A Temperature Monitoring Method For Power Electronic Converter Based on Infrared Image and Object Detection Algorithm

Abstract

Power electronic converters are more and more widely used, and abnormal temperature of converter components is the most important factor in converter failure. To improve the reliability of the converter design, it is necessary to monitor the temperature of key components in the converter during the prototype test stage. The temperature measurement method of infrared thermal images has rich temperature information, wide detection range, and does not affect the original circuit design. However, in the current automatic temperature measurement methods, it is necessary to manually establish a standard matching template for the infrared thermal image of the circuit to be tested, which indicates a large workload and poor versatility. This paper proposes a method for fully automatic temperature monitoring of converter components. This method is based on a deep learning object detection algorithm, which can automatically identify the type of converter components, obtain partial infrared thermal images of components through heterogeneous image registration, achieve accurate component temperature monitoring and facilitate the converter state monitoring and fault detection. The advantages of this method are: 1) there is no need to manually establish a standard template for each converter; 2) it can monitor the converter temperature without manual intervention; 3) combining the temperature information and circuit prior knowledge, the state monitoring and fault diagnosis can further be realized. The experimental results also verify the feasibility and accuracy of this method.