Insulation condition ranking of transformers through principal component analysis and analytic hierarchy process

Abstract

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.