Application of Wavelet Transform for Fault Pattern Recognition and Analysis of Power System Generator
The high-capacity turbine-generator set has been widely used in power system as an important power supply and its operating condition is under mal-condition, so keeping it running in safety status is essential. A novel approach using wavelet neural network is proposed for transient vibration signal processing and fault pattern classification. In signal acquiring, the occurrence of transient signal makes the waveform nonstationary, especially during the start-up of turbo-generator. By means of wavelet transform, the transient signal can be decomposed into series of wavelet subspaces, each of which covers a specific octave frequency band in time-frequency. The effective eigenvectors are acquired by orthonormal wavelet transform based on multi-resolution analysis, which is called feature extraction. These feature vectors are applied to the neural network for training and testing. The neural network has three advantageous: data driven learning, local interconnections and good convergence property. The improved training algorithm based on recursive orthogonal least squares is utilized to accomplish network parameter initialization. By means of proper samples selection and network parameter adjustment, the fault pattern can be determined from the network output values. The simulation results and applications show that the proposed method is effective and the diagnosis result is correct.
Turbo-generator set wavelet transform neural network feature eztraction fault diagnosis pattern recognition
Kang Shanlin Kang Yuzhe Zhang Huanzhen
Hebei University of Engineering, Handan 056038,China Beijing University of Chemical Technology, Beijing 100029, China
国际会议
2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)
广西桂林
英文
3908-3911
2009-06-17（万方平台首次上网日期，不代表论文的发表时间）