A Risk Assessment Method of Power Transformer Based on Three-Parameter Interval Grey Number Decision-Making

• Published in: Applied sciences Vol. 12; no. 7; p. 3480
• Main Authors: Yu, Hongbo, Xiong, Wei, Xu, Kui, Yu, Yunwen, Yuan, Xufeng, Zou, Xiaosong, Xiao, Ling
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.04.2022
• Subjects: DC cut load model; Decision making; Electric power grids; Load distribution; Methods; Parameters; power transformer; Probability distribution; Random variables; Risk assessment; three-parameter interval grey number; three-point estimation method; Uncertainty
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app12073480

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.