Transient thermal network model for train‐induced wind cooling dry‐type on‐board traction transformer in electric multiple units

Zhou, Lijun; Yuan, Shuai; Zhu, Qiuyue; Wang, Dongyang; Wang, Lujia

doi:10.1049/hve2.12272

Cited by 5 publications

(1 citation statement)

References 24 publications

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“…It is noted that the thermal network was reported to be a feasible method to characterise the internal material degradation of IGBT modules [21]. The thermal networks have advantages of high calculation accuracy and high simulation speed for the temperature of specific monitoring points [22][23][24][25]. Unfortunately, the reported thermal network models still lack the ability to monitor the internal material degradation of IGBT modules because they cannot calculate the difference between the junction temperatures of degraded IGBT module and that of the undegraded one.…”

Section: Introductionmentioning

confidence: 99%

A direct bonding copper degradation monitoring method for insulated gate bipolar transistor modules: Boundary‐dependent thermal network combined with feedback control

Zhang

Cheng

et al. 2023

High Voltage

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The direct bonding copper (DBC) substrates of insulated gate bipolar transistor (IGBT) modules degrade inevitably under cycling thermo‐mechanical stress, causing potential threat to the reliability of IGBT modules. However, little attention has been paid to monitoring their degradation. This paper proposes a DBC degradation monitoring method for IGBT modules, which combines boundary‐dependent thermal network and feedback control. A thermal network is employed to describe the internal material degradation of IGBT modules and can be extracted from a finite‐element method model. The boundary conditions including power losses and DBC degradation are considered, enabling the thermal network suitable for various working conditions and different DBC degradation conditions of IGBT modules. The DBC degradation is characterised by its equivalent thermal conductivities measured in the thermal cycling ageing experiments. On the basis of the boundary‐dependent thermal network, feedback control is applied to monitor DBC degradation by regulating boundary‐dependent thermal impedances. Finally, the proposed model is verified from the effectiveness and accuracy of DBC degradation monitoring and junction temperature calculation. This method casts new light on thermal network modelling and could provide a feasible method for the monitoring of DBC degradation.

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Section: Introductionmentioning

confidence: 99%

A direct bonding copper degradation monitoring method for insulated gate bipolar transistor modules: Boundary‐dependent thermal network combined with feedback control

Zhang

Cheng

et al. 2023

High Voltage

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Calculation method for the dynamic temperature distribution of winding area in dry-type on-board traction transformer

Zhou,

Xia,

et al. 2024

International Journal of Electrical Power & Energy Systems

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Fast prediction of temperature distributions in oil natural air natural transformers using proper orthogonal decomposition reduced‐order data‐driven modelling

Lan,

Tang,

Gong

et al. 2024

High Voltage

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In response to the time‐consuming computational fluid dynamics simulations faced in naturally convective oil‐immersed transformers, which result from complex models and a high degree of freedom, an innovative reduced‐order digital twin prediction model for transformer temperature fields is proposed. This model facilitates fast predictions of transient temperature distributions. Initially, a comprehensive full‐order finite element model of transformer temperature distributions is established. Subsequently, a hybrid approach, combining proper orthogonal decomposition (POD)‐Galerkin and data‐driven techniques, is proposed to create a reduced‐order model (ROM). In this model, a Fourier number is utilised as a criterion to select POD training snapshot sets. Subsequently, the dynamic predictive capability of the proposed model under changing operational conditions is validated. Finally, the ROM is employed for fast predictions of temperature field, and its computational errors and time efficiency are compared across diverse operating conditions with full‐order models. The research findings confirm the precision, timeliness, and dynamic nature of the reduced‐order prediction model, offering a substantial improvement in prediction efficiency and capabilities, all while preserving the accuracy of the digital twin model.

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Transient thermal network model for train‐induced wind cooling dry‐type on‐board traction transformer in electric multiple units

Cited by 5 publications

References 24 publications

A direct bonding copper degradation monitoring method for insulated gate bipolar transistor modules: Boundary‐dependent thermal network combined with feedback control

A direct bonding copper degradation monitoring method for insulated gate bipolar transistor modules: Boundary‐dependent thermal network combined with feedback control

Calculation method for the dynamic temperature distribution of winding area in dry-type on-board traction transformer

Fast prediction of temperature distributions in oil natural air natural transformers using proper orthogonal decomposition reduced‐order data‐driven modelling

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