Anomaly detection and diagnosis for wind turbines using long short-term memory-based stacked denoising autoencoders and XGBoost

C Zhang, D Hu, T Yang - Reliability Engineering & System Safety, 2022 - Elsevier
An anomaly detection and diagnosis method for wind turbines using long short-term memory-
based stacked denoising autoencoders (LSTM-SDAE) and extreme gradient boosting …
被引用次数：113 相关文章 所有 3 个版本

Anomaly detection for wind turbines based on the reconstruction of condition parameters using stacked denoising autoencoders

J Chen, J Li, W Chen, Y Wang, T Jiang - Renewable Energy, 2020 - Elsevier
This paper proposes an approach for detecting anomalies in a wind turbine (WT) based on
multivariate analysis. Firstly, the stacked denoising autoencoders (SDAE) model with …
被引用次数：88 相关文章 所有 6 个版本

Anomaly detection and critical SCADA parameters identification for wind turbines based on LSTM-AE neural network

H Chen, H Liu, X Chu, Q Liu, D Xue - Renewable Energy, 2021 - Elsevier
Continuous monitoring of wind turbine health conditions using anomaly detection methods
can improve the reliability and reduce maintenance costs during operation of wind turbine …
被引用次数：161 相关文章 所有 5 个版本

System-wide anomaly detection in wind turbines using deep autoencoders

N Renström, P Bangalore, E Highcock - Renewable Energy, 2020 - Elsevier
Using supervisory control and data acquisition (SCADA) data to detect faults in wind
turbines (WTs) has gained interest over the last few years. The SCADA system is installed by …
被引用次数：71 相关文章 所有 6 个版本

Anomaly detection and fault analysis of wind turbine components based on deep learning network

H Zhao, H Liu, W Hu, X Yan - Renewable energy, 2018 - Elsevier
Continuous monitoring of wind turbine health using early fault detection methods can
improve turbine reliability and reduce maintenance costs before they reach a catastrophic …
被引用次数：300 相关文章 所有 7 个版本

Wind turbine anomaly detection based on SCADA: A deep autoencoder enhanced by fault instances

J Liu, G Yang, X Li, Q Wang, Y He, X Yang - ISA transactions, 2023 - Elsevier
An increasing number of deep autoencoder-based algorithms for intelligent condition
monitoring and anomaly detection have been reported in recent years to improve wind …
被引用次数：19 相关文章 所有 3 个版本

Semi-supervised multivariate time series anomaly detection for wind turbines using generator SCADA data

M Zheng, J Man, D Wang, Y Chen, Q Li, Y Liu - Reliability Engineering & …, 2023 - Elsevier
The maintenance cost and unplanned downtime caused by faults are an important part of
the operation cost of wind turbines. Supervisory control and data acquisition (SCADA) data …
被引用次数：29 相关文章 所有 3 个版本

A new auto-encoder-based dynamic threshold to reduce false alarm rate for anomaly detection of steam turbines

JU Ko, K Na, JS Oh, J Kim, BD Youn - Expert Systems with Applications, 2022 - Elsevier
This study proposes an ensemble denoising auto-encoder-based dynamic threshold (EDAE-
DT) to overcome the false alarm issue in anomaly detection. The proposed ensemble …
被引用次数：43 相关文章 所有 2 个版本

Sparse dictionary learning based adversarial variational auto-encoders for fault identification of wind turbines

X Liu, W Teng, S Wu, X Wu, Y Liu, Z Ma - Measurement, 2021 - Elsevier
Incipient anomaly state identification of wind turbines is beneficial to improve the reliability of
wind turbines, reduce operation and maintenance costs. Due to various reasons such as …
被引用次数：37 相关文章
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[HTML][HTML] Deep anomaly detection in horizontal axis wind turbines using graph convolutional autoencoders for multivariate time series

ES Miele, F Bonacina, A Corsini - Energy and AI, 2022 - Elsevier
Wind power is one of the fastest-growing renewable energy sectors instrumental in the
ongoing decarbonization process. However, wind turbines are subjected to a wide range of …
被引用次数：50 相关文章 所有 4 个版本