Machine learning outperforms decades-old transformer safety models
Researchers have shown that AI-based time-series forecasting significantly improves predictions of power transformer temperatures compared to standards used since the 1980s. Better temperature forecasting could reduce unexpected transformer failures and extend equipment lifespan—cutting maintenance costs for utilities managing aging electrical grids.
Originaltitel: Data-driven vs traditional approaches to power transformer’s top-oil temperature estimation
• Comparative study of data-driven and traditional transformer thermal models. • Time-series models applied to real transformer operational data. • Machine learning improves top-oil temperature prediction accuracy. • Quantile regression estimates uncertainty bounds of predictions. • Robust performance under varying load and ambient temperature conditions. Power transformers are subjected to electrical currents and temperature fluctuations that, if not properly controlled, can lead to major deterioration of their insulation system. Therefore, monitoring the temperature of a power transformer is fundamental to ensure a long-term operational life. Models presented in the IEC 60076-7 and IEEE standards, for example, monitor the temperature by calculating the top-oil and the hot-spot temperatures. However, these models are not very accurate and rely on the power transformers’ properties. This paper focuses on finding an alternative method to predict the top-oil temperatures given previous measurements. Given the large quantities of data available, machine learning methods for time series forecasting are analyzed and compared to the real measurements and the corresponding prediction of the IEC standard. The methods tested are Artificial Neural Networks (ANNs), Time-series Dense Encoder (TiDE), and Temporal Convolutional Networks (TCN) using different combinations of historical measurements. Each of these methods outperformed the IEC 60076-7 model and they are extended to estimate the temperature rise over ambient. To enhance prediction reliability, we explore the application of quantile regression to construct prediction intervals for the expected top-oil temperature ranges. The best-performing model successfully estimates conditional quantiles that provide sufficient coverage.