Aluminum electrolytic capacitor (AEC) is one of the most pivotal components that affect the reliability of power electronic systems. The electrolyte evaporation and dielectric degradation are the two main reasons for the parametric degradation of AEC. Remaining useful life (RUL) prediction for AEC is beneficial for obtaining the health state in advance and making reasonable maintenance strategies before the system suffers shutdown malfunction, which can increase the reliability and safety. In this paper, a hybrid machine learning (ML) model with GRU and PSO-SVR is proposed to realize the RUL prediction of AEC. The GRU is used for the recursive multi-step prediction of AEC to model the times series of AEC, SVR optimized by PSO for hyper-parameters is applied for error compensation caused by recursive GRU. Finally, the proposed model is validated by two kinds of data sets with accelerated degradation experiments. Compared with the other methods, the results show that the proposed scheme can obtain greater prediction performance index of RUL under different prediction time points, which can support the technology of health management for power electronic system.

No abstract

Aluminum electrolytic capacitors (AECs) get multiple superior functions such as filtering, energy storage and decoupling, which have a great effect on the performance and lifetime for power converters. Therefore, analyzing and predicting the faults of Aluminum electrolytic capacitors (AECs) is conducive to improve the safety and reliability of the power converters. In order to establish the AECs' fault prediction model and improve the accuracy, an integrated model based on complete ensemble empirical mode decomposition with adaptive noise, grey wolf optimization algorithm and regularized extreme learning machine (CEEMDAN-GWO-RELM) is proposed. The CEEMDAN is used to decompose the time series of AEC degradation process into several sequences, which can decouple the feature of local fluctuations from global degradation in the AEC time series. Then, the RELM optimized by GWO is used to predict each sequence after decomposition. RELM has the advantages of fewer hyperparameters and less operation time, and GWO with strong astringency is used for its optimization to obtain better fault prediction. Eventually, the predicted values are reconstructed to obtain the predicted values of the integrated model. The results show that, based on the aging data of AEC, the integrated model based on CEEMDAN-GWO-RELM can provide better prediction progress than traditional models, and the maximum relative error of each prediction time point is lower than 1.6%. INDEX TERMSelectrolytic capacitor, fault prediction, CEEMDAN, GWO-RELM. Acronyms AEC Aluminum electrolytic capacitors IMF Intrinsic mode function CEEMDAN Complete ensemble empirical mode decomposition RMSE Root mean square error This article has been accepted for publication in IEEE Access.