Open AccessBayesian information fusion for probabilistic health index of power transformer
Author(s) -
Li Shuaibing,
Ma Hui,
Saha Tapan,
Wu Guangning
Publication year - 2018
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.2017.0582
Subject(s) - probabilistic logic , data mining , bayesian network , inference , computer science , transformer , bayesian inference , bayesian probability , artificial intelligence , principal component analysis , machine learning , engineering , voltage , electrical engineering
This study proposes a Bayesian information fusion approach for determining the probabilistic health index of power transformer. The proposed approach integrates a variety of data obtained from transformer measurements, maintenance records, and failure statistics. By making use of these data, an inference model is constructed using Bayesian belief network (BBN). In the inference model, the significance of each individual measurement on the corresponding component in the BBN is decided by both principal component analysis and expert's experience, and subsequently quantified with a score‐probability transform. Finally, the inference model is used to derive a probabilistic health index. Case studies are provided to demonstrate the applicability of the proposed approach for evaluating transformer condition.