Wind Turbine Blade Breakage Monitoring With Mogrifier LSTM Autoencoder

Wind turbines (WTs) often work in harsh environmental conditions. The risk of blade breakage by hitting the tower has increased because of the decreased bending stiffness of the WT blades with the increasing sizes of WTs. To address the system dynamics of WTs, this article proposes a Mogrifier long...

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Vydáno v:	IEEE transactions on instrumentation and measurement Ročník 72; s. 1 - 10
Hlavní autoři:	Wu, Ping, Wang, Yixuan, Zhang, Xujie, Gao, Jinfeng, Wang, Lin, Liu, Yichao
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Blade breakage monitoring Blades Correlation coefficients Data models False alarms Feature extraction long short-term memory (LSTM) mogrifier long short-term memory autoencoder (MLSTM-AE) Monitoring Sensors Supervisory control and data acquisition supervisory control and data acquisition (SCADA) data System dynamics Turbine blades Wind farms Wind power wind turbine (WT) Wind turbines
ISSN:	0018-9456, 1557-9662
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Abstract	Wind turbines (WTs) often work in harsh environmental conditions. The risk of blade breakage by hitting the tower has increased because of the decreased bending stiffness of the WT blades with the increasing sizes of WTs. To address the system dynamics of WTs, this article proposes a Mogrifier long short-term memory autoencoder (MLSTM-AE) method to monitor blade breakage using the supervisory control and data acquisition (SCADA) data. Firstly, the Pearson correlation coefficient (PCC) is calculated for variable selection. Secondly, the time-lagged multivariate variables are augmented and taken as input for the encoder which consists of a Mogrifier long short-term memory (MLSTM) layer to learn the deep features. Another MLSTM layer runs as the decoder to reconstruct the input. The proposed MLSTM-AE model can extract spatial-temporal information more effectively than traditional long short-term memory (LSTM) and autoencoder (AE). Next, kernel density estimation (KDE) is applied to develop boundaries for generating blade breakage alerts based on reconstruction errors, which can reflect changes in system dynamics caused by blade breakage. The advantages of the proposed MLSTM-AE-based monitoring method are illustrated by employing real blade breakage cases from several wind farms located in China by comparing with other related methods in terms of warning time, false alarm rate (FAR), and accuracy.
AbstractList	Wind turbines (WTs) often work in harsh environmental conditions. The risk of blade breakage by hitting the tower has increased because of the decreased bending stiffness of the WT blades with the increasing sizes of WTs. To address the system dynamics of WTs, this article proposes a Mogrifier long short-term memory autoencoder (MLSTM-AE) method to monitor blade breakage using the supervisory control and data acquisition (SCADA) data. Firstly, the Pearson correlation coefficient (PCC) is calculated for variable selection. Secondly, the time-lagged multivariate variables are augmented and taken as input for the encoder which consists of a Mogrifier long short-term memory (MLSTM) layer to learn the deep features. Another MLSTM layer runs as the decoder to reconstruct the input. The proposed MLSTM-AE model can extract spatial-temporal information more effectively than traditional long short-term memory (LSTM) and autoencoder (AE). Next, kernel density estimation (KDE) is applied to develop boundaries for generating blade breakage alerts based on reconstruction errors, which can reflect changes in system dynamics caused by blade breakage. The advantages of the proposed MLSTM-AE-based monitoring method are illustrated by employing real blade breakage cases from several wind farms located in China by comparing with other related methods in terms of warning time, false alarm rate (FAR), and accuracy.
Author	Wu, Ping Wang, Yixuan Wang, Lin Gao, Jinfeng Zhang, Xujie Liu, Yichao
Author_xml	– sequence: 1 givenname: Ping orcidid: 0000-0002-2729-9669 surname: Wu fullname: Wu, Ping email: pingwu@zstu.edu.cn organization: School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou, China – sequence: 2 givenname: Yixuan orcidid: 0000-0003-2767-8244 surname: Wang fullname: Wang, Yixuan organization: School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou, China – sequence: 3 givenname: Xujie orcidid: 0000-0002-8293-7539 surname: Zhang fullname: Zhang, Xujie email: xujie_zhang@zju.edu.cn organization: College of Control Science and Engineering, Zhejiang University, Hangzhou, China – sequence: 4 givenname: Jinfeng orcidid: 0000-0002-7837-7559 surname: Gao fullname: Gao, Jinfeng organization: School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou, China – sequence: 5 givenname: Lin orcidid: 0000-0001-8850-9386 surname: Wang fullname: Wang, Lin organization: Key Laboratory of Wind Power Technology of Zhejiang Province, Zhejiang Windey Company Ltd., Hangzhou, China – sequence: 6 givenname: Yichao orcidid: 0000-0002-4175-7638 surname: Liu fullname: Liu, Yichao organization: Energy and Materials Transition, TNO, The Hague, The Netherlands
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SubjectTerms	Blade breakage monitoring Blades Correlation coefficients Data models False alarms Feature extraction long short-term memory (LSTM) mogrifier long short-term memory autoencoder (MLSTM-AE) Monitoring Sensors Supervisory control and data acquisition supervisory control and data acquisition (SCADA) data System dynamics Turbine blades Wind farms Wind power wind turbine (WT) Wind turbines
Title	Wind Turbine Blade Breakage Monitoring With Mogrifier LSTM Autoencoder
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