Modification of weight value identification in transformer health index assessment model

Transformer is one of the most important components in the power system. Fault that occurs in transformer may result in power supply interruption which will also cause environmental damage, revenue losses and disruption in end users activities. Utility companies have adopting transformer asset management (TAM) with the aid of transformer health index (THI) to examine the health condition of the transformer. Over the years, the weight value for components and parameters in THI assessment model are determined by subjective judgment of transformers’ expert. Presently, general research on mathematical weight value identification is still very much in its infancy. Therefore, a substantial study on weight value identification by proper mathematical approach is essential in order to justify the use of THI assessment model for assessing health condition of transformers. Seven previous THI assessment models, which depending on subjective judgment of transformer’s expert were reviewed and named as THI Assessment Models 1 to 7. However, due to assumptions used and unknown references on certain parameters of THI Assessment Models 3 to 7, only THI Assessment Models 1 and 2 were used as references to be compared to the two proposed assessment models, named as THI Assessment Models A and B. It is demonstrated that THI determination in the proposed THI Assessments Model A and B are easier to be implemented compared to THI Assessment Models 1 and 2. This is due to the reason that direct mathematical steps are employed in the proposed THI Assessment Models A and B, whereas THI Assessment Models 1 and 2 are solely based on experts’ judgment and prediction. Therefore, the proposed models are more reliable and do not have to deal with arguments from different experts. The parameters chosen in this study are Dissolved Gas Analysis (DGA) and Oil Quality Analysis (OQA) where basic equation of THI was used in the model. In the proposed THI Assessment Models A and B, the score value used was in between ‘0’ to ‘100’ and the weight values for components and parameters were determined using Entropy Weight Method (EWM). The calculations of the proposed THI Assessment Models A and B were compared to the THI Assessment Models 1 and 2 using three data populations, where Data Population 1 involve 30 transformers, Data Population 2 involve five transformer and Data Population 3 involve 73 transformers. The results revealed that THI values and transformer condition in THI Assessments Models A and B are in good agreement with THI Assessment Model 2 and contradicted to the THI Assessment Model 1. However, the relationship between THI values with age for THI Assessment Models A and B agree relatively well with both THI Assessment Models 1 and 2. Furthermore, there is strong linear correlation between THI Assessment Models A and B with THI Assessment Models 1 and 2. The results of this study clearly support that the new proposed THI assessment models are more reliable to be used to obtain THI value, compared to the existing THI assessment models which solely depending on the judgment of transformers’ expert.

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