Abstract
This article presents an algorithm for modelling an Adaptive Neuro Fuzzy Inference System (ANFIS) for power transformer paper conditions in order to estimate the transformer’s expected life. The dielectric characteristics, dissolved gasses, and furfural of 108 running transformers were collected, which were divided into 76 training datasets and another 32 testing datasets. The degree of polymerization (DP) of the transformer paper was predicted using the ANFIS model based on using the dielectric characteristics and dissolved gases as input. These inputs were analyzed, and the best combination was selected, whereas CO + CO2, acidity, interfacial tension, and color were correlated with the paper’s deterioration condition and were chosen as the input variables. The best combination of input variables and membership function was selected to build the optimal ANFIS model, which was then compared and evaluated. The proposed ANFIS model has 89.07% training accuracy and 85.75% testing accuracy and was applied to a transformer paper insulation assessment and an estimation of the expected life of four Indonesian transformers for which furfural data is unavailable. This proposed algorithm can be used as a furfural alternative for the general assessment of transformer paper conditions and the estimation of expected life and provides a helpful assistance for experts in transformer condition assessment.
Highlights
A power transformer is a piece of equipment that is designed for years of usage and generally has good reliability, with a life expectancy up to 40 years or more
From transformed degree of polymerization (DP) based on furfural data collected, a correlation analysis was performed on each of the dielectric characteristics, as shown in the Table 1
An algorithm for building an Adaptive Neuro Fuzzy Inference System (ANFIS) model for power transformer paper condition and expected life estimation is presented in this article
Summary
A power transformer is a piece of equipment that is designed for years of usage and generally has good reliability, with a life expectancy up to 40 years or more. The condition of the transformer naturally decreases as it is operated because of the processes of aging. It may become damaged faster than normal. The aging agents such as oxygen, heat, and moisture will cause acid formation and other materials that negatively impact cellulose insulation. These transformers may become unreliable and incapable of functioning as expected. A good transformer assessment will give better insights on the condition of the transformer for maintenance purposes This will result in better power transfer efficiency because of the smaller loss of opportunity resulting from power outages
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