Research on IGBT aging prediction method based on adaptive VMD decomposition and GRU-AT model

The insulated gate bipolar transistor (IGBT) is widely used in the power electronic system, but its aging state is difficult to predict in advance due to the complicated failure mechanism, which will affect the performance of the equipment, and even cause serious disaster. Therefore, we propose a combined prediction model based on adaptive VMD decomposition (AVMD) and attention-based gated recurrent unit (GRU-AT) to achieve accurate prediction of the aging state of IGBT. For the nonlinear characteristics of the IGBT failure characteristic parameters, AVMD with the key parameters optimized by improved sparrow search algorithm (ISSA) was used to disaggregate the character sequence to a series of finite wide subcomponents, which overcome the interference of the irregularity of aging time sequence data to the prediction accuracy. Secondly, the attention mechanism is introduced to capture the temporal feature relationship between the current moment output and the historical degraded data, further improving the generalization ability of the model. Finally, the GRU-AT network is used to model each wide modal sub-component independently, and final prediction results of IGBT aging parameters are obtained by superimposing the prediction results of each mode. The experimental results indicate that the AVMD-GRU-AT model designed in this article has superior prediction performance in both over-the-top single-step prediction and multi-step prediction of aging state of IGBT