Condition Monitoring of DC-Link Capacitors Using Hidden Markov Model Supported-Convolutional Neural Network

McGrew, Tyler; Sysoeva, Viktoriia; Cheng, Ching‐Yu; Scott, Mark J.

doi:10.1109/apec42165.2021.9487107

Cited by 6 publications

(4 citation statements)

References 19 publications

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“…In [73], the spectral content of the EMI from 15-43MHz is used to predict the DC-link health of the inverter. The EMI Steps involved in this method is presented [74].…”

Section: Esr = ∆U DC I Sc (47)mentioning

confidence: 99%

“…However, for training, large data is needed. A neural network approach is proposed for health monitoring in [74]. The method involves extracting time-frequency characteristics of the high-frequency component in the DC-link current using continuous wavelet transform.…”

Section: Esr = ∆U DC I Sc (47)mentioning

confidence: 99%

“…By using this VEN, three discharge curves can be obtained, i.e., without X 1 and X 2 , with X 1 only, and with X 2 . Change in voltage and time taken are obtained from each section, which is used for the capacitance and ESR calculation using the relations ( 73)- (74). The block diagram of the proposed scheme is shown in Fig.…”

Section: Monitoring In Ac-dc-ac Convertersmentioning

confidence: 99%

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Advances in Capacitor Health Monitoring Techniques for Power Converters: A Review

Ramees,

Ahmad

2023

IEEE Access

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Capacitors play a critical role in power electronic systems, and their health and performance directly impact system reliability and efficiency. This article provides an overview of advanced techniques developed for capacitor monitoring, focusing on diagnosing, estimating, and predicting capacitor health in various power electronic converters. The condition monitoring techniques are categorized based on the type of converter, and a comprehensive review and comparison of various methods are provided within each category. The article covers various techniques, including impedance spectroscopy, frequency response analysis, harmonic analysis, wavelet analysis, short-time Fourier transform, and algorithm-based predictive models. These techniques enable early detection of capacitor faults, accurate estimation of capacitance and equivalent series resistance (ESR), and prediction of the remaining useful life of capacitors. By implementing these advanced monitoring techniques, engineers and researchers can enhance system reliability, prevent unexpected failures, and optimize maintenance and replacement strategies for capacitors in power electronic systems.

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“…In [73], the spectral content of the EMI from 15-43MHz is used to predict the DC-link health of the inverter. The EMI Steps involved in this method is presented [74].…”

Section: Esr = ∆U DC I Sc (47)mentioning

confidence: 99%

Section: Esr = ∆U DC I Sc (47)mentioning

confidence: 99%

Section: Monitoring In Ac-dc-ac Convertersmentioning

confidence: 99%

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Advances in Capacitor Health Monitoring Techniques for Power Converters: A Review

Ramees,

Ahmad

2023

IEEE Access

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“…The back propagation neural network is combined with the improved grey wolf optimization algorithm to identify the parameters of the capacitor in an EV inverter [9]. The continuous wavelet transform, convolutional neural network, and hidden Markov model are combined to classify the health condition of capacitors in a 3-phase inverter [10]. A conditional deep neural network with a dropout technique is proposed to predict the accelerated aging conditions of the capacitors at different elevated temperatures and voltages [11].…”

Section: Introductionmentioning

confidence: 99%

An online comprehensive health monitoring system for automotive auxiliary converter

Wu,

Xia,

Cui

et al. 2024

Meas. Sci. Technol.

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As an essential part of the electronic system in electric vehicles (EVs), the automotive auxiliary converter (AAC) connects the batteries and burdens the auxiliary power supply. This paper mainly proposes an online comprehensive health monitoring system for AAC. Firstly, based on the control diagram of the LLC resonant converter, three critical components are emerging, including capacitor, optocoupler, and semiconductor. These components can partly reflect the operation condition of the output loop, feedback loop, and control link, respectively. Then the characteristics parameters of these three components, equivalent series resistance (ESR) of the capacitor, current transfer ratio (CTR) of the optocoupler, and on-resistance of the semiconductor jointly construct the three-dimensional health monitoring vector. Finally, the online monitoring accuracy is verified and the proposed system can thus effectively promote the operation and maintenance of AAC. Furthermore, the online health monitoring system enables a flexible expansion, which can be further customized into different kinds of power converters, showing high applicability and practicality.

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Aging Detection for Capacitors in Power Electronic Converters

Zhao

Huai

2022

Fault Analysis and Its Impact on Grid‐connected Photovoltaic Systems Performance

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Condition Monitoring of DC-Link Capacitors Using Hidden Markov Model Supported-Convolutional Neural Network

Cited by 6 publications

References 19 publications

Advances in Capacitor Health Monitoring Techniques for Power Converters: A Review

Advances in Capacitor Health Monitoring Techniques for Power Converters: A Review

An online comprehensive health monitoring system for automotive auxiliary converter

Aging Detection for Capacitors in Power Electronic Converters

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