IGBT Junction Temperature Monitoring and State Identification Based on AWG-NN

Precise monitoring of insulated gate bipolar transistors (IGBTs) junction temperature, which impacts device lifespan, is crucial for assessing their health and operational status. However, traditional monitoring methods are limited, and effective IGBT state assessment methods are lacking. This article presents a novel IGBT junction temperature monitoring and state identification method leveraging the synergy between arrayed waveguide grating (AWG) demodulation and neural networks (NNs). We first develop a comprehensive thermal simulation model of the IGBT to investigate temperature distribution, thermal stress, and potential weak points under diverse operating conditions. The junction temperature is calculated from the reflected wavelength data obtained by waveguide Bragg grating (WBG) sensors, while AWG enables high-precision, multichannel temperature monitoring through wavelength demodulation. For state identification, an attention-enhanced Siamese network (Siam-MMSA) is proposed. By designing a spatial micro-macro spatial attention (MMSA), integrating microscopic and macroscopic perspectives, this model leverages the attention mechanism to extract relationships between temperature features and aging status. Experimental results demonstrate that this method accurately captures dynamic junction temperature variations with an error margin below $0.6~^{\circ }$ C and achieves a 99.5% accuracy rate in IGBT state identification under testing conditions. This approach supports intelligent control, performance assessment, and lifespan optimization for power electronic devices, offering valuable engineering insights for real-world applications.