Accurate Circuit-Level Modelling of IGBTs with Thermal Phenomena Taken into Account

Górecki, Paweł; Górecki, Krzysztof; Zarębski, J.

doi:10.3390/en14092372

Cited by 8 publications

(10 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It results from the fact that thermal resistance of a semiconductor component is not constant, as it is assumed in linear thermal models based on the Foster and Cauer networks [84]. In the models described in [81,82], the value of thermal resistance depends on the power emitted in it, and in [67,85]-on the junction temperature of the semiconductor device and the ambient temperature.…”

Section: Methods Based On Transient Analysismentioning

confidence: 99%

Methods of Fast Analysis of DC–DC Converters—A Review

Górecki

2021

Electronics

Self Cite

View full text Add to dashboard Cite

The paper discusses the methods of fast analysis of DC–DC converters dedicated to computer programmes. Literature methods of such an analysis are presented, which enable determination of the characteristics of the considered converters in the steady state and in the transient states. The simplifications adopted at the stage of developing these methods are discussed, and their influence on the accuracy of computations is indicated. Particular attention is paid to the methods of fast analysis of DC–DC converters, taking into account thermal phenomena in semiconductor devices. The sample results of computations of the DC–DC boost type converter obtained with the use of the selected methods are presented. The scope of application of particular computation methods and their duration times are discussed. Computations were performed with the use of SPICE and PLECS.

show abstract

Section: Methods Based On Transient Analysismentioning

confidence: 99%

Methods of Fast Analysis of DC–DC Converters—A Review

Górecki

2021

Electronics

Self Cite

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 97%

“…As it is shown in many papers [10][11][12][13][14][15], temperature affects properties of semiconductor devices. Its increase shortens their life time [10,11] and influences the characteristics of these devices [12][13][14][15]. The junction temperature of any semiconductor device situated on the common substrate with other devices is the sum of two components.…”

Section: Introductionmentioning

confidence: 97%

SPICE-Aided Nonlinear Electrothermal Modeling of an IGBT Module

Górecki,

Górecki

2023

Electronics

Self Cite

View full text Add to dashboard Cite

The paper proposes a compact electrothermal model of the IGBT module in the form of a subcircuit for SPICE. This model simultaneously takes into account electrical phenomena occurring in the module components and thermal phenomena occurring in this module. In the description of electrical phenomena, the previously formulated models of the IGBT and the diode are used, whereas the description of thermal phenomena is original. While describing thermal phenomena, self-heating in each component, mutual thermal couplings between each pair of these components, and the influence of the dissipated power on the efficiency of heat removal are taken into account. The form of the proposed model and the results of its experimental verification for the IGBT module operating under different cooling conditions are presented. The DC characteristics of the module and the characteristics of the half-bridge converter containing the considered module are presented.

show abstract

“…Prediction methods based on physical models are difficult to apply under actual working conditions because they require the establishment of a suitable physical model through expert knowledge and the acquisition of the geometrical and electrical parameters of the product. Prediction methods based on analytical models establish a mathematical model connecting the number of aging cycles and specific physical quantities based on failure data from IGBT module operation [8][9][10][11], such as the Coffin-Manson model [10], Bayerer model [11], etc. However, the established mathematical models assume ideal conditions, ignore some influencing factors, and only make good predictions for the data in the source domain, and it is difficult to make good failure predictions under other working conditions that cannot be directly predicted, which is seriously out of line with practical engineering applications.…”

Section: Introductionmentioning

confidence: 99%

Research on Power Device Fault Prediction of Rod Control Power Cabinet Based on Improved Dung Beetle Optimization–Temporal Convolutional Network Transfer Learning Model

Ye,

Chen,

Liu

et al. 2024

Energies

View full text Add to dashboard Cite

In order to improve the reliability and maintainability of rod control power cabinets in nuclear power plants, this paper uses insulated gate bipolar transistors (IGBTs), the key power device of rod control power cabinets, as the object of research on cross-working-condition fault prediction. An improved transfer learning (TL) model based on a temporal convolutional network (TCN) is proposed to solve the problem of low fault prediction accuracy across operating conditions. First, the peak emitter voltage of an IGBT aging dataset is selected as the source domain failure characteristic, and the TCN model is trained after the removal of outliers and noise reduction. Then, the time–frequency features are extracted according to the characteristics of the target domain data, and the target domain representation data are obtained using kernel principal component analysis (KPCA) for dimensionality reduction. Finally, the TCN model trained on the source domain is transferred; the model is fine-tuned according to the target domain data, and the learning rate, the number of hidden layer nodes, and the number of training times in the network model are optimized using the dung beetle optimization (DBO) algorithm to obtain the optimal network, making it more suitable for target sample fault prediction. The prediction results of this TCN model, the long short-term memory (LSTM) model, the gated recurrent unit (GRU) model, and the recursive neural network (RNN) model are compared and analyzed by selecting prediction performance evaluation indexes. The results show that the TCN model has a better predictive effect. Comparing the prediction results of the TCN-based optimized transfer learning model with those of the directly trained TCN model, the mean square error, root mean square error, and mean absolute error are reduced by a factor of two to three, which provides an effective solution for fault prediction across operating conditions.

show abstract

Accurate Circuit-Level Modelling of IGBTs with Thermal Phenomena Taken into Account

Cited by 8 publications

References 51 publications

Methods of Fast Analysis of DC–DC Converters—A Review

Methods of Fast Analysis of DC–DC Converters—A Review

SPICE-Aided Nonlinear Electrothermal Modeling of an IGBT Module

Research on Power Device Fault Prediction of Rod Control Power Cabinet Based on Improved Dung Beetle Optimization–Temporal Convolutional Network Transfer Learning Model

Contact Info

Product

Resources

About