Data-driven methods for power converter fault diagnosis
This Final Year Project (FYP) report investigates the efficacy of data-driven methods for power converter fault analysis, employing three distinct algorithms: Extreme Learning Machine (ELM), Random Vector Functional Link (RVFL), and Random Forest. The study aims to assess the performance of these al...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2024
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| Online Access: | https://hdl.handle.net/10356/176909 |
| _version_ | 1811683661912735744 |
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| author | Shahzrul Ramadzan Bin Johar |
| author2 | Xu Yan |
| author_facet | Xu Yan Shahzrul Ramadzan Bin Johar |
| author_sort | Shahzrul Ramadzan Bin Johar |
| collection | NTU |
| description | This Final Year Project (FYP) report investigates the efficacy of data-driven methods for power converter fault analysis, employing three distinct algorithms: Extreme Learning Machine (ELM), Random Vector Functional Link (RVFL), and Random Forest. The study aims to assess the performance of these algorithms in detecting faults under varying noise conditions. The experimental setup involves testing the algorithms against different noise samples to simulate real-world scenarios. Through comprehensive analysis and evaluation, it is concluded that the ELM algorithm exhibits superior robustness in handling noisy data compared to RVFL and Random Forest. The findings of this study contribute to the advancement of fault analysis techniques in power converters and highlight the effectiveness of ELM as a reliable method for fault detection in noisy environments. |
| first_indexed | 2024-10-01T04:16:17Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/176909 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:16:17Z |
| publishDate | 2024 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1769092024-05-24T15:45:23Z Data-driven methods for power converter fault diagnosis Shahzrul Ramadzan Bin Johar Xu Yan School of Electrical and Electronic Engineering xuyan@ntu.edu.sg Engineering This Final Year Project (FYP) report investigates the efficacy of data-driven methods for power converter fault analysis, employing three distinct algorithms: Extreme Learning Machine (ELM), Random Vector Functional Link (RVFL), and Random Forest. The study aims to assess the performance of these algorithms in detecting faults under varying noise conditions. The experimental setup involves testing the algorithms against different noise samples to simulate real-world scenarios. Through comprehensive analysis and evaluation, it is concluded that the ELM algorithm exhibits superior robustness in handling noisy data compared to RVFL and Random Forest. The findings of this study contribute to the advancement of fault analysis techniques in power converters and highlight the effectiveness of ELM as a reliable method for fault detection in noisy environments. Bachelor's degree 2024-05-23T05:03:13Z 2024-05-23T05:03:13Z 2024 Final Year Project (FYP) Shahzrul Ramadzan Bin Johar (2024). Data-driven methods for power converter fault diagnosis. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/176909 https://hdl.handle.net/10356/176909 en A1152-231 application/pdf Nanyang Technological University |
| spellingShingle | Engineering Shahzrul Ramadzan Bin Johar Data-driven methods for power converter fault diagnosis |
| title | Data-driven methods for power converter fault diagnosis |
| title_full | Data-driven methods for power converter fault diagnosis |
| title_fullStr | Data-driven methods for power converter fault diagnosis |
| title_full_unstemmed | Data-driven methods for power converter fault diagnosis |
| title_short | Data-driven methods for power converter fault diagnosis |
| title_sort | data driven methods for power converter fault diagnosis |
| topic | Engineering |
| url | https://hdl.handle.net/10356/176909 |
| work_keys_str_mv | AT shahzrulramadzanbinjohar datadrivenmethodsforpowerconverterfaultdiagnosis |