A Robust Accuracy Weighted Random Forests Algorithm for IGBTs Fault Diagnosis in PWM Converters without Additional Sensors
When an insulated-gate bipolar transistor (IGBT) open-circuit fault occurs, a three-phase pulse-width modulated (PWM) converter can usually keep working, which will lead to system instability and more serious secondary faults. The fault detection and diagnosis of the converter is extremely necessary...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/4/2121 |
| _version_ | 1797483036286124032 |
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| author | Gen Qiu Fan Wu Kai Chen Li Wang |
| author_facet | Gen Qiu Fan Wu Kai Chen Li Wang |
| author_sort | Gen Qiu |
| collection | DOAJ |
| description | When an insulated-gate bipolar transistor (IGBT) open-circuit fault occurs, a three-phase pulse-width modulated (PWM) converter can usually keep working, which will lead to system instability and more serious secondary faults. The fault detection and diagnosis of the converter is extremely necessary to improve the reliability of the power supply system. In order to solve the problem of fault misdiagnosis caused by parameters disturbance, this paper proposes a robust accuracy weighted random forests online fault diagnosis model to accurately locate various IGBTs open-circuit faults. Firstly, the fault signal features are preprocessed by using the three-phase current signal and normalization method. Based on the test accuracy of the perturbed out-of-bag data and the multiple converters test data, a robust accuracy weighted random forests algorithm is proposed for extracting a mapping relationship between fault modes and current signal. In order to further improve the fault diagnosis performance, a parameter optimization model is built to optimize hyper-parameters of the proposed method. Finally, comparative simulation and online fault diagnosis experiments are carried out, and the results demonstrate the effectiveness and superiority of the method. |
| first_indexed | 2024-03-09T22:41:09Z |
| format | Article |
| id | doaj.art-efb5c955b648464ab0f07dc960ef315c |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T22:41:09Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-efb5c955b648464ab0f07dc960ef315c2023-11-23T18:39:38ZengMDPI AGApplied Sciences2076-34172022-02-01124212110.3390/app12042121A Robust Accuracy Weighted Random Forests Algorithm for IGBTs Fault Diagnosis in PWM Converters without Additional SensorsGen Qiu0Fan Wu1Kai Chen2Li Wang3School of Automation, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Automation, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Automation, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Automation, University of Electronic Science and Technology of China, Chengdu 611731, ChinaWhen an insulated-gate bipolar transistor (IGBT) open-circuit fault occurs, a three-phase pulse-width modulated (PWM) converter can usually keep working, which will lead to system instability and more serious secondary faults. The fault detection and diagnosis of the converter is extremely necessary to improve the reliability of the power supply system. In order to solve the problem of fault misdiagnosis caused by parameters disturbance, this paper proposes a robust accuracy weighted random forests online fault diagnosis model to accurately locate various IGBTs open-circuit faults. Firstly, the fault signal features are preprocessed by using the three-phase current signal and normalization method. Based on the test accuracy of the perturbed out-of-bag data and the multiple converters test data, a robust accuracy weighted random forests algorithm is proposed for extracting a mapping relationship between fault modes and current signal. In order to further improve the fault diagnosis performance, a parameter optimization model is built to optimize hyper-parameters of the proposed method. Finally, comparative simulation and online fault diagnosis experiments are carried out, and the results demonstrate the effectiveness and superiority of the method.https://www.mdpi.com/2076-3417/12/4/2121three-phase PWM converterIGBT open-circuit faultfault diagnoserobust accuracy weightedrandom forests |
| spellingShingle | Gen Qiu Fan Wu Kai Chen Li Wang A Robust Accuracy Weighted Random Forests Algorithm for IGBTs Fault Diagnosis in PWM Converters without Additional Sensors Applied Sciences three-phase PWM converter IGBT open-circuit fault fault diagnose robust accuracy weighted random forests |
| title | A Robust Accuracy Weighted Random Forests Algorithm for IGBTs Fault Diagnosis in PWM Converters without Additional Sensors |
| title_full | A Robust Accuracy Weighted Random Forests Algorithm for IGBTs Fault Diagnosis in PWM Converters without Additional Sensors |
| title_fullStr | A Robust Accuracy Weighted Random Forests Algorithm for IGBTs Fault Diagnosis in PWM Converters without Additional Sensors |
| title_full_unstemmed | A Robust Accuracy Weighted Random Forests Algorithm for IGBTs Fault Diagnosis in PWM Converters without Additional Sensors |
| title_short | A Robust Accuracy Weighted Random Forests Algorithm for IGBTs Fault Diagnosis in PWM Converters without Additional Sensors |
| title_sort | robust accuracy weighted random forests algorithm for igbts fault diagnosis in pwm converters without additional sensors |
| topic | three-phase PWM converter IGBT open-circuit fault fault diagnose robust accuracy weighted random forests |
| url | https://www.mdpi.com/2076-3417/12/4/2121 |
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