Natural Fault-Tolerant Model-Free Predictive Flux Control in Five-Phase PMSM Drives Under Any-Phase Open-Circuit Fault
The fault-tolerant control strategies always need to not only diagnose open-circuit fault phase but also reconfigure transformation matrix and control structure according to specific fault phase, thus increasing complexity of control algorithm. Furthermore, the parameters of five-phase permanent mag...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10890945/ |
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| Summary: | The fault-tolerant control strategies always need to not only diagnose open-circuit fault phase but also reconfigure transformation matrix and control structure according to specific fault phase, thus increasing complexity of control algorithm. Furthermore, the parameters of five-phase permanent magnet synchronous motor (PMSM) are susceptible to perturbations under open-circuit fault condition, thus deteriorating operational performance. To address these issues, a natural fault-tolerant model-free predictive flux control (NFT-MFPFC) was proposed. The novelty of proposed strategy lies in the development of an ultra-local model with constant regulation coefficient, and a new extended state observer (ESO) which employed to estimate the stator inductance and the disturbances caused by any-phase open-circuit fault. Based on these, the faulty motor with model-free predictive control can not only operate smoothly under any-phase open-circuit fault condition without fault diagnosis and reconfiguration of control system and transformation matrix, but also have none adjustment of ultra-local model parameter and strong robustness even in the case of parameter mismatch. In addition, the virtual voltage vectors were adopted to restrain the third harmonic currents under both healthy and open-circuit fault conditions. The experimental results were presented to verify the feasibility of proposed strategy. |
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| ISSN: | 2169-3536 |