Identification of Power Quality Issues using Kalman Filter

A Kalman filter - generalized regression neural network based technique for the classification of power quality disturbances is presented here. A two stage classifier is proposed in which feature extraction is performed using a Kalman filter. Amplitude of the waveforms is the feature extracted from the Kalman filter and given as input to generalized regression neural network which classifies power quality disturbances. Distored waveforms on the power system model were simulated using Matlab software. Five classes of PQ disturbances were classified and the performance evaluation has been done using 500 signals, with a sampling rate of 128 per cycle of each signal.