Frequency Response Analysis (FRA) is a proven method for detecting mechanical faults in transformers, such as winding deformations and short circuits. However, traditional FRA interpretation relies heavily on visual and subjective comparison of frequency response curves, which can introduce human bias and lead to inconsistent results. Integrating Machine Learning (ML) with FRA can significantly enhance fault diagnosis by automatically identifying complex patterns within the data that are difficult to detect using through human analysis. This integration can automate diagnostics, enhance accuracy, improve predictive maintenance, reduce reliance on expert interpretation and curtail operational costs. This paper reviews the application of FRA and ML alongside complementary techniques for transformer winding health assessment.

The power transformer is a critical component in any transmission and distribution grid. This vital machine faces new thermal stresses arising from challenges related to energy transition along with the ever-increasing load. Understanding and predicting transformer thermal behavior is fundamental to optimizing operation and maintenance, and consequently ensuring the system’s reliability. Transformer thermal modeling (TTM) has garnered significant attention among engineers and researchers. Various approaches to TTM exist, including physical, semi-physical, physical-based numerical, and artificial intelligence (AI)-based models, with the latter being relatively unexplored in the literature. This contribution presents a comprehensive review of AI-based applications for transformer thermal modeling, examining commonly used techniques, inputs, and outputs. Perspectives in the field are discussed, with a focus on gray-box and adaptive models. The impacts of AI-based models in developing digital transformer twins are also explored. Prominent models in TTM include artificial neural networks and fuzzy systems, with support vector regression also featuring among the techniques utilized. Load and ambient temperature are primary inputs in top-oil temperature predictions, while top-oil temperature is crucial for hot-spot temperature predictions. Incorporating historical data is increasingly common in both cases. This review serves as a guide for researchers interested in TTM and highlights perspectives for future developments. AI-based applications offer powerful tools for TTM and, despite present challenges, hold significant potential for transformation in the field.

Transformer top-oil temperature prediction is essential for reliable thermal assessment and lifetime management. Although the IEC 60076-7 loading guide model is widely adopted for this purpose, its thermal parameters are generally assumed to remain constant regardless of environmental conditions: an assumption that has received limited attention. This paper investigates the influence of ambient temperature on the IEC top-oil model parameters and proposes an adaptive ambient-dependent parameterization framework. A segmented optimization methodology is applied to field measurements from a 66 MVA, 225/26.4 kV power transformer to identify the optimal rated top-oil temperature rise and thermal exponent x over different ambient temperatures. The results reveal an approximately linear dependence of the optimized parameters on ambient temperature, suggesting that the thermal dynamics represented by the IEC model evolve with environmental conditions. Incorporating this dependency improves prediction accuracy, achieving a mean absolute error (MAE) of 2.03 °C and a root mean squared error (RMSE) of 2.56 °C on the test dataset, outperforming both the conventional IEC model with fixed parameters and a previously published adaptive approach. These findings offer a practical extension of the IEC model with applications in transformer monitoring, dynamic loading, and digital-twin-based asset management.