Artificial Intelligence and Long-term Performance of Power Electronics Systems

Shuai Zhao, Saeed Peyghami, Daniel Gebbran, Tomislav Dragicevic, Huai Wang, Frede Blaabjerg

    Research output: Contribution to journalConference articlepeer-review

    Abstract

    This paper presents the application of artificial intelligence (AI) in the long-term performance assessment of power electronics. Long term performance of power converters depends on two major factors: intrinsic material strength and extrinsic stressors. In order to ensure acceptable performance, various life cycle management approaches can be employed to either reinforce the strength of components or control the stressors. Reinforcement can be performed within the design or by means of maintenance within the operation. Furthermore, stress management can be carried out by suitable control methods. Due to the complexity of power converters especially in large-scale systems, AI methods are employed to facilitate life cycle management of power converters. This paper presents the AI methods used for this purpose, implementation of AI methods, and basics of AI for maintenance in power converters. Furthermore, two case studies on the device level and system level are presented to illustrate the application of AI in the lifetime expansion of power electronics systems. Moreover, a perspective on challenges in the integration of AI in power electronics is provided.

    Original languageBritish English
    Pages (from-to)5-14
    Number of pages10
    JournalETG-Fachbericht
    Volume2022-March
    Issue number165
    StatePublished - 2022
    Event12th International Conference on Integrated Power Electronics Systems, CIPS 2022 - Berlin, Germany
    Duration: 15 Mar 202217 Mar 2022

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