Modified triple modular redundancy based fault-tolerant three-phase matrix converter design with AI driven diagnostic capabilities
Three-Phase Matrix Converters (TPMCs) are mostly used in various potential applications, including aircraft, wind turbines, and AC drives. These system applications are endangered by severe disruptions due to critical faults in power electronic switches, which can cause whole system shutdowns and co...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-03-01
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| Series: | Results in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025005328 |
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| Summary: | Three-Phase Matrix Converters (TPMCs) are mostly used in various potential applications, including aircraft, wind turbines, and AC drives. These system applications are endangered by severe disruptions due to critical faults in power electronic switches, which can cause whole system shutdowns and component damage. To reduce these risks and ensure reliable operation under faulty conditions, a robust Fault-Tolerant Control System (FTCS) for TPMC is essential. This paper introduces the diagnosed open-circuit switches fault and fault-tolerant control for TPMCs. The Fault Detection and Isolation (FDI) unit is recommended for integration with the Artificial Neural Networks (ANN) based FTCS to identify and isolate defective switches. The FDI unit is additionally outfitted with Modified Triple Modular Redundancy (MTMR) to ensure fault tolerance. To prevent system shutdowns and ensure process continuity, this system can detect faulty switches, isolate them, and then replace them with redundant ones. This strategy mitigates the impact of defective components on the system's overall performance by ensuring their prompt identification and repair. The proposed FTCS, together with MTMR and a cutting-edge FDI unit, will significantly improve the reliability. The MATLAB/Simulink results indicate that the proposed system provides 99.9 % reliability and ANN's parallel computations allow the system to offer fast fault detection. Investigations in this domain have demonstrated that the proposed methodology is an effective means of maintaining operational continuity despite the existence of open circuit failures. |
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| ISSN: | 2590-1230 |