Prediction of transformer fault in cooling system using combining advanced thermal model and thermography

Abstract

Abstract Thermal models are widely used for diagnosing thermal faults, predicting the thermal behaviour, estimating hot spots, and evaluating the loading capacity. This paper uses a new advanced thermal model, thermography method, and computational fluid dynamic separately to obtain the top‐oil and radiator temperatures. The model consists of four thermal points: the ambient, top‐oil, winding, and radiator. The heat transfer phenomena between these points are modelled by nonlinear thermal resistors. Adding the radiator thermal point to the model improves the accuracy. This is shown by comparing the proposed model with the standard and physical thermal models. The proposed thermal model is validated using the experimental results of three distribution transformers. The thermography camera and image processing technique are also employed to take thermal images and analyse them, respectively. By analysing the thermal images with the image processing method, the top‐oil temperature and radiator temperature are obtained. Furthermore, the computational fluid dynamic model is used to analyse and obtain more accurate prediction of the thermal behaviour of the transformer. Finally, it is proposed to detect faults inside the transformer cooling system by comparing the top‐oil and radiator temperature from two methods presented, that is, advanced thermal modelling and thermography with image processing.

Keywords

• Optical, image and video signal processing
• Image sensors
• Thermal variables measurement
• Computer vision and image processing techniques
• Power engineering computing
• Transformers and reactors