Transfer of learning in convolutional neural networks for thermal image classification in Electrical Transformer Rooms

Abstract

Overheating of power transformers, low-voltage panels, and medium-voltage components in Electric Transformer Rooms (ETRs) can result from various factors, such as contact issues, irregular loads, and other similar problems. Thermal imaging shows significant potential for detecting faults in power equipment. However, its effectiveness is hindered by the complex thermal patterns of faults and variability in equipment and environmental conditions, making accurate fault detection challenging. This paper aims to study the effectiveness of transfer learning architectures for automating equipment classification in ETRs. This work applies four transfer learning architectures: AlexNet, SqueezeNet, VGG19, and GoogLeNet. The findings of the testing phase demonstrated that the use of transfer learning by fine-tuning pre-trained convolutional neural networks was highly effective in the classification of thermal images captured from ETRs, with the models achieving accuracy rates between 86.98% and 100%, and F 1 -Scores between 86.79% and 100%. • Transfer learning is applied to automate the classification of thermal images of equipment in ETRs. • A thermal imaging dataset is set up and augmented to improve the generalization abilities of proposed models. • Establish standard comparisons among four transfer learning architectures.