addition, Table 6 shows the obtained P and Q features using our method. It can be seen that the value of P and Q had a better correlation with the load features in Table 1. Notably, the obtained value did not change the sign if the status of the load was turning off. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Turn on** & **P/W** & **Q/Var** & **Turn off** & **P/W** & **Q/Var** \\ \hline Event A & 461.2 & \(-\)150.6 & Event F & \(-\)1325.5 & \(-\)29.8 \\ Event B & 1622.2 & 46.7 & Event G & \(-\)1568.6 & \(-\)54.92 \\ Event C & 1410.0 & \(-\)25.3 & Event H & \(-\)1242.2 & 119.9 \\ Event D & 975.4 & \(-\)283.6 & Event I & \(-\)609.6 & 1.3 \\ Event E & 687.7 & 33.5 & Event J & \(-\)556.0 & 156.77 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Load characteristic information after resampling. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scenes** & **Before Device** & **After Device** & **Before Device** & **After Device** \\  & **Turning on P/W** & **Turning on P/W** & **Turning on Q/Var** & **Turning on Q/Var** \\ \hline Before resampling & 48.1–68.9 & 374.3–558.3 & 147.1–247.6 & 76.5–141.2 \\ After one resampling & 52.8–68.9 & 505.1–558.3 & 227.8–247.6 & 76.5–109.2 \\ After resampling & 60.5–71.6 & 513.8–540.5 & 226.5–246.0 & 80.7–89.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Air conditioning load switching and resampling results. Figure 7: Air-conditioning resampling process: (**a**) particles before resampling (before change point); (**b**) particles before resampling (after change point); (**c**) particles after one resampling (before change point); (**d**) particles after one resampling (after change point); (**e**) particles after resampling (before change point); and (**f**) particles after resampling (after change point). In order to verify the effectiveness of the above-obtained features, here, an induction cooker, a kettle, an air conditioner, a rice cooker, and a microwave oven are represented by \(A_{1}\), \(A_{2}\), \(A_{3}\), \(A_{4}\), and \(A_{5}\). In addition, nine levels of fuzzy language are used for active and reactive features:  
\[\Psi=\{EB,VB,B,MB,F,MG,G,VG,EG\}, \tag{15}\]  
respectively, denoted as extremely bad, very bad, bad, more or less bad, fair, more or less good, good, very good, and extremely good, and they are linked to the finite chain \(L=\{1,2,\ldots,9\}\).