Modified triple modular redundancy based fault-tolerant three-phase matrix converter design with AI driven diagnostic capabilities

• This paper introduces an MTMR-based robust FTCS for TPMC employing ANN for fault diagnosis. • The simulation in MATLAB/Simulink environment shows that the system provides 99.9 % reliability and rapid fault identification due to the ANN's parallel computation capabilities. • The combination of FTCS, MTMR, and an advanced FDI unit enhances system durability and robustness. 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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Publication Details

Journal
Results in Engineering
Published
2025-02-28
DOI
https://doi.org/10.1016/j.rineng.2025.104454
Citations
2
Primary Topic
Multilevel Inverters and Converters
Type
article
Field-Weighted Citation Impact
1.46

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article

Modified triple modular redundancy based fault-tolerant three-phase matrix converter design with AI driven diagnostic capabilities

Mohaira Ahmad, Arslan Ahmed Amin, Sami ud Din, Faizan Ahmad et al.
2 citations
Results in Engineering
Multilevel Inverters and Converters
1.46
article

Modified triple modular redundancy based fault-tolerant three-phase matrix converter design with AI driven diagnostic capabilities

Mohaira Ahmad, Arslan Ahmed Amin, Sami ud Din, Faizan Ahmad, Faisal Hayat, Muhammad Adnan
article en
2 citations

Abstract

• This paper introduces an MTMR-based robust FTCS for TPMC employing ANN for fault diagnosis. • The simulation in MATLAB/Simulink environment shows that the system provides 99.9 % reliability and rapid fault identification due to the ANN's parallel computation capabilities. • The combination of FTCS, MTMR, and an advanced FDI unit enhances system durability and robustness. 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.

Results in EngineeringVol. 25
National University of Computer and Emerging Sciences (PK), Namal University (PK), National University of Sciences and Technology (PK)
National University of Sciences and Technology, School of Electronic Engineering and Computer Science, Queen Mary University of London
Openalex Percentile: Top 19%
Multilevel Inverters and Converters
1.46
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