Open AccessIntegrated decision‐making method for power transformer fault diagnosis via rough set and DS evidence theories
Author(s) -
Xu Yaoyu,
Li Yuan,
Wang Yijing,
Wang Chen,
Zhang Guanjun
Publication year - 2020
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2020.0552
Subject(s) - transformer , computer science , data mining , fault (geology) , reliability engineering , dissolved gas analysis , artificial intelligence , pattern recognition (psychology) , engineering , voltage , transformer oil , seismology , geology , electrical engineering
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.