Anomaly Detection for Wind Turbines Using Long Short-Term Memory-Based Variational Autoencoder Wasserstein Generation Adversarial Network under Semi-Supervised Training

Anomaly Detection for Wind Turbines Using Long Short-Term Memory-Based Variational Autoencoder Wasserstein Generation Adversarial Network under Semi-Supervised Training

Intelligent anomaly detection for wind turbines using deep-learning methods has been extensively researched and yielded significant results. However, supervised learning necessitates sufficient labeled data to establish the discriminant boundary, while unsupervised learning lacks prior knowledge and...

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Bibliographic Details
Main Authors: Chen Zhang, Tao Yang
Format: Article
Language:English
Published: MDPI AG 2023-10-01
Series:Energies
Subjects:
wind turbine
anomaly detection
long short-term memory-based (LSTM-based)
variational autoencoder Wasserstein generation adversarial network (VAE-WGAN)
semi-supervised training
Online Access:https://www.mdpi.com/1996-1073/16/19/7008

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https://www.mdpi.com/1996-1073/16/19/7008

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