Abstract:
Transformer performance and efficiency can be enhanced by effectively address the properties of its
insulation system. The power transformer insulation system weakens as a result of operational thermal stresses
brought on by dynamic loading and shifting environmental patterns. Winding hot spot temperature is a crucial
metric that must be maintained below the prescribed limit while power transformers are operating so as to
maintained power system reliability. This is due to the fact that, among other variables, the time-dependent aging
effect of insulation depends on transitions in hot spot temperatures. Due to the non‐linear nature of the
conventional mathematical models used to determine these temperatures, and complexity of thermal
phenomena, investigations still need to be exercised to fully understand the variables that associate with hot spot
temperature computation with minimum error. This paper explores the possibilities of enhancing top oil and hot
spot temperature estimation accuracy through the use of an adaptive neuro-fuzzy inference (ANFIS) technique.
The paper presents an adaptive neuro fuzzy model to approximate the hot spot temperature of a mineral oilfilled power transformer based on loading, and established top oil temperature. Initially, a sub-ANFIS top oil
temperature estimation model based on loading and ambient temperature as inputs is established. Using a hybrid
optimization technique, the ANFIS membership functions were fine-tuned throughout the training process to
reduce the difference between the actual and anticipated outcomes. The correctness and reliability of the created
adaptive neural fuzzy model have been verified using real-world field data from a 60/90MVA, 132kV power
transformers under dynamic operating regimes. The ANFIS model results were validated against field measured
values and literature-based electrical-thermal analogous models, establishing a precise input-output correlation.
The developed ANFIS model achieves the highest coefficient of determination for both TOT and HST (0.98 and
0.96) and the lowest mean square error (7.8 and 10.3) among the compared thermal models. Correct
determination of HST can help asset managers in thermal analysis trending of the in-service transformers,
helping them to make proper loading recommendations for safeguarding the asset.
