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 of converter failure. In order 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 coverage, and does not affect the original circuit design. It is widely used in circuit temperature measurement occasions. 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 target detection algorithm, which can automatically identify the type of converter components, obtain partial infrared thermal images of components through heterogeneous image registration, and achieve accurate component temperature monitoring. The advantages of this method are: 1) there is no need to manually establish a standard template for each converter, and the versatility is good; 2) it is fully automatically when the monitoring the temperature of converter components without manual intervention; 3) it is easy to implement and promote due to low cost of hardware system. Finally, the experimental results verify the feasibility and accuracy of this method.