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

TL;DR

A monitoring system for the operating temperature of different regions within power transformers, aiming to detect and alert early on potential thermal anomalies in power electric transformers, showcasing potential applications in enhancing grid reliability and preventing equipment failures.

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 Doñana 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.

Figures (7)

• Figure 1: Simplified diagram of the thermographic data acquisition system.
• Figure 2: Raw thermographic images obtained by DS-2TD2137T-4/P cameras in eight randomly selected power transformation centers.
• Figure 3: Flowchart of the proposed thermal anomaly detection system.
• Figure 4: Manually-defined masks and automatic segmentation results. (A) Twelve examples of manually-defined segmentation masks are displayed. (B) Five distinct regions automatically detected by Otsu's algorithm. (C) The temperature histogram is depicted alongside the four thresholds used to delineate these five regions. Finally, (D) depicts the application of the MSER algorithm to each previously identified region, facilitating the identification of spatially separated subregions of equal intensity and resulting in a total of 12 final regions.
• Figure 5: Synthetic data generation. (A) This figure displays the fifth-order polynomial curves associated with each region that have been fitted to the actual temperature data model. These curves are subsequently used to extrapolate the transformer's operational curves throughout the entire year. (B) Standard Error of the Mean (SEM) of the 365 synthetic daily curves generated. (C and D) Thermal anomalies generated according to the mathematical model of the thermal system designed for the transformer.