Optimized Extreme Learning Machine-Based Main Bearing Temperature Monitoring Considering Ambient Conditions’ Effects

Wind Turbines (WTs) are exposed to harsh conditions and can experience extreme weather, such as blizzards and cold waves, which can directly affect temperature monitoring. This paper analyzes the effects of ambient conditions on WT monitoring. To reduce these effects, a novel WT monitoring method is...

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Main Authors:	Zhengnan Hou, Xiaoxiao Lv, Shengxian Zhuang
Format:	article
Language:	EN
Published:	MDPI AG 2021
Subjects:	Wind Turbine temperature monitoring ambient condition Extreme Learning Machine genetic algorithm SCADA Technology T
Online Access:	https://doaj.org/article/43f5777df17841029b37e9bf9660aa80
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id	oai:doaj.org-article:43f5777df17841029b37e9bf9660aa80
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spelling	oai:doaj.org-article:43f5777df17841029b37e9bf9660aa802021-11-25T17:26:19ZOptimized Extreme Learning Machine-Based Main Bearing Temperature Monitoring Considering Ambient Conditions’ Effects10.3390/en142275291996-1073https://doaj.org/article/43f5777df17841029b37e9bf9660aa802021-11-01T00:00:00Zhttps://www.mdpi.com/1996-1073/14/22/7529https://doaj.org/toc/1996-1073Wind Turbines (WTs) are exposed to harsh conditions and can experience extreme weather, such as blizzards and cold waves, which can directly affect temperature monitoring. This paper analyzes the effects of ambient conditions on WT monitoring. To reduce these effects, a novel WT monitoring method is also proposed in this paper. Compared with existing methods, the proposed method has two advantages: (1) the changes in ambient conditions are added to the input of the WT model; (2) an Extreme Learning Machine (ELM) optimized by Genetic Algorithm (GA) is applied to construct the WT model. Using Supervisory Control and Data Acquisition (SCADA), compared with the method that does not consider the changes in ambient conditions, the proposed method can reduce the number of false alarms and provide an earlier alarm when a failure does occur.Zhengnan HouXiaoxiao LvShengxian ZhuangMDPI AGarticleWind Turbinetemperature monitoringambient conditionExtreme Learning Machinegenetic algorithmSCADATechnologyTENEnergies, Vol 14, Iss 7529, p 7529 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Wind Turbine temperature monitoring ambient condition Extreme Learning Machine genetic algorithm SCADA Technology T
spellingShingle	Wind Turbine temperature monitoring ambient condition Extreme Learning Machine genetic algorithm SCADA Technology T Zhengnan Hou Xiaoxiao Lv Shengxian Zhuang Optimized Extreme Learning Machine-Based Main Bearing Temperature Monitoring Considering Ambient Conditions’ Effects
description	Wind Turbines (WTs) are exposed to harsh conditions and can experience extreme weather, such as blizzards and cold waves, which can directly affect temperature monitoring. This paper analyzes the effects of ambient conditions on WT monitoring. To reduce these effects, a novel WT monitoring method is also proposed in this paper. Compared with existing methods, the proposed method has two advantages: (1) the changes in ambient conditions are added to the input of the WT model; (2) an Extreme Learning Machine (ELM) optimized by Genetic Algorithm (GA) is applied to construct the WT model. Using Supervisory Control and Data Acquisition (SCADA), compared with the method that does not consider the changes in ambient conditions, the proposed method can reduce the number of false alarms and provide an earlier alarm when a failure does occur.
format	article
author	Zhengnan Hou Xiaoxiao Lv Shengxian Zhuang
author_facet	Zhengnan Hou Xiaoxiao Lv Shengxian Zhuang
author_sort	Zhengnan Hou
title	Optimized Extreme Learning Machine-Based Main Bearing Temperature Monitoring Considering Ambient Conditions’ Effects
title_short	Optimized Extreme Learning Machine-Based Main Bearing Temperature Monitoring Considering Ambient Conditions’ Effects
title_full	Optimized Extreme Learning Machine-Based Main Bearing Temperature Monitoring Considering Ambient Conditions’ Effects
title_fullStr	Optimized Extreme Learning Machine-Based Main Bearing Temperature Monitoring Considering Ambient Conditions’ Effects
title_full_unstemmed	Optimized Extreme Learning Machine-Based Main Bearing Temperature Monitoring Considering Ambient Conditions’ Effects
title_sort	optimized extreme learning machine-based main bearing temperature monitoring considering ambient conditions’ effects
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/43f5777df17841029b37e9bf9660aa80
work_keys_str_mv	AT zhengnanhou optimizedextremelearningmachinebasedmainbearingtemperaturemonitoringconsideringambientconditionseffects AT xiaoxiaolv optimizedextremelearningmachinebasedmainbearingtemperaturemonitoringconsideringambientconditionseffects AT shengxianzhuang optimizedextremelearningmachinebasedmainbearingtemperaturemonitoringconsideringambientconditionseffects
_version_	1718412346019282944

Optimized Extreme Learning Machine-Based Main Bearing Temperature Monitoring Considering Ambient Conditions’ Effects

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