风力发电机组的健康评估
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摘要
为了保证风力发电机组的正常运行,降低运行维护成本,建立了风力发电机组的健康评估系统。机组健康评估要素包括机组监测、数据预处理、特征提取和专家库。机组健康评估是利用机组监测采集信息,经数据预处理后,进行特征提取;将提取的特征与专家库分析、比较,进而对风力发电机组的健康进行评估。通过对机组的健康评估,预先了解机组的健康状况,针对不同的故障提早预防或给出相应的处理措施,尽量排除故障或者防止故障再扩大。对风力发电机组的健康评估为风电场的状态检修提供了依据。
In order to ensure the normal operation of wind turbines, the health assessment system of wind turbines is established to reduce operation and maintenance costs. The health assessment factors include monitoring, data preprocessing, feature extraction and expert database. The feature of the monitored information is first extracted through data preprocessing, then is compared to the expert database. In this way, finally the health assement of wind trubines can be realized. By use of the health assessment, the health status of wind turbines were prior known to early prevent for different faults or to give corresponding treatment measures. The ultimate aims are to troubleshoot or to prevent faults to expand. The health assessment of wind turbines provides the basis for the condition maintenance of wind farm.
In order to ensure the normal operation of wind turbines, the health assessment system of wind turbines is established to reduce operation and maintenance costs. The health assessment factors include monitoring, data preprocessing, feature extraction and expert database. The feature of the monitored information is first extracted through data preprocessing, then is compared to the expert database. In this way, finally the health assement of wind trubines can be realized. By use of the health assessment, the health status of wind turbines were prior known to early prevent for different faults or to give corresponding treatment measures. The ultimate aims are to troubleshoot or to prevent faults to expand. The health assessment of wind turbines provides the basis for the condition maintenance of wind farm.
引文
[1]王致杰.大型风力发电机组状态监测与智能故障诊断[M].上海:上海交通大学出版社.2013.
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[7]陈长征,赵新光,周勃,等.风电机组叶片裂纹故障特征提取方法[J].中国电机工程学报,2013,33(2):112-117.
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[12]Kirikera G R,Shinde V,Schulz M J,et al.Monitoring multi-site damage growth during quasi-static testing of a wind turbine blade using a structural neural system[J].Structural Health Monitoring,2008,7(2):157-173.
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[2]张碧波.设备状态监测与故障诊断[M].北京:化学工业出版社.2004.
[3]乔中亚.风电场设备从计划检修到状态检修转变的思考[J].江苏电机工程,2010,29(2):13-15.
[4]姚兴佳,宋俊.风力发电机组原理与应用[M].北京:机械工业出版社.2011.
[5]Li B,Zhang P L,Tian H,et al.A new feature extraction and selection scheme for hybrid fault diagnosis of gearbox[J].Expert Systems with Applications,2011,38(8):10000-10009.
[6]周雁冰,柳亦兵,赵秋丽,等.基于非高斯性强度的风电齿轮箱故障特征提取[J].动力工程学报,2013,33(11):865-870.
[7]陈长征,赵新光,周勃,等.风电机组叶片裂纹故障特征提取方法[J].中国电机工程学报,2013,33(2):112-117.
[8]Lekou D,Vionis P,Joosse P A,et al.Full-scale blade testing enhanced by acoustic emission monitoring[C]//Proceeding of European Wind Energy Conference,Madrid,Spain,2003.
[9]Paquette J,Van Dam J,Hughes S.Structural testing of9m carbon fiber wind turbine research blades[C]//AIAA2007 Wind Energy Symposium,Reno,USA,2007.
[10]Tavner P J.Review of condition monitoring of rotating electrical machines[J].IET Electric Power Applica-tions,2008,2(4):215-247.
[11]Kirikera G R,Shinde V,Schulz M J,et al.A structural neural system for real-time health monitoring of composite materials[J].Structural Health Monitoring,2008,7(1):65-83.
[12]Kirikera G R,Shinde V,Schulz M J,et al.Monitoring multi-site damage growth during quasi-static testing of a wind turbine blade using a structural neural system[J].Structural Health Monitoring,2008,7(2):157-173.
[13]张彦宁.基于小波分析的水电机组在线监测与故障诊断的研究[D].西安:西安理工大学.2013.