检索结果-南通市图书馆

Automatic Fault prediction of Wind Turbine Main Bearing Based on SCADA Data and Artificial Neural Network

Open Journal of Applied Sciences 2018年第6期8卷 211-225页

作者： Zhenyou Zhang Renewables & Utilities Kongsberg Digital AS Trondheim Norway

As the demand for wind energy continues to grow at exponential rate, reducing operation and maintenance (O & M) costs and improving reliability have become top priorities in wind turbine maintenance strategies. prediction of wind turbine failures before they reach a catastrophic stage is critical to reduce the O & M cost due to unnecessary scheduled maintenance. A SCADA-data based condition monitoring system, which takes advantage of data already collected at the wind turbine controller, is a cost-effective way to monitor wind turbines for early warning of failures. This article proposes a methodology of fault prediction and automatically generating warning and alarm for wind turbine main bearings based on stored SCADA data using Artificial Neural Network (ANN). The ANN model of turbine main bearing normal behavior is established and then the deviation between estimated and actual values of the parameter is calculated. Furthermore, a method has been developed to generate early warning and alarm and avoid false warnings and alarms based on the deviation. In this way, wind farm operators are able to have enough time to plan maintenance, and thus, unanticipated downtime can be avoided and O & M costs can be reduced.

关键词： Artificial Neural Network SCADA Data Wind Turbine Automatic Fault pre-diction

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Abstracts

引用

华电技术 2018年第9期40卷 77-80页

作者： XIAO Xiangwu WEN Wen BAI Quansheng HU Wei-dong LI Zhijin LIU Keqing Hunan Datang Xianyi Technol-ogy Company Limited Changsha 410007China

Optimization of energy consump-tion for ecast model based on big data platform and parallel random forestA healthy data set is acquired through data collection and preprocessing based on the construction of distributed big data analysis platform such as Hadoop,Spark and ***-gression forecasting model of energy consumption based on the parallel random forest algorithm is built to comprehensively ana-lyze and compare the relationship between input based on ran-dom forest prediction model,model parameters and *** emphasis lies on comparative analysis of the decision tree num-ber,depth of the decision tree and maximumnumber of split,which will affect the training model accuracy,running time and *** of the prediction model canachieve ac-curate prediction on the coal consumption for power supply and soft measurement calculation.

关键词： big data analysis platform random forest regression algorithm Spark distributed computing coal consumption pre-diction

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：