## 6 Conclusions  
Aiming at the influence of the correlation between the power quality performance parameters (that is, the PQ indices in this paper) on the comprehensive evaluation results of PQ, this paper proposes to establish a comprehensive evaluation model by combining the improved TOPSIS method and the AHP-EW combination weighting method. This method is suitable for a variety of evaluation scenarios, mainly for targets with multiple indices. In these scenarios, you only need to select the required indicators. This paper applies this method to PQ evaluation, through the analysis of actual case data, the following conclusions are drawn:  
1. There is a high correlation between some steady state indices of PQ, and it has not received attention and treatment in the previous comprehensive evaluation of PQ.
2. The Mahalanobis distance nonlinearly correlates the internal components of the evaluation matrix by its own covariance matrix. It can not only solve the problem of different dimensions of indices, but also eliminate the correlation interference between indices.
3. The new AHP-EW combination weighting method has the advantages of less calculation and strong operability compared with the traditional subjective and objective weighting method;
4. The evaluation results of the proposed method are in line with the fitting trend of other evaluation results, and the evaluation errors of other methods are corrected. The validity and superiority of the proposed TOPSIS method for comprehensive evaluation of PQ can be confirmed.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Sample 3** & **Sample 5** & **Importance** & **Contrast** \\ \hline \(x_{1}\)/Hz & 0.19 & 0.07 & 1 & Sample 5 better \\ \(x_{2}\)/\% & 1.18 & 1.35 & 2 & Sample 3 better \\ \(x_{3}\)/\% & 1.41 & 1.27 & 3 & Sample 5 better \\ \(x_{4}\)/\% & 0.47 & 0.53 & 4 & Sample 3 better \\ \(x_{5}\)/\% & 3.85 & 3.18 & 5 & Sample 5 better \\ \(x_{6}\)/\% & 0.83 & 1.23 & 6 & Sample 3 better \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of samples 3 and 5.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Evaluation Methods** & **Power Quality Evaluation Results** \\ \hline Analytic hierarchy process & Point 4 \(>\) Point 1 \(>\) Point 2 \(>\) Point 5 \(>\) Point 3 \\ Fuzzy theory method & Point 4 \(>\) Point 2 \(>\) Point 1 \(>\) Point 5 \(>\) Point 3 \\ Combined weight method & Point 1 \(>\) Point 4 \(>\) Point 2 \(>\) Point 5 \(>\) Point 3 \\ Traditional TOPSIS method & Point 4 \(>\) Point 1 \(>\) Point 2 \(>\) Point 3 \(>\) Point 5 \\ Method of this paper & Point 4 \(>\) Point 1 \(>\) Point 2 \(>\) Point 5 \(>\) Point 3 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of results of each evaluation method.  
The correlation between the steady state indices of PQ has been the first concern for the comprehensive evaluation results of PQ, which provides a new reference and consideration for the following work of PQ evaluation.  
All of the authors made contributions to the concept and design of the article. [PERSON] is the main author of this work; [PERSON] provided good advice and technical guidance for the manuscript; [PERSON] and [PERSON] reviewed and polished the manuscript.  
This research was funded by Construction of Local Capacity in Shanghai City, Grant No. 14110500900.  
The authors declare no conflict of interest.