Deep learning-based fuzzy decision support system-based fault diagnosis of wind turbine generators in electrical machines

The article introduces a novel hybrid decision support system (HDSS) that integrates deep learning and fuzzy optimization techniques for the fault diagnosis of wind turbine generators. This system enhances the detection of rotor faults, such as broken bars and induction errors, by analyzing rotor speed and vibration data. The HDSS improves fault detection accuracy and reduces energy differences, leading to optimized wind turbine performance and minimized downtime. The proposed method outperforms traditional methods by providing more accurate and timely fault detection, thereby enhancing the reliability and efficiency of wind turbine operations. The article also discusses the advantages of the HDSS over existing methods, including improved fault detection, proactive decision-making, and enhanced energy efficiency. The system's potential social benefits include improved wind turbine reliability and operational efficiency, leading to more consistent renewable energy generation and reduced maintenance costs. The article concludes by highlighting the future work possibilities for further optimizing the HDSS and integrating it with environmental tracking systems and predictive repair plans.