Power Loss Prediction for Aging Characteristics and Condition Monitoring for Parallel-Connected Power Modules Using Nonlinear Autoregressive Neural Network

Joint Authors

Xu, Shengyou
Yang, Xin
Ran, Li
Chen, Minyou
Lai, Wei

Source

Complexity

Issue

Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-10, 10 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2019-02-03

Country of Publication

Egypt

No. of Pages

10

Main Subjects

Philosophy

Abstract EN

Power modules connected in parallel may have different electrothermal performance variances resulting from aging because of the nonuniform rate of degradation; different electrothermal performance variances mean different current sharing, different junction temperature, and power losses, which will directly influence the overall characteristics of them.

Thus, it is essential to monitor the condition and evaluate the degradation grade to improve the reliability of large-scale power modules.

In this paper, the impact of thermal resistance difference on current sharing, junction temperature, and power loss of parallel-connected power modules has been discussed and analyzed.

Additionally, a methodology is proposed for condition monitoring and evaluation of the power modules without intruding them by recognizing the increase in external power loss due to internal degradation from aging.

In this method, power modules are deemed as a whole system considering only external factors associated with them, all important electrical and thermal parameters are classified as the inputs, and power loss is considered as the output.

Firstly, power dissipation is predicted by models using NARX (nonlinear autoregressive with exogenous input) neural network.

Then, a monitoring method is illustrated based on the prediction model; a reasonable criterion for the error between the normal and the predicted real-time power loss is established.

Finally, the real-time condition and the degradation grade of aging can be evaluated so that the operator can take suitable operating measures by means of this approach.

Experimental results validated the effectiveness of the proposed methodology.

American Psychological Association (APA)

Xu, Shengyou& Yang, Xin& Ran, Li& Chen, Minyou& Lai, Wei. 2019. Power Loss Prediction for Aging Characteristics and Condition Monitoring for Parallel-Connected Power Modules Using Nonlinear Autoregressive Neural Network. Complexity،Vol. 2019, no. 2019, pp.1-10.
https://search.emarefa.net/detail/BIM-1133072

Modern Language Association (MLA)

Xu, Shengyou…[et al.]. Power Loss Prediction for Aging Characteristics and Condition Monitoring for Parallel-Connected Power Modules Using Nonlinear Autoregressive Neural Network. Complexity No. 2019 (2019), pp.1-10.
https://search.emarefa.net/detail/BIM-1133072

American Medical Association (AMA)

Xu, Shengyou& Yang, Xin& Ran, Li& Chen, Minyou& Lai, Wei. Power Loss Prediction for Aging Characteristics and Condition Monitoring for Parallel-Connected Power Modules Using Nonlinear Autoregressive Neural Network. Complexity. 2019. Vol. 2019, no. 2019, pp.1-10.
https://search.emarefa.net/detail/BIM-1133072

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1133072