Data-Driven vs Traditional Approaches to Power Transformer's Top-Oil Temperature Estimation

TL;DR

This paper compares machine learning models like ANNs, TiDE, and TCN to traditional IEC standards for predicting power transformer top-oil temperatures, demonstrating improved accuracy and reliability through quantile regression.

Contribution

It introduces data-driven machine learning approaches for transformer temperature prediction that outperform standard IEC models and incorporates quantile regression for uncertainty estimation.

Findings

ML models outperform IEC standard in accuracy

Quantile regression provides reliable temperature prediction intervals

Best model estimates conditional quantiles with sufficient coverage

Abstract

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.