双馈式风力发电机组故障诊断的方法及实现
|
摘要
本文阐述了风力发电的优势及风力发电行业在国内的发展现状,针对风力发电机组(以下简称风力机)主传动链(主轴、齿轮箱、发电机)及塔筒出现的一些问题,结合小波分析理论,对我校风能技术研究所自主研发SUT-1000型风力机进行了故障诊断,提出合理化的故障监测方案。
本论文首先通过阐述风力发电的优势及存在的问题,说明风力机在实际国民生产中的重要地位,从而论证了实施风力机状态监测故障诊断的意义。再对小波分析理论进行简单说明,阐明小波分析在风力机分析的优势。并结合我所研究的兆瓦级双馈风力发电机实际工作状况,分析常见的故障,发现易出现故障的部件,决定振动监测的施工方案。然后对传动链上的关键部位(如主轴、齿轮箱等)采集的振动信号进行FFT分析。再利用MATLAB中的小波分析模块,设定阈值对振动信号消噪处理,使用小波函数对数据进行多尺度分析计算。最后是对塔筒故障诊断方法的研究,首先使用MSC有限元分析软件,计算出各阶固有频率。同时利用分析软件计算出风机在最大额定载荷下塔筒顶端的最大振幅。根据以上计算出的值作为振动监测的标准,设定危险报警值,提出合理的监测方案,监测其在额定工况下的工作状态。
本论文实验数据全部来自现场实际测试得到的,贴近风机工作实际。并采用小波分析方法与有限元分析相结合,达到综合、精确故障诊断的效果。同时把实际情况与诊断结果相比较,结果基本吻合,证明了所使用分析方法的正确性。最后,根据测试诊断的经验,做出了“兆瓦级风力机状态监测系统”塔筒诊断系统的概念设计。
The paper expatiates advantage of wind turbine and its development in our country. Aiming at failure of the transmission chain of the wind turbine(for example:main-shaft, gearbox etc) and tower,the paper analyze the fault of SUT-1000 wind turbine designed independently by Wind Energy Institution ShenYang University of Technology,and put forward reasonable project of fault diagnosis online according to wavelet theory.
The paper put forward the important status of wind turbine in domestic economic through explaining its advantage and problem,thereby demonstrates the meaning of wind turbine measure and diagnose on line.Then,the paper introduces the wavelet theory simply and illuminates the advantage of wavelet analysis theory on wind turbine diagnosis. Meanwhile,based on the practical condition of the wind turbine designed by the Wind Energy Institute ShenYang University of technology,and analyzing normal failure and finding the components easily gone wrong of wind turbine,so the actual construction program of vibration monitoring is decided.Then,the vibration signal from key parts (gearbox,generator etc.) of transmission chain is analyzed with FFT method.Utilizing wavelet analysis module in MATLAB,we can set threshold to eliminate noise,and farther apply wavelet function to implement multi-measure analysis calculation of data.At last, the paper studies the failure diagnosis method of tower.On the one hand,the natural frequency is figured out by using MSC software,on the other hand,the maximum amplitude of vibration is also calculated.According to calculation value above that is regarded as the standard of vibration online,we can set the alarm value,and put forward reasonable project to measure the condition of wind turbine anytime.
The experiment data used in the paper wholly comes from the testing of wind turbine on the spot,so it confirms to actual condition of wind turbine.The paper adopts wavelet analysis method combined with finite element analysis to get diagnosis result precisely, and compares the practical condition with diagnosis result,the correctness of result is proved.At last,according to the experience of testing diagnosis,we achieves the tower concept design of MW wind turbine diagnose and measure on line system.
本论文首先通过阐述风力发电的优势及存在的问题,说明风力机在实际国民生产中的重要地位,从而论证了实施风力机状态监测故障诊断的意义。再对小波分析理论进行简单说明,阐明小波分析在风力机分析的优势。并结合我所研究的兆瓦级双馈风力发电机实际工作状况,分析常见的故障,发现易出现故障的部件,决定振动监测的施工方案。然后对传动链上的关键部位(如主轴、齿轮箱等)采集的振动信号进行FFT分析。再利用MATLAB中的小波分析模块,设定阈值对振动信号消噪处理,使用小波函数对数据进行多尺度分析计算。最后是对塔筒故障诊断方法的研究,首先使用MSC有限元分析软件,计算出各阶固有频率。同时利用分析软件计算出风机在最大额定载荷下塔筒顶端的最大振幅。根据以上计算出的值作为振动监测的标准,设定危险报警值,提出合理的监测方案,监测其在额定工况下的工作状态。
本论文实验数据全部来自现场实际测试得到的,贴近风机工作实际。并采用小波分析方法与有限元分析相结合,达到综合、精确故障诊断的效果。同时把实际情况与诊断结果相比较,结果基本吻合,证明了所使用分析方法的正确性。最后,根据测试诊断的经验,做出了“兆瓦级风力机状态监测系统”塔筒诊断系统的概念设计。
The paper expatiates advantage of wind turbine and its development in our country. Aiming at failure of the transmission chain of the wind turbine(for example:main-shaft, gearbox etc) and tower,the paper analyze the fault of SUT-1000 wind turbine designed independently by Wind Energy Institution ShenYang University of Technology,and put forward reasonable project of fault diagnosis online according to wavelet theory.
The paper put forward the important status of wind turbine in domestic economic through explaining its advantage and problem,thereby demonstrates the meaning of wind turbine measure and diagnose on line.Then,the paper introduces the wavelet theory simply and illuminates the advantage of wavelet analysis theory on wind turbine diagnosis. Meanwhile,based on the practical condition of the wind turbine designed by the Wind Energy Institute ShenYang University of technology,and analyzing normal failure and finding the components easily gone wrong of wind turbine,so the actual construction program of vibration monitoring is decided.Then,the vibration signal from key parts (gearbox,generator etc.) of transmission chain is analyzed with FFT method.Utilizing wavelet analysis module in MATLAB,we can set threshold to eliminate noise,and farther apply wavelet function to implement multi-measure analysis calculation of data.At last, the paper studies the failure diagnosis method of tower.On the one hand,the natural frequency is figured out by using MSC software,on the other hand,the maximum amplitude of vibration is also calculated.According to calculation value above that is regarded as the standard of vibration online,we can set the alarm value,and put forward reasonable project to measure the condition of wind turbine anytime.
The experiment data used in the paper wholly comes from the testing of wind turbine on the spot,so it confirms to actual condition of wind turbine.The paper adopts wavelet analysis method combined with finite element analysis to get diagnosis result precisely, and compares the practical condition with diagnosis result,the correctness of result is proved.At last,according to the experience of testing diagnosis,we achieves the tower concept design of MW wind turbine diagnose and measure on line system.
引文
[1]孙孝仁,孙怡玲.2020年世界能源前景.利技情报开发与经济[J],2000年,第10卷第2期:12-14.
[2]张希良.风能开发利用[M].北京:化工工业出版社,2005.1:125-129.
[3]郭旺.风力发电蓬勃发展.广西电业[J],2006年,第6期:8-11.
[4]Doglas J.Smith.Intelligent Computer systems enhance power plant operations[J].Power Engineering.1989,93(12):21-26.
[5]Muszynska A.Vibrational Diagnostics of Rotating Machinery Malfunctions[J].Vibration and Rotor Dynamics.1992(6):40.
[6]张瑞林.机械故障诊断技术发展现状及展望[J],第二届全国机械设备故障诊断学术会议论文集,1988年:40-45.
[7]刘雄,赵振毅,屈梁生.机械故障诊断丛书[M].北京:冶金工业出版社,1991.1989-1993.
[8]Frank P.M.Fault Diagnosis in Dynamic Systems Using Analytical and Knowledge based Redundancy-A Survey and Some New Results[J].Automatica,1990,26(3):459-474.
[9]Patton R J,Chert J.Review of Parity Space Approaches to Fault Diagnosis for Aerospace System.Journal of Guidance[J].Control and Dynamics,1994,17(2):278-285.
[10]Patton R J.Robustness in Model based Fault Diagnosis:1995 situation[J].Annual Review in Control,1997,21:103-123.
[11]Gertler J J.Analytical Redundancy Methods in Fault Detection and Isolation.Proceedings of IFAC Fault Detection[J].Supervision and Safety for Technical Processes,1991(9):21.
[12]Frank P.M,X.Ding.Survey of Robust Residual Generation and Evaluation Methods in Observer based Fault Detection System.Journal of Process Control[J].Supervision and Safety for Technical Processes,1997,7(6):403-424.
[13]吴今培.智能故障诊断技术的发展和展望[J].震动、测试与诊断,1999,19(2):80-147.
[14]玄志成,陈章位,黄克强等.基于Petri网的符合故障诊断方法的研究[J].机械科学与技术,1999,18(2):291-293.
[15]Feng Lin.Dignosability of Discrete Event Systems and Its Applications[M].Discrete Event Dynamics Systems,1994,4:197-212.
[16]Meera Sampath,Raja Sengupta,Stephane Lafoutune.Dignosability of Discrete Event Systems[J].IEEE Transaction on Automatic Control,1995,40(9):1555-1575.
[17]Meera Sampath,Raja Sengupta,Stephane Lafoutune.Active Diagnosis of Discrete Event Systems[J].IEEE Transaction on Automatic Control,1998,43(7):908-929.
[18]姚兴佳,王士荣,董丽萍.风力发电技木讲座(二)风力机的工作原理[J].可再生能源 2006.2:88-89.
[19]倪受元.风力发电讲座第二讲风力机的工作原理和气动力特性[J].太阳能,2000,03:12-16.
[20]倪受元.风力发电讲座第一讲风力机的类型与结构[J].太阳能,2000,02:6-10.
[21]倪受元.风力发电讲座第六讲风力发电的现状和展望[J]太阳能,2001,03:14-17.
[22]李滨波,段向阳.风力发电机原理及风力发电技术[J].湖北电力,2007,12:55-56.
[23]王维俭,刘俊宏.近十年电气主设备继电保护技术的进展[J].电力自动化设备,1995.5.(2):1-9.
[24]王维俭.电气主设备继电保护原理与应用(第2版)[J].北京:中国电力出版社,2002:179-186..
[25]陈进.机械设备振动监测与故障诊断[M].上海:上海交通大学出版社,1999:12-19.
[26]刘济,张素贞,肖大雏.小波变换在机械故障诊断中的应用[J].控制工程,2003(7):30.
[27]赵炜.用冲击脉冲法诊断滚动轴承故障[J].轴承.2006(5):19-23.
[28]杨福生.小波变换的工程分析与应用[M].北京:科学出版社,1999:89-93.
[29]王丽丽,李文华.2种小波消噪方法的比较[J].煤矿机械,27(12):45-49.
[30]陈长征.动态测试与信号处理[M].沈阳:沈阳工业大学振动噪声研究所,2007.3:15-18.
[31]葛哲学,沙威.小波分析理论与MATLAB R2007实现[M].电子工业出版社.2007.10:79-83.
[32]王楠,杜劲松,小波消噪在振动信号处理中的应用[J].仪器仪表学报,2001.8:18-20.
[33]姚雪梅,刘增力,董卫等.一种新的小波消噪闽值的估计方法[J].通信技术,2007年第4期:23-28.
[34]杨福生.小波变换的上程分析与应用[M].北京:科学出版社,1999:201-205.
[35]李建平.小波分析与信号处理—理论、应用及软件实现[M].重庆:重庆出版社,1999:322-326.
[36]MALIAT S.A theory for mult-resolution signal decomposition,the wavelet reprentation[J].IEEE Trans,1989,11(7):674-693.
[37]石为人,罗雪松,胡宁.基于小波多分辨率分析的信号消噪[J].重庆大学学报(自然科学版),2002,25(6):337-340.
[38]王国锋,王子良,秦旭达等.基于小波包和径向基神经网络轴承故障诊断[J].北京科技大学学报.2004,26(2):68-72.
[39]姚兴佳,关新,单光坤.Wavelet Module of MATLAB Applies on Wind Turbine[J].2008年北京世界风能大会论文集.2008.10:30-34.
[40]金嘉琦,关新,单光坤.用小波理论对风力发电机振动监测信号的分析方法[J].沈阳工业大学学报(自然科学版).2008,30(5):520-524.
[41]陈余岳.大型风力机玻璃钢叶片设计[J].玻璃钢复合材料,1998,(4):17-20.
[42]王其政.结构藕合动力学[M].北京:宇航出版社,1999:167-170.
[2]张希良.风能开发利用[M].北京:化工工业出版社,2005.1:125-129.
[3]郭旺.风力发电蓬勃发展.广西电业[J],2006年,第6期:8-11.
[4]Doglas J.Smith.Intelligent Computer systems enhance power plant operations[J].Power Engineering.1989,93(12):21-26.
[5]Muszynska A.Vibrational Diagnostics of Rotating Machinery Malfunctions[J].Vibration and Rotor Dynamics.1992(6):40.
[6]张瑞林.机械故障诊断技术发展现状及展望[J],第二届全国机械设备故障诊断学术会议论文集,1988年:40-45.
[7]刘雄,赵振毅,屈梁生.机械故障诊断丛书[M].北京:冶金工业出版社,1991.1989-1993.
[8]Frank P.M.Fault Diagnosis in Dynamic Systems Using Analytical and Knowledge based Redundancy-A Survey and Some New Results[J].Automatica,1990,26(3):459-474.
[9]Patton R J,Chert J.Review of Parity Space Approaches to Fault Diagnosis for Aerospace System.Journal of Guidance[J].Control and Dynamics,1994,17(2):278-285.
[10]Patton R J.Robustness in Model based Fault Diagnosis:1995 situation[J].Annual Review in Control,1997,21:103-123.
[11]Gertler J J.Analytical Redundancy Methods in Fault Detection and Isolation.Proceedings of IFAC Fault Detection[J].Supervision and Safety for Technical Processes,1991(9):21.
[12]Frank P.M,X.Ding.Survey of Robust Residual Generation and Evaluation Methods in Observer based Fault Detection System.Journal of Process Control[J].Supervision and Safety for Technical Processes,1997,7(6):403-424.
[13]吴今培.智能故障诊断技术的发展和展望[J].震动、测试与诊断,1999,19(2):80-147.
[14]玄志成,陈章位,黄克强等.基于Petri网的符合故障诊断方法的研究[J].机械科学与技术,1999,18(2):291-293.
[15]Feng Lin.Dignosability of Discrete Event Systems and Its Applications[M].Discrete Event Dynamics Systems,1994,4:197-212.
[16]Meera Sampath,Raja Sengupta,Stephane Lafoutune.Dignosability of Discrete Event Systems[J].IEEE Transaction on Automatic Control,1995,40(9):1555-1575.
[17]Meera Sampath,Raja Sengupta,Stephane Lafoutune.Active Diagnosis of Discrete Event Systems[J].IEEE Transaction on Automatic Control,1998,43(7):908-929.
[18]姚兴佳,王士荣,董丽萍.风力发电技木讲座(二)风力机的工作原理[J].可再生能源 2006.2:88-89.
[19]倪受元.风力发电讲座第二讲风力机的工作原理和气动力特性[J].太阳能,2000,03:12-16.
[20]倪受元.风力发电讲座第一讲风力机的类型与结构[J].太阳能,2000,02:6-10.
[21]倪受元.风力发电讲座第六讲风力发电的现状和展望[J]太阳能,2001,03:14-17.
[22]李滨波,段向阳.风力发电机原理及风力发电技术[J].湖北电力,2007,12:55-56.
[23]王维俭,刘俊宏.近十年电气主设备继电保护技术的进展[J].电力自动化设备,1995.5.(2):1-9.
[24]王维俭.电气主设备继电保护原理与应用(第2版)[J].北京:中国电力出版社,2002:179-186..
[25]陈进.机械设备振动监测与故障诊断[M].上海:上海交通大学出版社,1999:12-19.
[26]刘济,张素贞,肖大雏.小波变换在机械故障诊断中的应用[J].控制工程,2003(7):30.
[27]赵炜.用冲击脉冲法诊断滚动轴承故障[J].轴承.2006(5):19-23.
[28]杨福生.小波变换的工程分析与应用[M].北京:科学出版社,1999:89-93.
[29]王丽丽,李文华.2种小波消噪方法的比较[J].煤矿机械,27(12):45-49.
[30]陈长征.动态测试与信号处理[M].沈阳:沈阳工业大学振动噪声研究所,2007.3:15-18.
[31]葛哲学,沙威.小波分析理论与MATLAB R2007实现[M].电子工业出版社.2007.10:79-83.
[32]王楠,杜劲松,小波消噪在振动信号处理中的应用[J].仪器仪表学报,2001.8:18-20.
[33]姚雪梅,刘增力,董卫等.一种新的小波消噪闽值的估计方法[J].通信技术,2007年第4期:23-28.
[34]杨福生.小波变换的上程分析与应用[M].北京:科学出版社,1999:201-205.
[35]李建平.小波分析与信号处理—理论、应用及软件实现[M].重庆:重庆出版社,1999:322-326.
[36]MALIAT S.A theory for mult-resolution signal decomposition,the wavelet reprentation[J].IEEE Trans,1989,11(7):674-693.
[37]石为人,罗雪松,胡宁.基于小波多分辨率分析的信号消噪[J].重庆大学学报(自然科学版),2002,25(6):337-340.
[38]王国锋,王子良,秦旭达等.基于小波包和径向基神经网络轴承故障诊断[J].北京科技大学学报.2004,26(2):68-72.
[39]姚兴佳,关新,单光坤.Wavelet Module of MATLAB Applies on Wind Turbine[J].2008年北京世界风能大会论文集.2008.10:30-34.
[40]金嘉琦,关新,单光坤.用小波理论对风力发电机振动监测信号的分析方法[J].沈阳工业大学学报(自然科学版).2008,30(5):520-524.
[41]陈余岳.大型风力机玻璃钢叶片设计[J].玻璃钢复合材料,1998,(4):17-20.
[42]王其政.结构藕合动力学[M].北京:宇航出版社,1999:167-170.