A novel intelligent system for analysis and recognition of power quality disturbance signal

The power quality disturbances and the resulting problems have emerged as an important research. The power system industries with sensitive electrical loads have become more dependent on the quality of power supply system. The power quality disturbances analysis is becoming an essential issue because of the widespread use of electronic nonlinear loads that have affected the operation of distributed power system network in residential and industrial areas. A novel approach to detect and locate short duration disturbance in distributed power system combing neural network is presented. The paper tries to explain to investigate feature extraction of transient signal and to analyze the disturbance signal. The feature information obtained from wavelet decomposition coefficients acts as input vector of wavelet network for power quality disturbance pattern recognition. The power quality disturbance recognition performance is completed and the improved back-propagation algorithm is used to fulfill the network parameter initialization. By means of simulation data training, the disturbance pattern can be obtained from the trained wavelet network output. The simulation results and analysis indicate that the wavelet transform combining with neural network is sensitive to transient signal singularity detection.