文章摘要
李刚,张建付,文福拴,等.计及信息不确定性的风电机组健康状态实时评估方法[J].电力系统自动化,2017,41(18):111-117. DOI: 10.7500/AEPS20170210001.
LI Gang,ZHANG Jianfu,WEN Fushuan, et al.Evaluation of Real-time Health Condition for Wind Turbines Considering Information Uncertainty[J].Automation of Electric Power Systems,2017,41(18):111-117. DOI: 10.7500/AEPS20170210001.
计及信息不确定性的风电机组健康状态实时评估方法
Evaluation of Real-time Health Condition for Wind Turbines Considering Information Uncertainty
DOI:10.7500/AEPS20170210001
关键词: 风电机组  预测与健康管理  Spark流式处理  高斯云变换  高斯云模型
KeyWords: wind turbine  prognostics and health management(PHM)  Spark streaming  Gaussian cloud transformation  Gaussian cloud model
上网日期:2017-07-11
基金项目:国家自然科学基金资助项目(51407076);河北省自然科学基金资助项目(F2014502050);中央高校基本科研业务费专项资金资助项目(2015ZD28)
作者单位E-mail
李刚 华北电力大学控制与计算机工程学院, 河北省保定市 071003 ququ_er2003@126.com 
张建付 华北电力大学控制与计算机工程学院, 河北省保定市 071003  
文福拴 浙江大学电气工程学院, 浙江省杭州市 310027; 文莱科技大学电机与电子工程系, 斯里巴加湾 BE1410, 文莱  
宋雨 华北电力大学控制与计算机工程学院, 河北省保定市 071003  
摘要:
      运行工况识别作为风电机组状态监测与健康管理领域的重要环节,往往受到不确定信息以及高速实时数据流的影响,造成健康状态评估难以有效实施。在此背景下,文中提出一种基于Spark流式处理的健康状态实时评估方法。首先,采用大数据分析技术实现风电机组运行工况的空间划分;然后,在充分考虑风电机组监测信息不确定性的情况下,结合数据采集与监控(SCADA)历史运行数据,对基于高斯云模型和高斯云变换的健康状态评估模型进行训练,并以健康指数作为风电机组健康状态评估的指标。最后,将该评估方法应用在中国北方某风电场1.5 MW风电机组故障前的健康状态评估中。算例分析结果表明,该方法可监测到风电机组健康状态的变化趋势,初步实现了故障的早期预警。
Abstract:
      As an important part of wind turbine health monitoring and management, the identification of operating condition is often affected by uncertainty information and high-speed real-time data streams, which causes it difficult to carry out a health assessment effectively. In this context, this paper proposes a real-time health assessment method based on Spark streaming, First, the big data analysis technology is used to realize the spatial division of wind turbine operating condition. And then, taking into consideration the uncertainty of monitoring information, the Gaussian cloud model and Gaussian cloud transformation-based assessment model is trained according to the historical data of supervisory control and data acquisition(SCADA). In this process, the health index is introduced and can be viewed as an indicator of wind turbines health assessment. Finally, the method is applied in order to assess the health condition of a 1. 5 MW wind turbine located in north of China. Results show that the method can monitor the changing of the health of wind turbines and the accuracy improves compared with the traditional method.
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