风电变流器主电路故障诊断监测系统研究
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摘要
永磁直驱风电系统由于结构简单、技术可靠和维护成本低等优点,得到了广泛的应用。其结构中虽然省去了双馈风电系统中故障率较高的齿轮箱,但全功率变流器也是故障率很高的部件之一,研究其故障诊断监视系统对风电系统的安全稳定运行具有重要意义。目前的风电故障诊断监视系统仍有人力耗费大、受环境影响大、诊断不准确等问题需要解决。
本文以风电变流器主电路拓扑结构为二极管整流器+升压斩波器+电压源型PWM逆变器的永磁直驱风力发电系统为研究对象,建立一套风电变流器的故障诊断监视系统。
首先,本文建立永磁直驱风电系统模型,选取风电变流器的三相输出电流来提取故障特征。在Matlab/Simulink软件下对风力发电系统正常运行以及25种断路故障状态分别进行仿真,得到正常运行和不同故障下的电流波形。采用小波分析对三相输出电流进行分解、重组,提取各相能量值,同时结合电流平均值参数,构造故障特征向量,并将所有的故障特征向量列表。
其次,本文选取基于小波分析和电流平均值参数构造的故障特征向量作为BP神经网络的输入量。根据故障特征向量表和期望特征,确定BP神经网络的结构,对确定好的神经网络进行学习训练,并利用训练好的神经网络对含有噪声的故障现象进行测试,验证了系统具有较强的稳定性。
最后,本文采用LabVIEW编写故障诊断监视系统的监视界面。然后通过LabVIEW的仿真工具包将Matlab的风电模型和监视界面链接起来,系统能够实时显示仿真模型中风电变流器的故障,并进行仿真验证。
本文将小波变换良好的时频局域化特性和神经网络的自学习功能相结合,运用于故障诊断系统,使得整个系统具有较强的逼近能力和容错能力。
Permanent magnet direct-drive wind generation has been widely applied with its various advantages, such as simple structure, reliable operating technology and low cost in maintenance. Although it eliminates the need for a gearbox which has a high failure rate in doubly-fed wind generation, the full power converter is also one of the high failure-rate components. It is significant that studying the fault diagnosis system for the safe and steady operation of wind generation. The current fault diagnosis system of wind generation still needs to overcome such problems as large human consumption, much environmental affection and the inaccuracy of diagnosis.
In this paper, we study permanent magnet direct-drive wind generation, and the wind power converter main circuit topology is diode rectifier+boost chopper+voltage source PWM inverter. A set of fault diagnostic monitoring system of wind power converter is established.
Firstly, in this paper, we establish a permanent magnet direct-drive wind power system model and select the three-phase output current of the wind power converter to extract the fault feature. The normal operation of the wind power generation system as well as25kinds of open-circuit fault condition is simulated in the Matlab/Simulink software, and then the current waveforms in the case of normal operation and under different fault can be obtained. I have decomposed and restructured the three-phase output current by using wavelet analysis method, extracted the phase energy value, combined the average current parameters with the tectonic fault feature vectors, as well as listed all the fault of feature vectors.
Secondly, we select the fault feature vector constructed by using wavelet analysis and the average current parameters as input of BP neural network in this paper. According to fault feature vector and expectations characteristics, the structure of BP neural network can be determined. We learn and train the determined neural network, then test the fault phenomena that contain noise by using the trained neural network, and it verifies that the system has strong stability.
Finally, we desgine a monitoring interface for fault diagnostic monitoring system by LabVIEW software.And then links the wind power model in matlab and monitoring interface by using LabVIEW Simulation Toolkit, The system can display wind power converter's failure for simulation model in real time, and simulation is carried out.
In this paper wavelet transform time-frequency localized features and neural network is the combination of self-learning function, applied to fault diagnosis system making the whole system has a strong approximation ability and fault tolerance.
本文以风电变流器主电路拓扑结构为二极管整流器+升压斩波器+电压源型PWM逆变器的永磁直驱风力发电系统为研究对象,建立一套风电变流器的故障诊断监视系统。
首先,本文建立永磁直驱风电系统模型,选取风电变流器的三相输出电流来提取故障特征。在Matlab/Simulink软件下对风力发电系统正常运行以及25种断路故障状态分别进行仿真,得到正常运行和不同故障下的电流波形。采用小波分析对三相输出电流进行分解、重组,提取各相能量值,同时结合电流平均值参数,构造故障特征向量,并将所有的故障特征向量列表。
其次,本文选取基于小波分析和电流平均值参数构造的故障特征向量作为BP神经网络的输入量。根据故障特征向量表和期望特征,确定BP神经网络的结构,对确定好的神经网络进行学习训练,并利用训练好的神经网络对含有噪声的故障现象进行测试,验证了系统具有较强的稳定性。
最后,本文采用LabVIEW编写故障诊断监视系统的监视界面。然后通过LabVIEW的仿真工具包将Matlab的风电模型和监视界面链接起来,系统能够实时显示仿真模型中风电变流器的故障,并进行仿真验证。
本文将小波变换良好的时频局域化特性和神经网络的自学习功能相结合,运用于故障诊断系统,使得整个系统具有较强的逼近能力和容错能力。
Permanent magnet direct-drive wind generation has been widely applied with its various advantages, such as simple structure, reliable operating technology and low cost in maintenance. Although it eliminates the need for a gearbox which has a high failure rate in doubly-fed wind generation, the full power converter is also one of the high failure-rate components. It is significant that studying the fault diagnosis system for the safe and steady operation of wind generation. The current fault diagnosis system of wind generation still needs to overcome such problems as large human consumption, much environmental affection and the inaccuracy of diagnosis.
In this paper, we study permanent magnet direct-drive wind generation, and the wind power converter main circuit topology is diode rectifier+boost chopper+voltage source PWM inverter. A set of fault diagnostic monitoring system of wind power converter is established.
Firstly, in this paper, we establish a permanent magnet direct-drive wind power system model and select the three-phase output current of the wind power converter to extract the fault feature. The normal operation of the wind power generation system as well as25kinds of open-circuit fault condition is simulated in the Matlab/Simulink software, and then the current waveforms in the case of normal operation and under different fault can be obtained. I have decomposed and restructured the three-phase output current by using wavelet analysis method, extracted the phase energy value, combined the average current parameters with the tectonic fault feature vectors, as well as listed all the fault of feature vectors.
Secondly, we select the fault feature vector constructed by using wavelet analysis and the average current parameters as input of BP neural network in this paper. According to fault feature vector and expectations characteristics, the structure of BP neural network can be determined. We learn and train the determined neural network, then test the fault phenomena that contain noise by using the trained neural network, and it verifies that the system has strong stability.
Finally, we desgine a monitoring interface for fault diagnostic monitoring system by LabVIEW software.And then links the wind power model in matlab and monitoring interface by using LabVIEW Simulation Toolkit, The system can display wind power converter's failure for simulation model in real time, and simulation is carried out.
In this paper wavelet transform time-frequency localized features and neural network is the combination of self-learning function, applied to fault diagnosis system making the whole system has a strong approximation ability and fault tolerance.
引文
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[17]Yang Wenxian, Tavner P J, Crabtree C J, et al. Cost effective condition monitoring for wind turbines[J]. IEEE Trans Industrial Electronics,2010, 57(1):263-271.
[18]Simon J W, Xiang B J, Yang Wenxian. Condition monitoring of the power output of wind turbine generators using wavelets[J].IEEE Trans on Energy Conversion,2010,25(3):715-721.
[19]郭鹏,David Infield,杨锡运.风电机组齿轮箱温度趋势状态监测及分析方法[J].中国电机工程学报,2011,32(31)：129-136.
[20]于辉,邓英.变速风力发电机变流器故障诊断方法[J].可再生能源.2010,28(3)：89-92.
[21]王占霞,张晓波.风电变流器故障诊断[J].电气制造,2011,12(2)：48-50.
[22]王占霞,张晓波.基于SOM网的风电变流器故障诊断[J].电网与清洁能源,2011,27(4)：35-38.
[23]张亮.基于小波神经网络的风电变流器故障诊断研究.重庆大学硕士学位论文[D].2011.4.
[24]Chuanwei Yang, Hui Liang, Jiuchun Jiang. Modeling and Simulation of AC-DC-AC Converter System for MW-Level Direct-Drive Wind Turbine grid Interface[J]. Power Electronics Specialists Conference,2006, 18(22):1-4.
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