The accurate identification of fault types is the primary measure when disposing grid faults. Based on singular value decomposition（SVD） of time-frequency matrix and support vector machine（SVM）

a method to identify distribution network faults is proposed. By applying local characteristic-scale decomposition（LCD）

Hilbert transform as well as band-pass filter on the voltage waveforms of grid bus and current waveforms of low-voltage inlet line of the main transformer

a group of time-frequency matrices are constructed. Then these time-frequency matrices are decomposed by SVD to obtain corresponding singular spectra

of which the distributed parameters are extracted as feature vectors. By taking these vectors as the input of multilevel SVM

grid faults can be identified. The results of simulation and experiments under typical working conditions show that the accurate rate of proposed method can reach 90%

revealing that the identification of various faults can be realized effectively. In addition

the proposed method is highly adaptable and practicable.