Enhanced fault diagnosis of wind energy conversion systems using ensemble learning based on sine cosine algorithm

Enhanced fault diagnosis of wind energy conversion systems using ensemble learning based on sine cosine algorithm

Abstract This paper investigates the problem of incipient fault detection and diagnosis (FDD) in wind energy conversion systems (WECS) using an innovative and effective approach called the ensemble learning-sine cosine optimization algorithm (EL-SCOA). The evolved strategy involves two primary steps...

Full description

Bibliographic Details
Main Authors: Khadija Attouri, Khaled Dhibi, Majdi Mansouri, Mansour Hajji, Kais Bouzrara, Hazem Nounou
Format: Article
Language:English
Published: SpringerOpen 2023-06-01
Series:Journal of Engineering and Applied Science
Subjects:
Wind energy conversion (WEC) systems
Fault diagnosis
Machine learning (ML)
Sine-cosine optimization algorithm (SCOA)
Feature selection
Feature extraction
Online Access:https://doi.org/10.1186/s44147-023-00227-3

Internet

https://doi.org/10.1186/s44147-023-00227-3

Similar Items