RESISTO Project: Automatic detection of operation temperature anomalies for power electric transformers using thermal imaging

TL;DR

This paper presents a thermal imaging-based monitoring system with an adaptive prediction model for early detection of temperature anomalies in power transformers, aiming to improve grid reliability and prevent failures.

Contribution

Introduction of a real-time thermal monitoring system with an online learning prediction model for transformer temperature anomaly detection.

Findings

System effectively predicts thermal anomalies in transformers.

Real-time thermal surveillance enhances early fault detection.

Synthetic data testing shows promising long-term performance.

Abstract

The RESISTO project represents a pioneering initiative in Europe aimed at enhancing the resilience of the power grid through the integration of advanced technologies. This includes artificial intelligence and thermal surveillance systems to mitigate the impact of extreme meteorological phenomena. RESISTO endeavors to predict, prevent, detect, and recover from weather-related incidents, ultimately enhancing the quality of service provided and ensuring grid stability and efficiency in the face of evolving climate challenges. In this study, we introduce one of the fundamental pillars of the project: a monitoring system for the operating temperature of different regions within power transformers, aiming to detect and alert early on potential thermal anomalies. To achieve this, a distributed system of thermal cameras for real-time temperature monitoring has been deployed in The Do\~nana National Park, alongside servers responsible for the storing, analyzing, and alerting of any potential thermal anomalies. An adaptive prediction model was developed for temperature forecasting, which learns online from the newly available data. In order to test the long-term performance of the proposed solution, we generated a synthetic temperature database for the whole of the year 2022. Overall, the proposed system exhibits promising capabilities in predicting and detecting thermal anomalies in power electric transformers, showcasing potential applications in enhancing grid reliability and preventing equipment failures.