@inproceedings{c7cdfc3e6ab24ae3873cb577e17380dc,
title = "Predictive Maintenance and Anomaly Detection of Wind Turbines Based on Bladed Simulator Models",
abstract = "This paper proposes a novel data-driven scheme for condition monitoring and detecting anomalies in the wind turbine critical components based on advanced deep learning algorithms. The proposed method employs time-series data taken from a Bladed simulator model for the 5MW wind turbine. To emulate the characteristic behavior of essential wind turbine components, we develop supervised and unsupervised deep learning models using self-organizing map (SOM), long short-term memory auto encoder (LSTM-AE), and long short-term memory recurrent neural network (LSTM-RNN). Statistical process control(SPC) charts are used to evaluate the anomalous behavior predicted by the developed data-driven models. The proposed method is tested on a Bladed 5MW wind turbine model with 24 m/sec wind speed for validating its accuracy and applicability.",
keywords = "Wind turbine, anomaly detection, fault prediction, long short-term memory auto encoder (LSTM-AE), recurrent neural networks (RNNs), self-organizing maps (SOM)",
author = "Rama, {V. Siva Brahmaiah} and Hur, {Sung Ho} and Yang, {Jung Min}",
note = "Publisher Copyright: Copyright {\textcopyright} 2023 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/); 22nd IFAC World Congress ; Conference date: 09-07-2023 Through 14-07-2023",
year = "2023",
month = jul,
day = "1",
doi = "10.1016/j.ifacol.2023.10.974",
language = "English",
series = "IFAC-PapersOnLine",
publisher = "Elsevier B.V.",
number = "2",
pages = "4633--4638",
editor = "Hideaki Ishii and Yoshio Ebihara and Jun-ichi Imura and Masaki Yamakita",
booktitle = "IFAC-PapersOnLine",
address = "Netherlands",
edition = "2",
}