Data-driven fault diagnosis in the converter system
In modern power electronics industry, three-phase pulse width modulation (PWM) converter has been widely applied due to the noise resistance and economic efficiency. To guarantee the steady working state of converter, fault diagnosis method has become an essential and popular area. In this paper, a...
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| Other Authors: | |
| Format: | Thesis |
| Language: | English |
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2019
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| Online Access: | https://hdl.handle.net/10356/106442 http://hdl.handle.net/10220/47916 |
| _version_ | 1811687161168134144 |
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| author | Xia, Yang |
| author2 | Xu Yan |
| author_facet | Xu Yan Xia, Yang |
| author_sort | Xia, Yang |
| collection | NTU |
| description | In modern power electronics industry, three-phase pulse width modulation (PWM) converter has been widely applied due to the noise resistance and economic efficiency. To guarantee the steady working state of converter, fault diagnosis method has become an essential and popular area. In this paper, a data-driven methodology based on randomized learning algorithm is proposed to diagnose insulated gate bipolar transistor (IGBT) open-circuit (OC). To exclude the disturbance of frequency variation, RELIEFF algorithm is combined with Fast Fourier Transform (FFT) to select certain frequency components as input. In order to improve diagnosis performance, the hybrid ensemble time-adaptive model is designed to balance the tradeoff between diagnostic accuracy and time. Besides, parameters in this diagnosis model are optimized by multi-objective programming. Furthermore, parameters are selected from solution set of Pareto Optimal Front (POF), which is able to satisfy multiple practical requirements in online application. Simulated and experimental data are utilized in offline model training, as well as validate online performance of the proposed fault diagnosis method. |
| first_indexed | 2024-10-01T05:11:54Z |
| format | Thesis |
| id | ntu-10356/106442 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:11:54Z |
| publishDate | 2019 |
| record_format | dspace |
| spelling | ntu-10356/1064422023-07-04T16:50:16Z Data-driven fault diagnosis in the converter system Xia, Yang Xu Yan School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Power electronics In modern power electronics industry, three-phase pulse width modulation (PWM) converter has been widely applied due to the noise resistance and economic efficiency. To guarantee the steady working state of converter, fault diagnosis method has become an essential and popular area. In this paper, a data-driven methodology based on randomized learning algorithm is proposed to diagnose insulated gate bipolar transistor (IGBT) open-circuit (OC). To exclude the disturbance of frequency variation, RELIEFF algorithm is combined with Fast Fourier Transform (FFT) to select certain frequency components as input. In order to improve diagnosis performance, the hybrid ensemble time-adaptive model is designed to balance the tradeoff between diagnostic accuracy and time. Besides, parameters in this diagnosis model are optimized by multi-objective programming. Furthermore, parameters are selected from solution set of Pareto Optimal Front (POF), which is able to satisfy multiple practical requirements in online application. Simulated and experimental data are utilized in offline model training, as well as validate online performance of the proposed fault diagnosis method. Master of Engineering 2019-03-28T01:59:15Z 2019-12-06T22:11:50Z 2019-03-28T01:59:15Z 2019-12-06T22:11:50Z 2019 Thesis Xia, Y. (2019). Data-driven fault diagnosis in the converter system. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/106442 http://hdl.handle.net/10220/47916 10.32657/10220/47916 en 88 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering::Power electronics Xia, Yang Data-driven fault diagnosis in the converter system |
| title | Data-driven fault diagnosis in the converter system |
| title_full | Data-driven fault diagnosis in the converter system |
| title_fullStr | Data-driven fault diagnosis in the converter system |
| title_full_unstemmed | Data-driven fault diagnosis in the converter system |
| title_short | Data-driven fault diagnosis in the converter system |
| title_sort | data driven fault diagnosis in the converter system |
| topic | DRNTU::Engineering::Electrical and electronic engineering::Power electronics |
| url | https://hdl.handle.net/10356/106442 http://hdl.handle.net/10220/47916 |
| work_keys_str_mv | AT xiayang datadrivenfaultdiagnosisintheconvertersystem |