## 5 Experimental Results  
The PQ performance is usually determined in terms of six indices, namely frequency deviation, voltage total harmonic distortion, voltage fluctuation, voltage flicker, voltage deviation and three-phase voltage unbalance. Table 3 shows the measured PQ data of the five major substation busbar nodes of a large wind farm in China, and obtained five sets of PQ indices data sets. The five substation nodes are: 10 kV busbar of substation 1, recorded as sample 1; 110 kV busbar of substation 2, recorded as sample 2; 10 kV busbar of substation 3, recorded as sample 3; 35 kV busbar of substation 4, recorded as sample 4; 10 kV busbar of substation 5, recorded as sample 5. Take the PQ data of the above five evaluation samples as an example. Since the PQ is fluctuating, the PQ data obtained only once is not sufficient to explain the overall performance of the power. Therefore, in order to obtain reasonable PQ data, long-term monitoring of observation points is required. In the PQ monitoring system, when the PQ data are collected and summarized daily, weekly, monthly, seasonally, and annually, the 95% probability value is often used as the quantitative statistical index. The data in Table 3 is the statistical value measured in days, which can represent a dynamic PQ level. The original data in Table 3 was imported into the software SPSS (version, Manufacturer, City, US State abbrev. if applicable, Country) for correlation analysis, and the Pearson correlation coefficient (Pcc) was obtained, as shown in Table 4. It can be seen from Table 4 that, under the two-sided test of the significance level of 0.01, the correlation coefficient between the voltage deviation and the three-phase voltage unbalance is 1, and the probability value of the two-sided test is 0, less than 0.01, which means the correlation degree is significant and completely positive. Under the two-sided test of  
Figure 6: The flowchart of power quality (PQ) comprehensive evaluation based on the improved TOPSIS method. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Samples/Indices 1** & \(x_{1}\)**/Hz** & \(x_{2}\)**/\%** & \(x_{3}\)**/\%** & \(x_{4}\)**/\%** & \(x_{5}\)**/\%** & \(x_{6}\)**/\%** \\ \hline
1 & 0.09 & 1.12 & 0.96 & 0.22 & 2.53 & 0.88 \\
2 & 0.04 & 1.26 & 1.05 & 0.34 & 1.66 & 1.07 \\
3 & 0.19 & 1.18 & 1.41 & 0.47 & 3.85 & 0.83 \\
4 & 0.11 & 0.82 & 0.85 & 0.38 & 2.01 & 0.58 \\
5 & 0.07 & 1.35 & 1.27 & 0.53 & 3.18 & 1.23 \\ \hline \hline \end{tabular} 1  
\end{table} 
Table 3: Measured PQ data. the significance level of 0.05, the correlation coefficient between the voltage deviation and the voltage total harmonic distortion is 0.95, and the probability of the two-sided test is 0.013, less than 0.05, which means that the correlation degree is significant and highly positive. The correlation coefficient between voltage total harmonic distortion and three-phase voltage unbalance is 0.95, and the probability of two-sided test is 0.013, less than 0.05, which means that the correlation degree is significant and highly positive. Therefore, there is a clear correlation between some indices. In this case, if traditional evaluation methods are still adopted, certain errors will be caused. The PQ measured data of Table 3 will be comprehensively evaluated according to the method proposed in this paper. The first step is to determine the combined weights of the six PQ indices. The AHP is used to calculate the subjective weights of the indices. According to the experts' opinions, the six indices can be arranged in descending order of importance as follows: frequency deviation \(>\) voltage total harmonic distortion \(>\) voltage fluctuation \(\geq\) voltage flicker \(>\) voltage deviation \(>\) three-phase voltage unbalance. The scale values can be determined according to the experts' opinions when it is actually applied. Here, it is assumed that \(r_{12}=1.8\), \(r_{23}=1.7\), \(r_{34}=1\), \(r_{45}=1.4\), \(r_{56}=1.2\). According to the Equation (1), the judgment matrix \(\mathbf{R}\) can be calculated as follows:  
\[R=\left[