Junction Temperature Prediction of IGBT Power Module Based on BP Neural Network

Wu, Junke; Zhou, Luowei; Du, Xiong; Sun, Pengju

doi:10.5370/jeet.2014.9.3.970

Cited by 21 publications

(7 citation statements)

References 17 publications

(17 reference statements)

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“…Constantly update weight value and threshold value until the results meet the error precision. At last, save the final weight value and threshold value as the initial weight value and threshold value to predict construction cost of the project [17,18].…”

Section: Prediction Modelmentioning

confidence: 99%

Prediction Technology of Power Transmission and Transformation Project Cost Based on the Decomposition-Integration

Niu

Qiu

et al. 2015

Mathematical Problems in Engineering

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Through the analysis of power transmission and transformation project cost, total cost can be decomposed into construction cost, equipment purchase cost, installation cost, and other costs. This paper proposes a decomposition-integration cost prediction model taking a substation project as an example by fully considering the cost characteristics. In decomposition module, the total cost is decomposed into four expenses. In prediction module, different forecasting models are selected to forecast different expense. In integrated module, choose different integration methods to get the predicting results of total cost. The empirical results show that decomposition-integration prediction algorithm has good effect which can effectively predict the cost of power transmission and transformation project and has practical application and popularization value.

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Section: Prediction Modelmentioning

confidence: 99%

Prediction Technology of Power Transmission and Transformation Project Cost Based on the Decomposition-Integration

Niu

Qiu

et al. 2015

Mathematical Problems in Engineering

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“…It has a powerful nonlinear mapping ability and can be trained by a backpropagation algorithm. Literature [9] constructed an IGBT junction temperature prediction model using this advantage of the BP neural network and achieved better prediction accuracy than polynomial fitting methods. However, BP neural network also has some disadvantages.…”

Section: Introductionmentioning

confidence: 99%

Junction Temperature Prediction Model for GaAs HBT Devices Based on ASO-ELM

2023

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In this study, an accurate temperature prediction model is proposed for GaAs HBT, which considers both the bias voltage and current rather than power consumption only. The increase in temperature is closely related to the heat source property, which leads to a complex interaction between the lattice vibration and the uneven distribution of the electric field and current density. To improve the accuracy and stability of the temperature prediction model, a machine learning method of Extreme Learning Machine (ELM) optimized with an Atomic Search Algorithm (ASO) is introduced. The validity of the model is verified by comparing it with experimental observations by the QFI InfraScope TM temperature mapping system. The predicted temperatures for an 8 × 8 HBT power cell fabricated with 2 μm GaAs technology show good agreement with the measurement results, with a ±2 °C error and a relative error deviation below 3%. This demonstrates the superior performance of the proposed model in accurately predicting the temperature of GaAs HBT.

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“…However, the applicable range of the AR model for temperature prediction is limited to linear time series. Wu et al [ 13 ] established a back propagation (BP) artificial neural network model to predict the junction temperature of an IGBT power module by measuring the device’s saturation voltage and collector current under a specified temperature. However, this paper remains a preliminary study for semiconductor temperature prediction by using neural networks, and the prediction accuracy may decline as the quantity of training data decreases.…”

Section: Introductionmentioning

confidence: 99%

Remaining Useful Life Estimation of Insulated Gate Biploar Transistors (IGBTs) Based on a Novel Volterra k-Nearest Neighbor Optimally Pruned Extreme Learning Machine (VKOPP) Model Using Degradation Data

Liu

Mei

Zeng

et al. 2017

Sensors

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The insulated gate bipolar transistor (IGBT) is a kind of excellent performance switching device used widely in power electronic systems. How to estimate the remaining useful life (RUL) of an IGBT to ensure the safety and reliability of the power electronics system is currently a challenging issue in the field of IGBT reliability. The aim of this paper is to develop a prognostic technique for estimating IGBTs’ RUL. There is a need for an efficient prognostic algorithm that is able to support in-situ decision-making. In this paper, a novel prediction model with a complete structure based on optimally pruned extreme learning machine (OPELM) and Volterra series is proposed to track the IGBT’s degradation trace and estimate its RUL; we refer to this model as Volterra k-nearest neighbor OPELM prediction (VKOPP) model. This model uses the minimum entropy rate method and Volterra series to reconstruct phase space for IGBTs’ ageing samples, and a new weight update algorithm, which can effectively reduce the influence of the outliers and noises, is utilized to establish the VKOPP network; then a combination of the k-nearest neighbor method (KNN) and least squares estimation (LSE) method is used to calculate the output weights of OPELM and predict the RUL of the IGBT. The prognostic results show that the proposed approach can predict the RUL of IGBT modules with small error and achieve higher prediction precision and lower time cost than some classic prediction approaches.

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Junction Temperature Prediction of IGBT Power Module Based on BP Neural Network

Cited by 21 publications

References 17 publications

Prediction Technology of Power Transmission and Transformation Project Cost Based on the Decomposition-Integration

Prediction Technology of Power Transmission and Transformation Project Cost Based on the Decomposition-Integration

Junction Temperature Prediction Model for GaAs HBT Devices Based on ASO-ELM

Remaining Useful Life Estimation of Insulated Gate Biploar Transistors (IGBTs) Based on a Novel Volterra k-Nearest Neighbor Optimally Pruned Extreme Learning Machine (VKOPP) Model Using Degradation Data

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