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The power system load equipment is more sensitive to power quality disturbances than equipment applied in the past. Therefore, the electric supply quality has become a major concern of electric utilities and end-users. A novel approach to detect and locate power quality disturbance in distributed power system combining wavelet transform with neural network is proposed. By performing decomposition of transient waveform, the original signal is divided into two parts: the low-frequency and the high-frequency, corresponding to approximation part and details part respectively. The paper aims at complex wavelet analysis, and then explores feature extraction of disturbance signal to obtain dynamic parameters, superior to real wavelet analysis result. The characteristic vector obtained from wavelet decomposition coefficients are input data of neural network for power quality disturbance pattern recognition. The improved training algorithm is used to complete the network parameter identification. By means of simulation and experimental data, the disturbance pattern can be obtained from the neural network output. The simulation results show that the proposed method is effective for transient signal analysis, taking advantage of complex wavelet transform and neural network.