Abstract: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.

Abstract:In order to improve the measurement accuracy of dynamic weighing and realize realtime monitoring and fine management of intelligent pasture, a dynamic weighing algorithm based on chaotic sparrow search algorithm to optimize LSTM neural network is proposed. The data is collected by the dynamic weighing platform, and the Kalman filter algorithm is used to process the interference data. The CSSA-LSTM neural network model is established by using the Tent mapping strategy and the sparrow search algorithm after Gaussian mutation to optimize the parameters of the LSTM neural network. The results show that the average absolute percentage error of CSSA-LSTM neural network is within 1.5%, the average absolute error is reduced by 0.874, and the root mean square error is reduced by 1.115 3. The comparative experiments show that the hybrid algorithm has the smallest prediction error and effectively improves the measurement accuracy of dynamic weighing.