Dynamic State Evaluation Method of Power Transformer Based on Mahalanobis–Taguchi System and Health Index

Yunhe Luo, Xiaosong Zou (), Wei Xiong (), Xufeng Yuan, Kui Xu, Yu Xin and Ruoyu Zhang
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Yunhe Luo: College of Electrical Engineering, Guizhou University, Guiyang 550025, China
Xiaosong Zou: College of Electrical Engineering, Guizhou University, Guiyang 550025, China
Wei Xiong: College of Electrical Engineering, Guizhou University, Guiyang 550025, China
Xufeng Yuan: College of Electrical Engineering, Guizhou University, Guiyang 550025, China
Kui Xu: Electric Power Research Institute of Guizhou Power Grid Co., Ltd., Guiyang 550002, China
Yu Xin: College of Electrical Engineering, Guizhou University, Guiyang 550025, China
Ruoyu Zhang: College of Electrical Engineering, Guizhou University, Guiyang 550025, China

Energies, 2023, vol. 16, issue 6, 1-16

Abstract: Health status assessment is the key link of transformer-condition-based maintenance. The health status assessment method of power transformers mostly adopts the method based on the health index, which has the problems of multiple parameters of each component and strong subjectivity in the selection of weight value, which is easily causes misjudgment. However, the existing online monitoring system for dissolved gas in transformer oil (DGA) can judge the normal or abnormal state of the transformer according to the gas concentration in a monitoring cycle. Still, there are problems, such as fuzzy evaluation results and inaccurate judgment. This paper proposes a dynamic state evaluation method for power transformers based on the Mahalanobis–Taguchi system. First, the oil chromatography online monitoring time series is used to screen key features using the Mahalanobis–Taguchi system to reduce the problem of excessive parameters of each component. Then, a Mahalanobis distance (MD) calculation is introduced to avoid subjectivity in weight selection. The health index (HI) model of a single transformer is built using the MD calculated from all DGA data of a single transformer. Box–Cox transformation and 3 σ criteria determine the alert value and threshold value of all transformer His. Finally, taking two transformers as examples, we verify that the proposed method can reflect the dynamic changes of transformer operation status and give early warning on time, avoiding the subjectivity of parameter and weight selection in the health index, which easily causes misjudgment and other problems and can provide a decision-making basis for transformer condition-based maintenance strategies.

Keywords: transformer; status assessment; oil chromatogram; Mahalanobis distance; health index (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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