Research On The Wind Turbine Vibration Monitoring And Fault Diagnosis

As the increase of the wind turbine unit capacity and the new installed capacity every year, the relative tertiary industry such as maintenance, monitoring and fault diagnosis will be a new growth point in the wind industry. As the wind turbine work environment is very poor and the wind has high instability, the alternant force makes the transmission system the much easier damaged components in wind turbine. The wind turbine is installed in remote areas and is high from the ground, which makes it difficult to maintenance. Therefore the condition monitoring and fault diagnosis of the wind turbine in this case has significant meaning. Some research work shows that the vibration fault in transmission system has higher proportion compared to other wind turbine parts, and the condition monitoring to the transmission system is important. Currently most large commercial wind turbine has their own kinds of Supervisor Control And Data Acquisition (SCADA) systems, which supplied strong technology flat roof and sustentation for the improvement of the wind farm's stability and reliability. However, at the same time, the quality of the SCADA systems can't satisfy the needs of the vibration monitoring and fault diagnosis. It has relatively simple analysis functions and is lack of time-frequency analysis method which has better effect in dealing with no-stationary signals. The SCADA system is also lack of vibration monitoring and relative analysis. Though the WindCon system aimed at the wind turbine vibration fault monitoring has the early warning and alarming functions, it at the same time is lack of precision fault diagnosis function. Therefore this paper select the transmission system as the keystone monitoring objects and carry out the research work on the vibration monitoring and fault diagnosis. The main research work and conclusion are as follows:①The components such as wheels, gear box and generator in the transmission system and its vibration fault characteristic are analyzed in detail, which helps to ensure the measurement point positions in monitoring. The wind turbine vibration signal is disturbed by the background noise and the fault components such as bearing and gear in the rotation part shows cycle non-stationary characteristic. At the same time the alternate load by the non-stationary wind forced on the transmission system, makes the vibration signal showing Gauss noise immingled and non-linearity characteristic. Through the analysis on the key components in the gear-box, the mesh frequency and fault frequency are calculated and the monitoring points are confirmed. Then the vibration sensors can be settled and the vibration signal can be collected.②In the pretreatment research of the wind turbine vibration signal, a new method based on cross validation method optimized Morlet wavelet is put forward and discussed in detail. In the wind turbine structures the signals under considerations are known to be non-stationary, for which the signal parameters are time-varying. But for early fault signals, the fault feature signal is not strong enough to be caught, which can be drowned in the strong noise signals. In this case the traditional filter methods can't separate the noise and useful components. The wavelet de-nosing method has better analysis affect but at the same time has some difficult in the selection of wavelet base and decomposition level. Aimed on the characteristic that the wind turbine work condition is rush and full of strong noise pollution, an adaptive wavelet de-noising method was proposed according to the inverse characteristics of useful signal and noise in different wavelet scales and the limitation of the traditional threshold methods. Then a new de-noising method based on parameter optimized Morlet wavelet is put forward. The simulation and experiment results reveal that both these two methods can considerably improves the capability of feature extraction and incipient fault diagnosis under strong noise background.③Aimed at the cycle non-stationary characteristic of the wind turbine vibration signal, a fault diagnosis method based on auto term window repressed Wigner-Ville distribution (WVD) is discussed in detail. As the wind turbine vibration signal has cycle non-stationary characteristic, the simple time domain methods and frequency domain methods can't obtain perfect effect. The time-frequency methods have good effect in dealing with no-stationary signals, in which the WVD theoretically has an infinite resolution in time-frequency domain, is chosen to extract feature of the wind turbine vibration signal. But the WVD has the fault in cross term interface, which need to be suppressed by appropriate methods in the feature extraction analysis. Based on the relationship between the auto terms and the cross terms of WVD, a new threshold adaptive short-time Fourier transform (ASTFT) method is put forward. Then the auto term window suppressed WVD feature extraction method is discussed in detail. The auto term window is designed based on the smoothed pseudo Wigner-Ville distribution (SPWVD) and takes the place of the auto term in window analysis. All these three methods can not only remove the cross terms efficiently, but also reserve most advantage of WVD at the same time. The simulation and experiment results show that the proposed methods are validity tools for TFR of multi-component non-stationary signals in feature extraction.④Aimed at the non-gaussian and non-linearity characteristic of the wind turbine vibration signal, a fuzzy high-order spectrum fault diagnosis method is presented. This method can not only de-noises the Gauss noise in the vibration signal, but also has good effect in analyzing the no-linearity characteristic vibration signal and realize the correct fault diagnosis. At first the research using bi-spectrum analysis on the rolling bearing fault vibration signal in different fault styles show that, the bi-spectrum analysis results has relationship with the fault styles and this relationship has no effects by the rotate speed. On the base of the bi-spectrum analysis threshold result, the target template combined of kernel map and region map is constructed. Then by testing the distance between the test sample and the target template, the different fault can be distinguished on the value of the distance. Theoretical analysis and rolling bearing fault diagnosis show that the new method has good validity in fault diagnosis and the classification of all the test samples are correct.⑤In the pilot study on the wind turbine vibration monitoring and fault diagnosis system, the system and the design and realization of hardware and software are discussed in detail. Investigating the system structure of the parameter-sharing module software, the uniform frame work of the system module and the apparatus interface are designed. The wind turbine vibration monitoring and fault diagnosis system is in principium exploited based on the methods in this paper, which supplies strong help to the fault character extraction and fault diagnosis. The project applications and wind turbine vibration analysis proved the software be practical and availability. At the end of the thesis, the summarization of the article and expectation of the relative technology development are presented.