Scalable Reliability Monitoring of GaN Power Converter Through Recurrent Neural Networks

Biglarbegian, Mehrdad; Baharani, Mohammadreza; Kim, Namwon; Tabkhi, Hamed; Parkhideh, Babak

doi:10.1109/ecce.2018.8557565

Cited by 11 publications

(6 citation statements)

References 17 publications

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“…Hard and soft faults in the super-buck converter circuit have been investigated in [53] using the Kernel Entropy-Based Classification approach and ELM methods. In [54], one of the novel deep learning applications called Long Short-Term Memory (LSTM), a prominence version of the Recurrent Neural Network (RNN) has been suggested for fault detection and scalable reliability in high-frequency Gallium Nitride power dc-dc converter. In 2018, in a valuable study [55], one of the powerful applications of deep learning in feature extraction called Convolutional Neural Network (CNN) has been proposed and utilized for the first time to detect OC fault in modular multilevel converter (MMC).…”

Section: B Literature Review Of Fault Detection In Pessmentioning

confidence: 99%

Data Mining Applications to Fault Diagnosis in Power Electronic Systems: A Systematic Review

Moradzadeh

Mohammadi‐Ivatloo

Pourhossein

et al. 2022

IEEE Trans. Power Electron.

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Section: B Literature Review Of Fault Detection In Pessmentioning

confidence: 99%

Data Mining Applications to Fault Diagnosis in Power Electronic Systems: A Systematic Review

Moradzadeh

Mohammadi‐Ivatloo

Pourhossein

et al. 2022

IEEE Trans. Power Electron.

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“…GaN-based devices have incredible performance and exhibit better material properties when compared to those devices made up of silicon. Using GaN device would be highly useful for power engineers in enhancing the reliability of the system [47], [48]. Crow Search Algorithm-LSSVM is novel approach which yields high computational efficiency for boost converters [49].…”

Section: Remaining Useful Life (Rul)mentioning

confidence: 99%

Investigation of reliability assessement in power electronics circuits using machine learning

Mestha¹,

A.J²

2021

IJPEDS

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<p>Recent advances in power electronics (PE) and machine learning (ML) have prompted the technologists to adapt these new technologies to improve the reliability of PE systems. During the process, a lot of investigations on the performance and reliability of PE systems is carried out. The intention of this paper is to present a comprehensive study of advances in the field of reliability of PE systems using machine learning. Recent publications in this regard are analysed and findings are tabulated. In addition to this, literatures published in the prediction of remaining useful life (RUL) of power electronic components is discussed with emphasis on its limitations.</p>

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“…In addition, the RT-models can be utilized as digital-twins for predictive maintenance of the EVs components [22]. Moreover, the RT-models can be used to verify the reliability of the PE converters for different mission profiles, thus providing a guideline to estimate the PE components ageing factor [23].…”

Section: Necessity Of Hardware-in-the Loop Testingmentioning

confidence: 99%

“…The Improved Generalized Steinmetz Equation (IGSE) is used to estimate the core losses and gap losses of the inductor as equations (21)-(23), where SE parameters are taken into consideration. The parameters are accumulated from the vendor datasheet.…”

mentioning

confidence: 99%

Scalable Modeling Approach and Robust Hardware-in-the-Loop Testing of an Optimized Interleaved Bidirectional HV DC/DC Converter for Electric Vehicle Drivetrains

et al. 2020

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Automotive Original Equipment Manufacturers (OEMs) require varying levels of functionalities and model details at different phases of the electric vehicles (EV) development process, with a trade-off between accuracy and execution time. This article proposes a scalable modelling approach depending on the multi-objective targets between model functionalities, accuracy and execution time. In this article, four different fidelity levels of modelling approaches are described based on the model functionalities, accuracy and execution time. The highest error observed between the low fidelity (LoFi) map-based model and the high fidelity (HiFi) physics-based model is 5.04%; while, the simulation time of the LoFi model is ~10 4 times faster than corresponding one of the HiFi model. A detailed comparison of all characteristics between multi-fidelity models is demonstrated in this paper. Furthermore, a dSPACE SCALEXIO Hardwarein-the-Loop (HiL) testbench, equipped with a minimal latency of 18μsec, is used for real-time (RT) model implementation of the EV's HV DC/DC converter. The performance of the entire HiL setup is compared with the Model-in-the-Loop (MiL) setup and the highest RMSE is limited to 0.54 among the HiL and MiL results. Moreover, the accuracy (95.7%) of the passive component loss estimation is verified through the Finite Element Method (FEM) software model. Finally, the experimental results of a full-scale 30-kW SiC DC/DC converter prototype are presented to validate the accuracy and correlation between multi-fidelity models. It has been observed that the efficiency deviation between the hardware prototype and multi-fidelity models is less than 1.25% at full load. Furthermore, the SiC Interleaved Bidirectional Converter (IBC) prototype achieves a high efficiency of 98.4% at rated load condition. INDEX TERMS DC/DC interleaved converter, EV, efficiency, electro-thermal modelling, multi-fidelity models, optimization, scalable modelling, Hardware-in-the-loop, and wide-bandgap technology.

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Scalable Reliability Monitoring of GaN Power Converter Through Recurrent Neural Networks

Cited by 11 publications

References 17 publications

Data Mining Applications to Fault Diagnosis in Power Electronic Systems: A Systematic Review

Data Mining Applications to Fault Diagnosis in Power Electronic Systems: A Systematic Review

Investigation of reliability assessement in power electronics circuits using machine learning

Scalable Modeling Approach and Robust Hardware-in-the-Loop Testing of an Optimized Interleaved Bidirectional HV DC/DC Converter for Electric Vehicle Drivetrains

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