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<p>In this study, an intelligent fault diagnosis is introduced for the early identification of inter-turn short-circuit faults (ITSCF) in Permanent Magnet Synchronous Generators (PMSGs) deployed in wind power applications. The proposed method integrates the Negative Sequence Voltage (NSV) approach with a sliding-mode observer (SMO) to estimate the generator’s three-phase voltages without relying on physical voltage sensors. The results show that the NSV amplitude is a clear indicator of the presence of a fault, while its phase angle allows for accurate identification of the affected phase. Moreover, the proposed method was evaluated through simulations conducted under various fault conditions. By removing the dependency on direct voltage measurements, this technique offers a more robust and cost-effective solution for monitoring the health of wind turbine generators. Overall, the study introduces a practical, intelligent and efficient alternative to traditional sensor-based fault detection methods in PMSGs.</p>

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Sensorless inter-turn short-circuit fault diagnosis in PMSG wind turbine generators using a sliding-mode observer and negative sequence voltage analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月2日

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  • Issam Bahloul

摘要

In this study, an intelligent fault diagnosis is introduced for the early identification of inter-turn short-circuit faults (ITSCF) in Permanent Magnet Synchronous Generators (PMSGs) deployed in wind power applications. The proposed method integrates the Negative Sequence Voltage (NSV) approach with a sliding-mode observer (SMO) to estimate the generator’s three-phase voltages without relying on physical voltage sensors. The results show that the NSV amplitude is a clear indicator of the presence of a fault, while its phase angle allows for accurate identification of the affected phase. Moreover, the proposed method was evaluated through simulations conducted under various fault conditions. By removing the dependency on direct voltage measurements, this technique offers a more robust and cost-effective solution for monitoring the health of wind turbine generators. Overall, the study introduces a practical, intelligent and efficient alternative to traditional sensor-based fault detection methods in PMSGs.