Real time remaining useful life prediction of the electrolytic capacitor

Khandebharad, A. R.; Dhumale, R. B.; Lokhande, S. S.; Lokhande, Shashikant

doi:10.1109/infop.2015.7489460

Cited by 3 publications

(1 citation statement)

References 25 publications

(37 reference statements)

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“…Afterwards, the loading profiles of the converters can be obtained by solving the optimal power flow problems. Furthermore, for Process 3), RUL prediction methods for the IGBTs, diodes, and capacitors can be found in [25], [26], and [27], respectively. Afterwards, the predicted RUL cumulative distribution functions of the components in the submodules can be obtained.…”

Section: Submodulesmentioning

confidence: 99%

A Tractable Failure Probability Prediction Model for Predictive Maintenance Scheduling of Large-Scale Modular-Multilevel-Converters

Peyghami

Núñez

et al. 2023

IEEE Trans. Power Electron.

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Modular-multilevel-converters (MMCs) are vital components in direct current transmission networks. Predictive maintenance scheduling of MMCs requires estimations of the failure probabilities of MMCs during a period of time in the future. Particularly, the predicted future failure probabilities are influenced by two main factors, the mission profiles of the MMCs and the maintenance decisions on the MMCs during the prediction period. This paper proposes a failure probability prediction model (FPM) for MMCs by considering these two factors. First, the expectations of the failure probabilities of the components for all the scenarios of mission profiles are obtained. Second, in predictive maintenance scheduling problems, the decisions to perform the maintenance actions are represented by binary variables. When the number of submodules is very large, using the binomial probability form currently used in reliability engineering to express the "r-out-of-n" failure probability of arms of the MMCs is intractable. Thus, this paper proposes a tractable form (T-form) in FPM by observing that the submodules on one arm are homogeneous. Furthermore, an approximation method, i.e., clustering and assignment (C&A), is proposed to reduce the computation times for calculating the parameters needed by the proposed T-form. Then, we perform a case study that assesses the accuracy and computation time of the C&A approach. The results show that the accuracy of the C&A approach is high and that the computation time is reduced significantly compared with the accurate method. We also show that the computation time for solving the predictive maintenance scheduling problem can be reduced hugely by using the T-form instead of the binomial probability form.

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Section: Submodulesmentioning

confidence: 99%

A Tractable Failure Probability Prediction Model for Predictive Maintenance Scheduling of Large-Scale Modular-Multilevel-Converters

Peyghami

Núñez

et al. 2023

IEEE Trans. Power Electron.

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Data Driven Remaining Life Prediction of Electrolytic Capacitor in DC/DC Converter

Zhang

et al. 2021

J. Phys.: Conf. Ser.

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A remaining useful life prediction model for electrolytic capacitor in DC-DC converter is presented in this paper based on Neural Network method. First, the degradation data of electrolytic capacitor is acquired from mathematical models. Next, the neural network prediction model is established by processing the degradation data of the electrolytic capacitor. Finally, the effectiveness of the developed life prediction model is verified using Matlab/Simulink. This paper provides the basis for the overall life prediction of dc/dc converter.

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Exponential degradation model for Remaining Useful Life estimation of electrolytic capacitors

Patrizi,

Ciani,

Catelani

2023

2023 IEEE International Workshop on Metrology for Automotive (MetroAutomotive)

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Real time remaining useful life prediction of the electrolytic capacitor

Cited by 3 publications

References 25 publications

A Tractable Failure Probability Prediction Model for Predictive Maintenance Scheduling of Large-Scale Modular-Multilevel-Converters

A Tractable Failure Probability Prediction Model for Predictive Maintenance Scheduling of Large-Scale Modular-Multilevel-Converters

Data Driven Remaining Life Prediction of Electrolytic Capacitor in DC/DC Converter

Exponential degradation model for Remaining Useful Life estimation of electrolytic capacitors

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