Integrated decision-making method for power transformer fault diagnosis via rough set and DS evidence theories

• Published in: IET generation, transmission & distribution transmission & distribution, 2020-12, Vol.14 (24), p.5774-5781
• Main Authors: Xu, Yaoyu, Li, Yuan, Wang, Yijing, Wang, Chen, Zhang, Guanjun
• Format: Article
• Language: English
• Subjects: contradictory information fusion; decision making; decision‐making table; diagnostic transformer rules; DS evidence theories; fault diagnosis; fault type probability; fault‐warning symptoms; integrated decision‐making; multisource monitoring information; power engineering computing; power transformer fault diagnosis; power transformers; probability; probability assignment calculation; rough set; rough set theory; Special Issue: Advanced Data-Analytics for Power System Operation, Control and Enhanced Situational Awareness; transformer monitoring information
• ISSN: 1751-8687; 1751-8695
• DOI: 10.1049/iet-gtd.2020.0552

Precise power transformer fault diagnosis involves incorporating multi-source monitoring information. Uncertain information, missing data, usually occurs in transformer fault cases and diagnosis tasks. To address these challenges, the authors proposed an integrated method of comprehensive transformer fault diagnosis. Diagnostic transformer rules extracted from fault cases form a decision-making table, whereby the main transformer monitoring information and fault types serve as conditional and decision attributes, respectively. Different fault-warning symptoms of the conditional attributes and corresponding decision attributes constitute diagnostic rules. Each obtained symptom in a diagnostic task is evidence supporting different fault types. A modified basic probability assignment (BPA) calculation method is proposed to determine the fault type probability by the obtained symptom. To address contradictory evidence, the symptom significance is introduced to design an improved combination rule incorporating all calculated BPA values to accomplish fault diagnosis. The obtained diagnostic results indicate that more symptoms and a higher symptom significance enable reliable transformer fault diagnosis. The recognition rate of the authors’ method reaches 91.2% with 12–14 symptoms and 94.3% for a 0.9 symptom significance coefficient. It is demonstrated that compared with other combination rules, their method attains a suitable performance (contradiction coefficient K = 0.9 at an 81.3% recognition rate) in realising contradictory information fusion.