Research on IGBT life prediction based on SMA-Elman

The insulated gate bipolar transistor (IGBT) is an important part of power converter, and the prediction of its remaining service life is very important. In response to the remaining service life of IGBT, the method of optimizing the adaptive options of the Elman neural network implementation of the Elman neural network is optimized by using the slime mould algorithm (SMA), and it is used for the life prediction of IGBT. Firstly, the peak of the gate emitter turn-off voltage in the aging test data set of NASA research center is smoothed. Secondly, the time domain feature is extracted from the processed data. Thirdly, the kernel principle component analysis (KPCA) is used for optimization dimensionality reduction. Finally, the SMA-Elman neural network model is used to predict the lifetime of IGBT. The results show that the proposed SMA-Elman neural network has better performance than Elman and BP neural network and SVR, the mean square error is 0.021%, the root mean square error is 0.014, the fitting degree is 0.998, and it can better predict the remaining service life of IGBT.