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...

وصف كامل

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)
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