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Title: A new ensemble-based classifier for IGBT open-circuit fault diagnosis in three-phase PWM converter
Authors: Xia, Yang
Gou, Bin
Xu, Yan
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2018
Source: Xia, Y., Gou, B., & Xu, Y. (2018). A new ensemble-based classifier for IGBT open-circuit fault diagnosis in three-phase PWM converter. Protection and Control of Modern Power Systems, 3(1), 33-. doi:10.1186/s41601-018-0109-x
Journal: Protection and Control of Modern Power Systems
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.
ISSN: 2367-0983
DOI: 10.1186/s41601-018-0109-x
Rights: © 2018 The Author(s) (published by SpringerOpen). This is an open-access article distributed under the terms of the Creative Commons Attribution License.
Fulltext Permission: open
Fulltext Availability: With Fulltext
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