1.河南理工大学电气工程与自动化学院 焦作;2.河南省煤矿装备智能检测与控制重点实验室 焦作
In order to characterize the aging trend of IGBT modules in inverter faults and improve the prediction accuracy of the aging process, this paper proposes an IGBT aging prediction model based on improved dung beetle optimizer (IDBO) optimizing the hyper-parameters of bidirectional long-short-term neural network (BiLSTM). Firstly, the time-frequency domain features of Vce.on in the aging process are extracted, and the normalized composite index is constructed by dimensionality reduction using kernel principal component analysis. Secondly, to address the shortcomings of the dung beetle optimizer (DBO), the optimization ability and convergence performance of the DBO are improved by introducing the improved Circle chaotic mapping, Levy flight, and adaptive weighting factors, and the global optimization is achieved by using the IDBO for the hyperparameters of the BiLSTM prediction model. Finally, the effectiveness and superiority of the BiLSTM aging prediction model optimized based on IDBO are verified by actual IGBT degradation data. The results show that the constructed IDBO-BiLSTM model reduces RMSE by 36.42%, MAE by 31.77%, and MAPE by 41.03% on average compared with the BiLSTM model.