Effective Fault Detection and Diagnosis for Power Converters in Wind Turbine Systems Using KPCA-Based BiLSTM

Effective Fault Detection and Diagnosis for Power Converters in Wind Turbine Systems Using KPCA-Based BiLSTM

<p dir="ltr">The current work presents an effective fault detection and diagnosis (FDD) technique in wind energy converter (WEC) systems. The proposed FDD framework merges the benefits of kernel principal component analysis (KPCA) model and the bidirectional long short-term memory (B...

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محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Zahra Yahyaoui (17821208) (author)
مؤلفون آخرون:	Mansour Hajji (16869894) (author), Majdi Mansouri (16869885) (author), Kamaleldin Abodayeh (16904865) (author), Kais Bouzrara (16869906) (author), Hazem Nounou (16869900) (author)
منشور في:	2022
الموضوعات:	Engineering Control engineering, mechatronics and robotics Electrical engineering Information and computing sciences Artificial intelligence Machine learning wind energy conversion (WEC) fault detection and diagnosis (FDD) kernel PCA (KPCA) bidirectional long short term memory (BiLSTM)
الوسوم:	إضافة وسم لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!

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