Enhanced Simulation Technique for Hot Spot Temperature Modelling under Dynamic Climate Conditions | Chapter 9 | New Horizons of Science, Technology and Culture Vol. 3

 

The Hot-Spot Temperature (HST) of transformer windings is a vital diagnostic parameter used to assess the thermal ageing and overall health condition of power transformers. As the hottest point in the winding insulation system, the HST largely determines the rate at which insulation deteriorates over time. Prolonged exposure to elevated HST levels can lead to significant reductions in insulation lifespan, increasing the risk of transformer malfunction or failure. Therefore, accurately modelling and monitoring the HST is crucial for asset management, preventive maintenance, and ensuring long-term transformer reliability. Traditionally, thermal models used to estimate HST rely on simplified assumptions and steady-state conditions, typically neglecting the real-time influence of environmental factors. These include short-term variations in ambient temperature, fluctuations in wind speed, and changes in solar radiation levels, all of which significantly impact a transformer's cooling capacity and internal temperature distribution. The absence of such dynamic inputs in conventional models results in the underestimation of the actual thermal stress experienced by transformers during transient or extreme climate events, such as heatwaves or rapid weather shifts. To overcome these limitations, this study proposes an enhanced simulation framework that integrates both physical transformer behaviour and statistical modelling techniques to more accurately predict the HST under dynamic operating conditions. The framework considers essential external parameters—ambient temperature, solar irradiance, and wind velocity—that affect the transformer’s heat dissipation characteristics. Internally, it processes real-time transformer loading data, thermal time constants, and historical operating profiles to derive more realistic estimates of both the Top-Oil Temperature (TOT) and the resulting HST rise. One of the key innovations in the proposed method is the development of a hybrid physical–statistical model. While the physical model applies established transformer thermal equations to simulate heat generation and transfer, the statistical component corrects for deviations due to environmental variability using regression-based estimation or adaptive response curves. This dual-layered approach compensates for the superposed inaccuracies found in traditional models, especially under non-steady climatic conditions. As a result, the model delivers improved accuracy and responsiveness when simulating transformer temperature dynamics in real-world scenarios.

 

The entire simulation framework is developed and executed in MATLAB/Simulink, which allows for modular implementation and high configurability. The model’s architecture comprises subsystems that handle specific functionalities, such as real-time environmental input acquisition, transformer loss calculations, TOT estimation, and HST prediction. A feedback mechanism is embedded into the system to utilise delayed TOT values, providing better stability and reflecting the thermal inertia of the transformer components. This structure ensures that the simulation remains responsive to real-time changes in input data while maintaining computational efficiency and scalability for utility-scale applications. Furthermore, the framework supports the integration of sensor-based and IoT-enabled data streams, enabling continuous monitoring and live simulation of transformer performance. This feature is particularly beneficial for utilities aiming to implement predictive maintenance strategies and reduce unplanned outages. The model can be calibrated using field data from operating transformers, ensuring its adaptability across various transformer types and geographic locations. In summary, this work presents a comprehensive and practical solution for transformer hot-spot temperature modelling under dynamic climate conditions. By merging physical and statistical modelling within a modular simulation platform, the approach bridges the gap between theoretical prediction and real-world performance. The model not only enhances the accuracy of HST estimation but also empowers asset managers with reliable thermal indicators that support informed operational and maintenance decisions. This makes it a valuable tool in modern smart grid environments, where climate variability and load diversity are critical challenges in transformer lifecycle management.

 

 

Author(s) Details

Srinivasan. M
Department of Electrical and Electronics Engineering, Rajeev Institute of Technology, Hassan, Karnataka - 573201, India.

 

Pradeep K G M
Department of Electrical and Electronics Engineering, Rajeev Institute of Technology, Hassan, Karnataka - 573201, India.

 

 

Please see the book here:- https://doi.org/10.9734/bpi/nhstc/v3/5706