SCADA Data-Based Working Condition Classification for Condition Assessment of Wind Turbine Main Transmission System

Due to the complex and variable conditions under which wind turbines operate, existing working condition classification methods are inadequate for condition assessment of the main transmission system. Because working conditions are too few after classification, it cannot effectively describe the com...

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Autores principales:	Huanguo Chen, Chao Xie, Juchuan Dai, Enjie Cen, Jianmin Li
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	main transmission system working conditions classification wind turbine working characteristics alarm threshold <i>k</i>-means clustering SCADA data Technology T
Acceso en línea:	https://doaj.org/article/b968fce88f244271b647634af552df54
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spelling	oai:doaj.org-article:b968fce88f244271b647634af552df542021-11-11T15:51:25ZSCADA Data-Based Working Condition Classification for Condition Assessment of Wind Turbine Main Transmission System10.3390/en142170431996-1073https://doaj.org/article/b968fce88f244271b647634af552df542021-10-01T00:00:00Zhttps://www.mdpi.com/1996-1073/14/21/7043https://doaj.org/toc/1996-1073Due to the complex and variable conditions under which wind turbines operate, existing working condition classification methods are inadequate for condition assessment of the main transmission system. Because working conditions are too few after classification, it cannot effectively describe the complex and variable working conditions of wind turbine. This can lead to high false-alarm rates in the condition monitoring, which affect normal operations. This paper proposes a working condition classification method for the main transmission system of wind turbines based on supervisory control and data acquisition (SCADA) data. Firstly, correlation analysis of SCADA data acquired by wind farm is used to select the parameters relevant to the main transmission system. Secondly, according to the wind turbine control principle, the working conditions are initially divided into four phases: shutdown, start-up, maximum wind energy tracking, and constant speed. The <i>k</i>-means clustering algorithm is used to subdivide the maximum wind energy-tracking phase and constant speed phase, which account for a larger proportion of the working conditions, to achieve better classification. Finally, a case study is used to demonstrate the calculation of alarm thresholds and alarm rates for each working condition. The results are compared with the direct use of <i>k</i>-means clustering for working condition classification. It is concluded that the proposed method can significantly reduce the false-alarm rate of the vibration detection process.Huanguo ChenChao XieJuchuan DaiEnjie CenJianmin LiMDPI AGarticlemain transmission systemworking conditions classificationwind turbine working characteristicsalarm threshold<i>k</i>-means clusteringSCADA dataTechnologyTENEnergies, Vol 14, Iss 7043, p 7043 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	main transmission system working conditions classification wind turbine working characteristics alarm threshold <i>k</i>-means clustering SCADA data Technology T
spellingShingle	main transmission system working conditions classification wind turbine working characteristics alarm threshold <i>k</i>-means clustering SCADA data Technology T Huanguo Chen Chao Xie Juchuan Dai Enjie Cen Jianmin Li SCADA Data-Based Working Condition Classification for Condition Assessment of Wind Turbine Main Transmission System
description	Due to the complex and variable conditions under which wind turbines operate, existing working condition classification methods are inadequate for condition assessment of the main transmission system. Because working conditions are too few after classification, it cannot effectively describe the complex and variable working conditions of wind turbine. This can lead to high false-alarm rates in the condition monitoring, which affect normal operations. This paper proposes a working condition classification method for the main transmission system of wind turbines based on supervisory control and data acquisition (SCADA) data. Firstly, correlation analysis of SCADA data acquired by wind farm is used to select the parameters relevant to the main transmission system. Secondly, according to the wind turbine control principle, the working conditions are initially divided into four phases: shutdown, start-up, maximum wind energy tracking, and constant speed. The <i>k</i>-means clustering algorithm is used to subdivide the maximum wind energy-tracking phase and constant speed phase, which account for a larger proportion of the working conditions, to achieve better classification. Finally, a case study is used to demonstrate the calculation of alarm thresholds and alarm rates for each working condition. The results are compared with the direct use of <i>k</i>-means clustering for working condition classification. It is concluded that the proposed method can significantly reduce the false-alarm rate of the vibration detection process.
format	article
author	Huanguo Chen Chao Xie Juchuan Dai Enjie Cen Jianmin Li
author_facet	Huanguo Chen Chao Xie Juchuan Dai Enjie Cen Jianmin Li
author_sort	Huanguo Chen
title	SCADA Data-Based Working Condition Classification for Condition Assessment of Wind Turbine Main Transmission System
title_short	SCADA Data-Based Working Condition Classification for Condition Assessment of Wind Turbine Main Transmission System
title_full	SCADA Data-Based Working Condition Classification for Condition Assessment of Wind Turbine Main Transmission System
title_fullStr	SCADA Data-Based Working Condition Classification for Condition Assessment of Wind Turbine Main Transmission System
title_full_unstemmed	SCADA Data-Based Working Condition Classification for Condition Assessment of Wind Turbine Main Transmission System
title_sort	scada data-based working condition classification for condition assessment of wind turbine main transmission system
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/b968fce88f244271b647634af552df54
work_keys_str_mv	AT huanguochen scadadatabasedworkingconditionclassificationforconditionassessmentofwindturbinemaintransmissionsystem AT chaoxie scadadatabasedworkingconditionclassificationforconditionassessmentofwindturbinemaintransmissionsystem AT juchuandai scadadatabasedworkingconditionclassificationforconditionassessmentofwindturbinemaintransmissionsystem AT enjiecen scadadatabasedworkingconditionclassificationforconditionassessmentofwindturbinemaintransmissionsystem AT jianminli scadadatabasedworkingconditionclassificationforconditionassessmentofwindturbinemaintransmissionsystem
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SCADA Data-Based Working Condition Classification for Condition Assessment of Wind Turbine Main Transmission System

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