Fuzzy clustering approach to access the ageing of transformer insulation oil

The present paper introduces Subtractive clustering method for identifying the ageing of insulation oil of power transformers. This work handles the problem with complex dimensions to an efficient and accurate dimensional area. Feature selection for clustering is done by extracting values from Spectroscopy absorbance curves of various oil samples. Feature validation is done by designing a fuzzy inference engine; the results obtained from fuzzy inference engine are then compared with practical data to enhance/ensure the validity and found that these results were matched with practical data. These features are now plotted in a space as training data samples and further Subtractive clustering algorithm is applied for different radius values to generate an efficient inference engine. The oil groups viz new age, medium age and old age samples are identified and correspondingly centers have been plotted for each group. Verification for this work is done practically in the laboratory and has been found that the proposed methodology is efficient in diagnosing the ageing of insulation oil.