| Abstract: |
| Three-phase pulse width modulation converters using insulated gate bipolar transistors (IGBTs) have been widely
used in industrial application. However, faults in IGBTs can severely affect the operation and safety of the power
electronics equipment and loads. For ensuring system reliability, it is necessary to accurately detect IGBT faults
accurately as soon as their occurrences. This paper proposes a diagnosis method based on data-driven theory. A
novel randomized learning technology, namely extreme learning machine (ELM) is adopted into historical data
learning. Ensemble classifier structure is used to improve diagnostic accuracy. Finally, time window is defined to
illustrate the relevance between diagnostic accuracy and data sampling time. By this mean, an appropriate time
window is achieved to guarantee a high accuracy with relatively short decision time. Compared to other traditional
methods, ELM has a better classification performance. Simulation tests validate the proposed ELM ensemble
diagnostic performance. |
| Key words: IGBT open-circuit fault, Extreme learning machine (ELM), Data-driven method, Ensemble structure |
| DOI：10.1186/s41601-018-0109-x |
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| Fund: |
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