Wavelet-based neural network for power disturbance classification

In this paper a wavelet-based neural network classifier for recognizing power quality disturbances is implemented and tested under various transient events. The discrete wavelet (DWT) technique is integrated with the probabilistic neural network (PNN) model to construct the classifier. First, the multi-resolution analysis (MRA) technique of DWT and the Parseval´s theorem are employed to extract the energy distribution features of the distorted signal at different resolution levels. Second, the PNN classifies these extracted features to identify the disturbance type according to the transient duration and the energy features. Since the proposed methodology can reduce a great quantity of the features of distorted signal without losing its original property less memory space and computing time are required. Various transient events tested, such as momentary interruption, capacitor switching, voltage sag/swell, harmonic distortion, and flicker show that the classifier can detect and classify different power disturbance types efficiently.