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Deep Learning Method and Infrared Imaging as a Tool for Transformer Faults Detection

In behaviour recognition, the development of the Deep Learning (DL) method introduced massive improvements in the field of artificial intelligence, where DL represents an upgrade of the present artificial neural network architecture (ANN). Deep Learning as a comprehensive new field of artificial intelligence completely covers the neural networks architecture that is devised to carry out certain forms of identification, such as behaviour, forms of things, trends, similarities in complex forms, etc. Regarding thermography in energy, the cases used to illustrate this are photographs of active energy components in the plant. Failures that are seen with thermography cannot be recognized by other methods. However, an expert needs to do segmentation of focusing and classification of failures. The need for daily sampling and expert work is growing. With the DL method, it can be done in real time any time. One of the popular network architectures for using DL in image analysis is the recognition algorithm – convolution neural network (CNN). Traditional artificial intelligence methods require determining factors and computations, leading to training algorithm. Machine learning has important features as well as the right weight to make decisions about new input data. This work presents DL as a flexible and adaptive method for the analysis of thermal images of energy facilities, as well as a tool used for the construction and implementation of an efficient fault analysis on the 10/0.4 kV service transformer.

Deep Learning, electric components, transformers, infrared imaging, photograph analysis, Convolution Neural Network

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