## 5 Experimental Results
The proximity values calculated by the method in this paper are sorted as: \(c_{4}>c_{1}>c_{2}>c_{5}>c_{3}\), that is, sample \(4>\) sample \(1>\) sample \(2>\) sample \(5>\) sample \(3\). Obviously, the improved TOPSIS method proposed in this paper differs from the traditional TOPSIS method in the ranking of samples \(3\) and \(5\). According to the experience given by experts, the importance of the six PQ indices in descending order is as follows: frequency deviation \(>\) voltage total harmonic distortion \(>\) voltage fluctuation \(>\) voltage flicker \(>\) voltage deviation \(>\) three-phase voltage unbalance. According to the above order, the PQ of the samples \(3\) and \(5\) are compared in detail, as shown in Table 7. It can be seen that the sample \(5\) is better than the sample \(3\), and this result is consistent with the evaluation result of this paper. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Sample 1** & **Sample 2** & **Sample 3** & **Sample 4** & **Sample 5** \\ \hline Traditional TOPSIS method & 0.492999 & 0.395786 & 0.271828 & 0.858968 & 0.213107 \\ Proposed method & 0.558041 & 0.511785 & 0.302466 & 0.733041 & 0.361819 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of evaluation results. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Samples/Indices** & \(\mathbf{x_{1}}\) & \(\mathbf{x_{2}}\) & \(\mathbf{x_{3}}\) & \(\mathbf{x_{4}}\) & \(\mathbf{x_{5}}\) & \(\mathbf{x_{6}}\) \\ \hline
1 & 0.026787 & 0.312209 & 0.119204 & 0.030674 & 0.212933 & 0.066747 \\
2 & 0.011905 & 0.351235 & 0.130379 & 0.047405 & 0.139711 & 0.081158 \\
3 & 0.056550 & 0.328934 & 0.175081 & 0.065530 & 0.324028 & 0.062955 \\
4 & 0.032740 & 0.228581 & 0.105545 & 0.052982 & 0.169168 & 0.043992 \\
5 & 0.020834 & 0.376323 & 0.157697 & 0.073895 & 0.267639 & 0.093294 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Weighted normalized matrix. In order to further verify the effectiveness of the improved TOPSIS method for comprehensive evaluation of PQ in consideration of the correlation of indices proposed in this paper, the evaluation results of existing evaluation methods, including AHP, fuzzy theory method, combined weighting method, traditional TOPSIS method, and the results of this paper are compared, as shown in Table 8. In the process of calculation, the traditional AHP needs to construct the judgment matrix, and the consistency test is performed many times, that is, the calculation amount is huge. The fuzzy theory method is too subjective because it relies too much on experience in determining the membership function and obtaining weights, and does not consider the information of the data itself. The combined weighting method only aggregates the PQ data through the combination weights of the indices, and lacks the rigor of engineering mathematics. None of the above methods took measures to resolve the correlation between PQ indices. It can be seen from Table 8 that the evaluation results of this paper are consistent with the evaluation results of other methods, which is consistent with the fitting trend of each evaluation result, which confirms the effectiveness and superiority of the proposed method.