基于云计算平台的风电场SCADA系统的设计与应用  

Design and application of a wind farm SCADA system based on cloud computing platform

作　　者：梁涛[1] 侯振国 邹继行 张迎娟 Liang Tao;Hou Zhenguo;Zou Jihang;Zhang Yingjuan(School of Control Science and Engineering, Hebei University of Technology, Tianjin 300130)

机构地区：[1]河北工业大学控制科学与工程学院,天津300130

出　　处：《高技术通讯》2018年第1期8-14,共7页Chinese High Technology Letters

基　　金：河北省科技计划(16214510D)资助项目

摘　　要：针对目前风电场积累的海量多源异构的大数据,借助云计算平台架构灵活多变、数据并行处理的优势,设计了基于云计算平台的风电场数据采集与监控(SCADA)系统。在该系统的云计算平台上采用智能化数据挖掘算法,完成了风机数据的远程采集、智能分析、故障预判。最后选取反映风电场性能的功率和转子转速数据,以自适应权重(AFW)的K-means算法和最小二乘支持向量机两种典型的数据挖掘算法为例,对数据进行了聚类分析和回归分析,验证了系统的可行性。Aiming at the large amount of multi-source and heterogeneous data accumulated by wind farms,a wind farm supervisory control and data acquisition（ SCADA） system based on the cloud computing platform is designed with the help of cloud computing platform＇s advantages of architectura flexibility and parallel data processing. The intelligent data mining algorithm is adopted in the cloud computing platform of the system to complete the remote collection,intelligent analysis and fault prediction of wind power generator＇s data. Finally,the power and rotor speed data reflecting the performance of the wind farm are selected and are analyzed by cluster analysis and regression analysis by using the two typical data mining algorithms of the adaptive weight based AFW-K-means and the least square support vector machine（ SVM） to verify the feasibility of the system.

关 键 词：数据采集与监控(SCADA) 云计算 数据挖掘 聚类 支持向量机(SVM) 

分 类 号：TP274.2[自动化与计算机技术—检测技术与自动化装置] TP277[自动化与计算机技术—控制科学与工程] TP311.13