Automatic detection of power quality disturbances and identification of transient signals

Many works involving detection and classification of power quality (PQ) events report the use of artificial neural network (ANN) to perform the classification task. No doubt, many have found ANN successfully performs the required task, but the approach requires a long training process and is too rigid if expansion or modification is desired. This paper proposes an alternative approach for the detection of PQ disturbances, which is simple, expandable and does not require training. The proposed system is built and tested using field-measured voltage waveforms, which are made of five types of PQ disturbances, namely, impulsive transient, oscillatory transient, single notch, repetitive notch and voltage sag. It perfectly detects and categorizes all test frames as either "clean" or "not clean", in which the frame labeled as "not clean" consists of some form of PQ disturbances. Results show that the frame identification consisting of impulsive and oscillatory transient disturbances achieved an overall accuracy rate of nearly 95%