一种风电机组变桨系统故障分析方法
|
摘要
为了提高风电机组变桨系统的故障识别准确率,提出一种通过提取采集数据的特征参数来构造高维特征参数矩阵,高维特征参数矩阵经过LLE降维得到低维特征参数,低维特征参数矩阵作为SVM模式分类输入进行工况识别的方法。该方法在风机故障识别中的准确率为87.50%,正常状态识别准确率为100%,最终的总识别准确率为93.75%。通过理论分析和实验表明:该方法提高了永磁直驱风电机组变桨系统故障识别的准确率。
In order to improve the fault identification accuracy of wind turbines' variable paddle system, a condition identifying method which has the feature parameter extracted from the collected data to construct the high-dimensional feature parameter matrix was proposed. In which, the high-dimensional feature parameter matrix was reduced by LLE to obtain the low-dimensional feature parameters, and the low-dimensional feature parameter matrix was used as the input of SVM pattern classification to identify the working conditions. The recognition rate of this method for wind turbines' faults is 87.50% and the recognition rate at normal state is 100% along with the final total recognition accuracy of 93.75%. The theoretical analysis and experiments show that, this method improves the accuracy rate of identifying the pitch system's faults of the permanent magnet direct drive wind turbine.
In order to improve the fault identification accuracy of wind turbines' variable paddle system, a condition identifying method which has the feature parameter extracted from the collected data to construct the high-dimensional feature parameter matrix was proposed. In which, the high-dimensional feature parameter matrix was reduced by LLE to obtain the low-dimensional feature parameters, and the low-dimensional feature parameter matrix was used as the input of SVM pattern classification to identify the working conditions. The recognition rate of this method for wind turbines' faults is 87.50% and the recognition rate at normal state is 100% along with the final total recognition accuracy of 93.75%. The theoretical analysis and experiments show that, this method improves the accuracy rate of identifying the pitch system's faults of the permanent magnet direct drive wind turbine.
引文
[1] 王正宇,张扬帆,段向阳,等.基于Relief算法的风电机组故障特征参数提取方法[J].华北电力技术,2017,(10):57～62.
[2] 李辉,杨超,李学伟,等.风机电动变桨系统状态特征参量挖掘及异常识别[J].中国电机工程学报,2014,34(12):1922～1930.
[3] 李勇,陈贺新,赵刚,等.基于可变k近邻LLE数据降维的图像检索方法[J].吉林大学学报(工学版),2008,38(4):946～949.
[4] 郭锋,刘丽丽,吕凝.基于LLE和SVM的人像识别方法[J].吉林大学学报(信息科学版),2008,26(1):48～54.
[5] 兰兰,陈万忠,魏庭松.基于融合特征提取与LLE方法的表情识别[J].吉林大学学报(信息科学版),2017,35(4):384～391.
[6] 苏盈盈,马飞,刘兴华,等.基于LLE算法和SVM的旋转机械故障诊断[J].重庆电力高等专科学校学报,2013,18(6):63～66.
[7] 李力,李冕,陈法法.基于LLE和LSSVM的滚动轴承故障诊断[J].煤矿机械,2015,36(7):308～310.
[8] 路敬祎.传感器原理及应用[M].哈尔滨:哈尔滨工程大学出版社,2014:145～149.
[9] 王小祥.增量式旋转编码器的简介与应用[J].数字技术与应用,2016,(10):118～119.
[10] 蒋利勇,李伟光.绝对式光电编码器的接口装置设计与应用[J].装备制造技术,2007,(5):62～64.
[11] 夏亮.绝对式编码器总线传输技术研究与实现[D].武汉:华中科技大学,2011.
[12] 王建忠.高维数据几何结构及降维[M].北京:高等教育出版社,2012:203～220.
[2] 李辉,杨超,李学伟,等.风机电动变桨系统状态特征参量挖掘及异常识别[J].中国电机工程学报,2014,34(12):1922～1930.
[3] 李勇,陈贺新,赵刚,等.基于可变k近邻LLE数据降维的图像检索方法[J].吉林大学学报(工学版),2008,38(4):946～949.
[4] 郭锋,刘丽丽,吕凝.基于LLE和SVM的人像识别方法[J].吉林大学学报(信息科学版),2008,26(1):48～54.
[5] 兰兰,陈万忠,魏庭松.基于融合特征提取与LLE方法的表情识别[J].吉林大学学报(信息科学版),2017,35(4):384～391.
[6] 苏盈盈,马飞,刘兴华,等.基于LLE算法和SVM的旋转机械故障诊断[J].重庆电力高等专科学校学报,2013,18(6):63～66.
[7] 李力,李冕,陈法法.基于LLE和LSSVM的滚动轴承故障诊断[J].煤矿机械,2015,36(7):308～310.
[8] 路敬祎.传感器原理及应用[M].哈尔滨:哈尔滨工程大学出版社,2014:145～149.
[9] 王小祥.增量式旋转编码器的简介与应用[J].数字技术与应用,2016,(10):118～119.
[10] 蒋利勇,李伟光.绝对式光电编码器的接口装置设计与应用[J].装备制造技术,2007,(5):62～64.
[11] 夏亮.绝对式编码器总线传输技术研究与实现[D].武汉:华中科技大学,2011.
[12] 王建忠.高维数据几何结构及降维[M].北京:高等教育出版社,2012:203～220.