IEICE TRANSACTIONS on Information
Anomaly Prediction for Wind Turbines Using an Autoencoder with Vibration Data Supported by Power-Curve Filtering
Summary :
The prediction of the malfunction timing of wind turbines is essential for maintaining the high profitability of the wind power generation industry. Studies have been conducted on machine learning methods that use condition monitoring system data, such as vibration data, and supervisory control and data acquisition (SCADA) data to detect and predict anomalies in wind turbines automatically. Autoencoder-based techniques that use unsupervised learning where the anomaly pattern is unknown have attracted significant interest in the area of anomaly detection and prediction. In particular, vibration data are considered useful because they include the changes that occur in the early stages of a malfunction. However, when autoencoder-based techniques are applied for prediction purposes, in the training process it is difficult to distinguish the difference between operating and non-operating condition data, which leads to the degradation of the prediction performance. In this letter, we propose a method in which both vibration data and SCADA data are utilized to improve the prediction performance, namely, a method that uses a power curve composed of active power and wind speed. We evaluated the method's performance using vibration and SCADA data obtained from an actual wind farm.
- Publication
- IEICE TRANSACTIONS on Information Vol.E105-D No.3 pp.732-735
- Publication Date
- 2022/03/01
- Publicized
- 2021/12/07
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2021EDL8089
- Type of Manuscript
- LETTER
- Category
- Artificial Intelligence, Data Mining
Authors
Masaki TAKANASHI
Toyota Central Research and Development Laboratories Incorporated
Shu-ichi SATO
Toyota Central Research and Development Laboratories Incorporated
Kentaro INDO
Eurus Technical Service Corporation
Nozomu NISHIHARA
Eurus Technical Service Corporation
Hiroki HAYASHI
Eurus Technical Service Corporation
Toru SUZUKI
Toyota Tsusho Corporation, Tokyo
Keyword
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Masaki TAKANASHI, Shu-ichi SATO, Kentaro INDO, Nozomu NISHIHARA, Hiroki HAYASHI, Toru SUZUKI, "Anomaly Prediction for Wind Turbines Using an Autoencoder with Vibration Data Supported by Power-Curve Filtering" in IEICE TRANSACTIONS on Information,
vol. E105-D, no. 3, pp. 732-735, March 2022, doi: 10.1587/transinf.2021EDL8089.
Abstract: The prediction of the malfunction timing of wind turbines is essential for maintaining the high profitability of the wind power generation industry. Studies have been conducted on machine learning methods that use condition monitoring system data, such as vibration data, and supervisory control and data acquisition (SCADA) data to detect and predict anomalies in wind turbines automatically. Autoencoder-based techniques that use unsupervised learning where the anomaly pattern is unknown have attracted significant interest in the area of anomaly detection and prediction. In particular, vibration data are considered useful because they include the changes that occur in the early stages of a malfunction. However, when autoencoder-based techniques are applied for prediction purposes, in the training process it is difficult to distinguish the difference between operating and non-operating condition data, which leads to the degradation of the prediction performance. In this letter, we propose a method in which both vibration data and SCADA data are utilized to improve the prediction performance, namely, a method that uses a power curve composed of active power and wind speed. We evaluated the method's performance using vibration and SCADA data obtained from an actual wind farm.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2021EDL8089/_p
Copy
@ARTICLE{e105-d_3_732,
author={Masaki TAKANASHI, Shu-ichi SATO, Kentaro INDO, Nozomu NISHIHARA, Hiroki HAYASHI, Toru SUZUKI, },
journal={IEICE TRANSACTIONS on Information},
title={Anomaly Prediction for Wind Turbines Using an Autoencoder with Vibration Data Supported by Power-Curve Filtering},
year={2022},
volume={E105-D},
number={3},
pages={732-735},
abstract={The prediction of the malfunction timing of wind turbines is essential for maintaining the high profitability of the wind power generation industry. Studies have been conducted on machine learning methods that use condition monitoring system data, such as vibration data, and supervisory control and data acquisition (SCADA) data to detect and predict anomalies in wind turbines automatically. Autoencoder-based techniques that use unsupervised learning where the anomaly pattern is unknown have attracted significant interest in the area of anomaly detection and prediction. In particular, vibration data are considered useful because they include the changes that occur in the early stages of a malfunction. However, when autoencoder-based techniques are applied for prediction purposes, in the training process it is difficult to distinguish the difference between operating and non-operating condition data, which leads to the degradation of the prediction performance. In this letter, we propose a method in which both vibration data and SCADA data are utilized to improve the prediction performance, namely, a method that uses a power curve composed of active power and wind speed. We evaluated the method's performance using vibration and SCADA data obtained from an actual wind farm.},
keywords={},
doi={10.1587/transinf.2021EDL8089},
ISSN={1745-1361},
month={March},}
Copy
TY - JOUR
TI - Anomaly Prediction for Wind Turbines Using an Autoencoder with Vibration Data Supported by Power-Curve Filtering
T2 - IEICE TRANSACTIONS on Information
SP - 732
EP - 735
AU - Masaki TAKANASHI
AU - Shu-ichi SATO
AU - Kentaro INDO
AU - Nozomu NISHIHARA
AU - Hiroki HAYASHI
AU - Toru SUZUKI
PY - 2022
DO - 10.1587/transinf.2021EDL8089
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E105-D
IS - 3
JA - IEICE TRANSACTIONS on Information
Y1 - March 2022
AB - The prediction of the malfunction timing of wind turbines is essential for maintaining the high profitability of the wind power generation industry. Studies have been conducted on machine learning methods that use condition monitoring system data, such as vibration data, and supervisory control and data acquisition (SCADA) data to detect and predict anomalies in wind turbines automatically. Autoencoder-based techniques that use unsupervised learning where the anomaly pattern is unknown have attracted significant interest in the area of anomaly detection and prediction. In particular, vibration data are considered useful because they include the changes that occur in the early stages of a malfunction. However, when autoencoder-based techniques are applied for prediction purposes, in the training process it is difficult to distinguish the difference between operating and non-operating condition data, which leads to the degradation of the prediction performance. In this letter, we propose a method in which both vibration data and SCADA data are utilized to improve the prediction performance, namely, a method that uses a power curve composed of active power and wind speed. We evaluated the method's performance using vibration and SCADA data obtained from an actual wind farm.
ER -
English
