Citation: Yu, H.; Xiong, W.; Xu, K.;
Yu, Y.; Yuan, X.; Zou, X.; Xiao, L. A
Risk Assessment Method of Power
Transformer Based on
Three-Parameter Interval Grey
Number Decision-Making. Appl. Sci.
2022, 12, 3480. https://doi.org/
10.3390/app12073480
Academic Editors: Pierluigi Siano,
Hassan Haes Alhelou and Amer
Al-Hinai
Received: 8 March 2022
Accepted: 28 March 2022
Published: 29 March 2022
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Article
A Risk Assessment Method of Power Transformer Based on
Three-Parameter Interval Grey Number Decision-Making
Hongbo Yu
1
, Wei Xiong
1,
*, Kui Xu
2
, Yunwen Yu
1
, Xufeng Yuan
1
, Xiaosong Zou
1
and Ling Xiao
2
1
College of Electrical Engineering, Guizhou University, Guiyang 550025, China;
yuhongboyyyy@163.com (H.Y.); yuyunwen5741@163.com (Y.Y.); ee.xfyuan@gzu.edu.cn (X.Y.);
zouxs@mail.xjtu.edu.cn (X.Z.)
2
Guizhou Power Grid Co., Ltd., Guiyang 550002, China; zlh2681010814@163.com (K.X.);
lyh187958078682022@163.com (L.X.)
* Correspondence: wxiong@gzu.edu.cn
Abstract:
In the process of power transformer risk assessment, the loss degree index is difficult to
accurately quantify due to the influence of uncertain factors, leading to the deviation of risk judgment.
A power transformer risk assessment method based on the three-parameter interval grey number
decision-making is proposed. Firstly, the fault probability of the transformer is quantified based
on the condition evaluation results. Secondly, considering the uncertainty of DG output and load,
the Nataf transform and Cholesky decomposition were used to eliminate the correlation of random
variables, and a three-point estimation method combined with a DC cut load model was introduced
to calculate the probability distribution of the loss degree caused by the transformer fault. Finally,
the origin moment of each order was obtained based on the calculation formula of risk value, and
the risk probability distribution was obtained through the Cornish–Fisher series expanding. The
decision method of the three-parameter interval grey number distance measure was used to judge
the risk grade of the equipment. The results show that the proposed method fully considers the
influence of uncertainty on equipment risk judgment, can realize the full use of the equipment risk
value interval number to judge the risk, and avoids the decision-making defects of the traditional
certain risk quantification method. Meanwhile, the influence of different factors on the risk evaluation
results is in line with the actual operation condition of the transformer. The results also verify the
effectiveness and accuracy of the proposed method, which provides a new judgment idea for power
grid equipment risk quantitative assessment.
Keywords:
power transformer; risk assessment; three-point estimation method; DC cut load model;
three-parameter interval grey number
1. Introduction
With a high proportion of new energy access [
1
,
2
], the strong uncertainty and volatility
of the power grid will lead to more extreme and drastic operating conditions for power
equipment, and higher requirements are put forward for the safe and reliable operation
of power equipment [
3
–
5
]. However, the power transformer is one of the key pieces of
equipment in the power grid. How to accurately and effectively evaluate its risk grade
not only provides a basis for the selection of equipment maintenance strategy but also has
important significance for maintaining the stable operation of the system.
At present, the research on risk assessment methods for power transformers mainly
focuses on certain decision-making methods, with the equipment fault probability as the
main index. For example, based on fault tree analysis, a risk assessment model of the
power transformer was constructed, and the overall risk of the equipment is obtained
mainly from the probability of occurrence of each component. However, the risk factors
considered are too individual, and cannot fully reflect the actual risk influence condition
of the equipment [
6
,
7
]. A risk assessment model based on a health index was established
Appl. Sci. 2022, 12, 3480. https://doi.org/10.3390/app12073480 https://www.mdpi.com/journal/applsci
