An Ensemble of Component-Based and Population-Based Self-Organizing Maps for the Identification of the Degradation State of Insulated-Gate Bipolar Transistors

This paper gives an overview of the Artificial Intelligence (AI) applications for power electronic systems. The three distinctive life-cycle phases, design, control, and maintenance are correlated with one or more tasks to be addressed by AI, including optimization, classification, regression, and data structure exploration. The applications of four categories of AI are discussed, which are expert system, fuzzy logic, metaheuristic method, and machine learning. More than 500 publications have been reviewed to identify the common understandings, practical implementation challenges, and research opportunities in the application of AI for power electronics. This paper is accompanied by an Excel file listing the relevant publications for statistical analytics.

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“…In [133], a health state identification method for IGBTs based on self-organizing maps (SOMs) is proposed. It is essentially a clustering task.…”

Section: A Condition Monitoringmentioning

confidence: 99%

An Overview of Artificial Intelligence Applications for Power Electronics

Zhao

Blaabjerg

Wang

2021

IEEE Trans. Power Electron.

462

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show abstract

“…In [132], a health state identification method for IGBTs based on self-organizing maps (SOMs) is proposed. It is essentially a clustering task, where the states of the device are clustered as the healthy state, the partially degraded state, the heavily degraded state, and the failure state considering the distance between the input measurements (including collector current I c , collector-emitter voltage V ce , and case temperature T ) and the best matching unit of the trained SOMs.…”

Section: A Condition Monitoringmentioning

confidence: 99%

An overview of artificial intelligence applications for power electronics

Blaabjerg¹,

Wang²

2020

Preprint

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<div>This is a preprint version of the manuscript submitted to IEEE on June 4, 2020.</div><div><br></div><div>This paper gives an overview of the Artificial Intelligence (AI) applications for power electronic systems. The three distinctive life-cycle phases, design, control, and maintenance are correlated with one or more tasks to be addressed by AI, including optimization, classification, regression, and data structure exploration. The applications of four categories of AI are discussed, which are expert system, fuzzy logic, metaheuristic method, and machine learning. More than 500 publications have been reviewed to identify the common understandings, practical implementation challenges, and research opportunities in the application of AI for power electronics.<br></div>

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“…The prediction method is the key technique in the RUL analysis. Although some lifetime models are proposed for RUL prediction [32][33][34][35][36][37][38], the data-driven methods are obviously advanced in dealing with this kind of problem with huge historic data [39][40][41][42][43][44]. In recent years, the data-driven methods in condition estimation and prediction have developed very fast, thanks to the progress of AI and high performance programing chips [45][46][47][48][49][50][51].…”

Section: Introductionmentioning

confidence: 99%

Overview of recent progress in condition monitoring for insulated gate bipolar transistor modules: Detection, estimation, and prediction

et al. 2021

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The insulated gate bipolar transistor (IGBT) is one of the most fragile components in power electronics converters. In order to improve the reliability of IGBTs, various measurements are taken according to the condition monitoring (CM) technique. Traditional CM techniques include the measurement and estimation of the device operation conditions. Recently, emerging techniques have been developed, not only for the detection and estimation but also for the prognostics of IGBTs with the condition data. In this paper, a review is performed on the recent progress in the CM techniques for IGBTs. First, some emerging electrical and thermal measurements are reviewed. Based on the sensed data, the health indicator estimation techniques are summarised. Moreover, for the emerging prognostics and health management applications, some remaining using lifetime (RUL) prediction methods are reviewed. Finally, the research gaps and directions are discussed for the CM in IGBT applications.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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An Ensemble of Component-Based and Population-Based Self-Organizing Maps for the Identification of the Degradation State of Insulated-Gate Bipolar Transistors

Cited by 12 publications

References 43 publications

An Overview of Artificial Intelligence Applications for Power Electronics

An Overview of Artificial Intelligence Applications for Power Electronics

An overview of artificial intelligence applications for power electronics

Overview of recent progress in condition monitoring for insulated gate bipolar transistor modules: Detection, estimation, and prediction

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