Study On State Monitoring And Fault Diagnosis Of Water-Wheel Generator Set Based On Hilbert-Huang Transform(HHT) And Support Vector Machine

High reliability of electrical power system demands that a fault diagnosis system should find out failures of a water-wheel generator set in time, make a suggestion to correct them and give a optimistic scheme for operating as the states of the generator set. However, the traditional fault diagnosis method based on Fourier transform(FT) can only find failures from frequencies of vibration signals and can not know what time and how the failures will happen.This thesis investigates methodology of the state monitoring and fault diagnosis of a water-wheel generator set. Firstly the Hilbert-Huang transform is deeply studied. After that, it is applied into a fault diagnosis system of a water-wheel generator set. By this method, signals are decomposed by empirical mode decomposition (EMD) firstly and the intrinsic mode functions (IMFs) are obtained. Then Hilbert spectrum is obtained by Hilbert transform. Abnormal frequencies and their occurring time can be discovered from the Hilbert spectrum. So do the failures of the generator set.Applications of the Hilbert-Huang transform are restricted because of its point effect. Armed with analyzing the principle of point effect in detail, two methods of dealing with it are presented. The one is an alterable length extremism mirroring extension algorithm. The algorithm does not like the close mirroring extension algonithm which can produce an amount of data. Moreover, it has good effect by taking extension extremism length in the actual condition. The other is a point extension algorithm based on the least square support vector machine. This algorithm makes full use of the character of short transient vibration signal data of the water-wheel generator set and optimal advantage of the least square support vector machine with limited samples. So it can solve the point effect more effectively.It is difficult to distill signal characters for the fault diagnosis. With the character of vibration signals of a water-wheel generator set, the thesis gives two algorithms to distill signalcharaters. The first algorithm is based on EMD and AR mode to distill signal characters from the wave form. This algorithm takes four parameters as inputs of an intelligent recognizing system from every IMF; while the second algorithm is based on energy and it is to make energy of IMF as inputs of the intelligent recognizing system. Both the algorithms can transform signal characters into digital characters and they are the basis of the fault diagnosis.At the end of this thesis, Hilbert-Huang transform is used in fault diagnosis system of No.1 water-wheel generator set of SUO FENG YING Its performance and vibration reasons are analyzed. Least square classification vector methane is applied to the fault diagnosis intelligent recognizing system of the water-wheel generator set and results are analyzed. They indicate that state monitoring and fault diagnosis system based on Hilbert-Huang transform and support vector machine can give a good estimate for the performance of water-wheel generator set and locate the failures of the generator set. Thus, it is worth of spreading and application.