Open AccessInsulation condition ranking of transformers through principal component analysis and analytic hierarchy process
Author(s) -
Tee ShengJi,
Liu Qiang,
Wang Zhongdong
Publication year - 2017
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.2016.0589
Subject(s) - analytic hierarchy process , transformer , principal component analysis , reliability engineering , asset management , computer science , data mining , test data , ranking (information retrieval) , engineering , operations research , artificial intelligence , electrical engineering , voltage , finance , economics , programming language
Interpretation of oil test data for transformer insulation condition is essential towards justifying asset management practices. Traditionally, an empirical formula (EF) is used by asset managers. This study introduces principal component analysis (PCA) and analytic hierarchy process (AHP) as two alternatives. Through the use of an oil test dataset consisting of 39 in‐service UK transmission transformers measured for multiple ageing related parameters, PCA demonstrated its potential in working directly with data to explore parameter relations as well as ranking transformers according to their conditions. AHP on the other hand presented a way to coherently aggregate criteria in a flexible hierarchical setup for identifying the weightages of the oil test parameters before interpretation of measurements. The interpreted conditions based on PCA and AHP, along with a track‐record proven EF are similar, particularly for transformers at extreme ends of the insulation condition.