A Hybrid Dynamic Thermal Modeling Approach for Tank Temperature Estimation of a 25-MVA ONAF Power Transformer
DOI:
https://doi.org/10.14456/jeit.2026.20Keywords:
Steady-State Thermal Model, Dynamic Thermal Model, Hybrid Dynamic Thermal ModelAbstract
This research presents the development of a hybrid dynamic thermal model for estimating the tank temperature of a 25 MVA power transformer equipped with an Oil Natural Air Forced (ONAF) cooling system. The primary objective is to enhance the accuracy of temperature prediction under time-varying load conditions. The proposed model integrates the principles of steady-state thermal modeling and dynamic thermal modeling, enabling the simultaneous determination of both the final steady-state temperature and the transient thermal response. In the simulation, the ambient temperature was assumed constant at 28 °C, and the computation was performed at 15-minute intervals over a 24-hour period, yielding a total of 96 data points. The load current was varied within a range of 0.3 to 1.8 per unit (p.u.). The comparative results indicate that the hybrid dynamic thermal model more accurately captures the temperature variation trends in accordance with actual operating behavior than either the steady-state or conventional dynamic thermal model. Furthermore, the model performance was validated against the Finite Element Method (FEM). The evaluation was conducted across three load levels conditions: low, medium, and high, with copper losses ranging from 0 to 57.39 kW, consistent with the transformer’s rated loss specifications. The mean absolute percentage error (MAPE) for the steady-state model was found to be 6%, 8%, and 10% for low, medium, and high load conditions, respectively. In comparison, the dynamic thermal model yielded MAPE values of 1.80%, 2.57%, and 3.31%, while the hybrid dynamic thermal model achieved lower errors of 1.24%, 0.66%, and 2.35%, respectively. The results demonstrate that the hybrid dynamic thermal model provides the lowest average temperature estimation error among the considered approaches. This confirms that the proposed model achieves an acceptable level of engineering accuracy and shows strong potential for practical applications in load analysis and real-time transformer temperature monitoring systems.
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