Artificial Intelligence Aided Automated Design for Reliability of Power Electronic Systems

Dragicevic, Tomislav; Wheeler, Patrick; Blaabjerg, Frede

Abstract

© 1986-2012 IEEE. This paper proposes a new methodology for automated design of power electronic systems realized through the use of artificial intelligence. Existing approaches do not consider the system's reliability as a performance metric or are limited to reliability evaluation for a certain fixed set of design parameters. The method proposed in this paper establishes a functional relationship between design parameters and reliability metrics, and uses them as the basis for optimal design. The first step in this new framework is to create a nonparametric surrogate model of the power converter that can quickly map the variables characterizing the operating conditions (e.g., ambient temperature and irradiation) and design parameters (e.g., switching frequency and dc link voltage) into variables characterizing the thermal stress of a converter (e.g., mean temperature and temperature variation of its devices). This step can be carried out by training a dedicated artificial neural network (ANN) either on experimental or simulation data. The resulting network is named as text{ANN}-{1} and can be deployed as an accurate surrogate converter model. This model can then be used to quickly map the yearly mission profile into a thermal stress profile of any selected device for a large set of design parameter values. The resulting data is then used to train text{ANN}-{2}, which becomes an overall system representation that explicitly maps the design parameters into a yearly lifetime consumption. To verify the proposed methodology, text{ANN}-{2} is deployed in conjunction with the standard converter design tools on an exemplary grid-connected PV converter case study. This study showed how to find the optimal balance between the reliability and output filter size in the system with respect to several design constraints. This paper is also accompanied by a comprehensive dataset that was used for training the ANNs.

Citation

Dragicevic, T., Wheeler, P., & Blaabjerg, F. (2019). Artificial Intelligence Aided Automated Design for Reliability of Power Electronic Systems. IEEE Transactions on Power Electronics, 34(8), 7161-7171. https://doi.org/10.1109/TPEL.2018.2883947

• Journal Article Type: Article
• Acceptance Date: Nov 26, 2018
• Online Publication Date: Dec 20, 2018
• Publication Date: Aug 1, 2019
• Deposit Date: Dec 20, 2019
• Publicly Available Date: Jan 15, 2020
• Journal: IEEE Transactions on Power Electronics
• Print ISSN: 0885-8993
• Electronic ISSN: 1941-0107
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 34
• Issue: 8
• Pages: 7161-7171
• DOI: https://doi.org/10.1109/TPEL.2018.2883947
• Keywords: Electrical and Electronic Engineering
• Public URL: https://nottingham-repository.worktribe.com/output/1516328
• Publisher URL: https://ieeexplore.ieee.org/document/8584133
• Additional Information: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.