Open-Circuit Fault Analysis and Recognition in Three-Level Inverters Based on Recurrence Plot and Convolution Neural Network
Power electronics is vital to modern infrastructure, but it is susceptible to open-circuit faults that can cause serious damage. Three-level inverters are commonly used in such equipment, but their high sensitivity and probability of failure make them particularly challenging to diagnose. In this gr...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi-Wiley
2023-01-01
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| Series: | International Transactions on Electrical Energy Systems |
| Online Access: | http://dx.doi.org/10.1155/2023/4755960 |
| _version_ | 1797662167315513344 |
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| author | Jianjun Yan Yanxing Huang Shuai Yuan Yufan Lu Zeyu Yu |
| author_facet | Jianjun Yan Yanxing Huang Shuai Yuan Yufan Lu Zeyu Yu |
| author_sort | Jianjun Yan |
| collection | DOAJ |
| description | Power electronics is vital to modern infrastructure, but it is susceptible to open-circuit faults that can cause serious damage. Three-level inverters are commonly used in such equipment, but their high sensitivity and probability of failure make them particularly challenging to diagnose. In this groundbreaking study, we present a new method for accurately detecting and locating open-circuit faults in three-level, neutral-clamped inverters. Using advanced simulation tools and nonlinear dynamic methods, we develop a new diagnostic model that outperforms existing fault classification algorithms. By converting the current signal into an unthreshold recurrence plot (URP) and mapping its nonlinear features to a two-dimensional plane, it is possible to extract key spatial information and train a residual neural network model for fault diagnosis. The method represents a major advance in power electronics and has the potential to save equipment from costly damage. By accurately detecting and locating open-circuit faults in three-level inverters, the reliability and safety of power electronics can be guaranteed for years to come. |
| first_indexed | 2024-03-11T18:56:10Z |
| format | Article |
| id | doaj.art-12f182740a4d441a9822af528f1b37da |
| institution | Directory Open Access Journal |
| issn | 2050-7038 |
| language | English |
| last_indexed | 2024-03-11T18:56:10Z |
| publishDate | 2023-01-01 |
| publisher | Hindawi-Wiley |
| record_format | Article |
| series | International Transactions on Electrical Energy Systems |
| spelling | doaj.art-12f182740a4d441a9822af528f1b37da2023-10-11T00:00:01ZengHindawi-WileyInternational Transactions on Electrical Energy Systems2050-70382023-01-01202310.1155/2023/4755960Open-Circuit Fault Analysis and Recognition in Three-Level Inverters Based on Recurrence Plot and Convolution Neural NetworkJianjun Yan0Yanxing Huang1Shuai Yuan2Yufan Lu3Zeyu Yu4Shanghai Key Laboratory of Intelligent Sensing and Detection TechnologySchool of Mechanical and Power EngineeringSchool of Mechanical and Power EngineeringSchool of Mechanical and Power EngineeringSchool of Mechanical and Power EngineeringPower electronics is vital to modern infrastructure, but it is susceptible to open-circuit faults that can cause serious damage. Three-level inverters are commonly used in such equipment, but their high sensitivity and probability of failure make them particularly challenging to diagnose. In this groundbreaking study, we present a new method for accurately detecting and locating open-circuit faults in three-level, neutral-clamped inverters. Using advanced simulation tools and nonlinear dynamic methods, we develop a new diagnostic model that outperforms existing fault classification algorithms. By converting the current signal into an unthreshold recurrence plot (URP) and mapping its nonlinear features to a two-dimensional plane, it is possible to extract key spatial information and train a residual neural network model for fault diagnosis. The method represents a major advance in power electronics and has the potential to save equipment from costly damage. By accurately detecting and locating open-circuit faults in three-level inverters, the reliability and safety of power electronics can be guaranteed for years to come.http://dx.doi.org/10.1155/2023/4755960 |
| spellingShingle | Jianjun Yan Yanxing Huang Shuai Yuan Yufan Lu Zeyu Yu Open-Circuit Fault Analysis and Recognition in Three-Level Inverters Based on Recurrence Plot and Convolution Neural Network International Transactions on Electrical Energy Systems |
| title | Open-Circuit Fault Analysis and Recognition in Three-Level Inverters Based on Recurrence Plot and Convolution Neural Network |
| title_full | Open-Circuit Fault Analysis and Recognition in Three-Level Inverters Based on Recurrence Plot and Convolution Neural Network |
| title_fullStr | Open-Circuit Fault Analysis and Recognition in Three-Level Inverters Based on Recurrence Plot and Convolution Neural Network |
| title_full_unstemmed | Open-Circuit Fault Analysis and Recognition in Three-Level Inverters Based on Recurrence Plot and Convolution Neural Network |
| title_short | Open-Circuit Fault Analysis and Recognition in Three-Level Inverters Based on Recurrence Plot and Convolution Neural Network |
| title_sort | open circuit fault analysis and recognition in three level inverters based on recurrence plot and convolution neural network |
| url | http://dx.doi.org/10.1155/2023/4755960 |
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