Fault Prediction for Capacitor of Power Converters Based on CEEMDAN and GWO-RELM

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 condu...

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Bibliographic Details
Published in:IEEE access 2022, Vol.10, p.123971-123980
Main Authors: Meng, Linghui, Sun, Quan, Zhou, Zhenwei, Yang, Lichen
Format: Article
Language:English
Subjects:
Algorithms
Aluminum
Artificial neural networks
Capacitors
CEEMDAN
Circuit faults
Data models
Decoupling
Degradation
Electrolytic capacitor
Electrolytic capacitors
Empirical analysis
Energy storage
Fault diagnosis
fault prediction
GWO-RELM
Integrated circuit modeling
Machine learning
Model accuracy
Optimization
Power converters
Prediction models
Predictions
Predictive models
Sequences
Time series
Time series analysis
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Summary: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%.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2022.3224187