## 7 Conclusions  
Given that the risk assessment method of power transformers mainly focuses on the certain decision-making method with the equipment fault probability as the main index, and that the lack of consideration of the influence of the power grid operation loss due to uncertainty leads to the deviation of its risk judgment, a power transformer risk assessment method based on the three-parameter interval gray number decision-making was proposed, and the calculation and analysis were carried out in MATLAB simulation software. The following conclusions can be obtained:  
1. Considering that the loss degree is influenced by uncertain factors, the probabilistic power flow model based on the three-point estimation method calculates loss degree probability distribution and uses the interval numbers to characterize its uncertainty, which avoids the deviation of risk judgment caused by the difficulty of the accurate quantification of the loss degree index.  
2. The three-parameter interval gray number decision-making is introduced into the risk assessment of power transformers considering uncertainty, which realizes the full use of the equipment risk value interval number to determine the risk and avoids the decision-making defects of the traditional certain risk quantification method.  
3. The influence of different factors on the risk assessment results is discussed, which is in line with the actual operation condition of the transformer.  
To sum up, compared with traditional certain quantitative risk assessment methods, the proposed method can effectively and accurately evaluate the transformer risk condition, which provides a new judgment idea for power grid equipment quantitative risk assessment.  
The proposed method in this paper considers the influence of uncertainty on the risk judgment of the transformer, but only the loss degree of the three risk indexes is characterized by interval number, whereas other indexes are still quantified by certain  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test Object** & **Test Group** & **Access Positions** & **Risk Value (10,000 RMB)** & **Evaluation Result** \\ \hline \multirow{3}{*}{Transformer 1} & \multirow{3}{*}{First group} & 11, 13 & [52.29, 74.89, 97.50] & Medium risk \\  & & 5, 9 & [44.44, 69.85, 93.26] & Medium risk \\  & & 11, 9 & [48.80, 69.39, 89.98] & Medium risk \\ \hline \multirow{3}{*}{Transformer 1} & \multirow{3}{*}{Second group} & 11, 13 & [65.19, 94.19, 123.18] & Overall large risk \\  & & 5, 9 & [56.86, 88.32, 119.77] & Medium risk \\  & & 11, 9 & [61.11, 87.73, 114.35] & Medium risk \\ \hline \hline \end{tabular}
\end{table}
Table 15: Comparison of evaluation results of different wind turbines access positions.  
values. How to reasonably convert equipment fault probability and equipment importance into interval numbers and judge the risk of the transformer according to the interval numbers of the three indexes still needs to be studied further.  
Formal analysis, H.Y.; investigation, W.X. and K.X.; supervision, Y.Y. and X.Y.; methodology, X.Z. and L.X. All authors have read and agreed to the published version of the manuscript.  
This work was supported by National Natural Science Foundation of China (NSFC) (52067004). This work was supported by Science and Technology Foundation of Guizhou Province ([2019]1128, [2019]1058).  
The authors declare no conflict of interest.