Application of self-organising map algorithm for analysis and interpretation of dissolved gases in power transformers

Onset of incipient faults in power transformers can degrade the mineral oil and cellulose insulation, leading to the formation of dissolved gases. The process from oil sampling to quantification of gases is known as dissolved gas analysis (DGA). Despite the availability of DGA interpretation schemes and artificial intelligence (AI) methods for transformer condition monitoring (CM) based on DGA data, it is pointed out in this paper that these approaches are less than ideal and practical in implementation. In view of that, this paper illustrates a novel approach for analysis and interpretation of DGA data, which leads to a more credible CM of power transformers. The proposed approach, which is based on the self-organising map (SOM) algorithm, has been validated using real fault-cases and thereby is proven to be more reliable in portraying the current condition of power transformers.