Wavelet-Based Neural Network for Power Disturbance Recognition and Classification

TLDR

The study develops and evaluates a prototype wavelet‑based neural‑network classifier for recognizing power‑quality disturbances. The classifier combines discrete wavelet transform feature extraction with a probabilistic neural network that uses energy‑distribution features across resolution levels to identify disturbance types. The approach reduces feature dimensionality, lowers memory and computation demands, and accurately detects and classifies transient events such as interruptions, switching, sag/swell, harmonic distortion, and flicker.

Abstract

In this paper, a prototype wavelet-based neural-network classifier for recognizing power-quality disturbances is implemented and tested under various transient events. The discrete wavelet transform (DWT) technique is integrated with the probabilistic neural-network (PNN) model to construct the classifier. First, the multiresolution-analysis technique of DWT and the Parseval's theorem are employed to extract the energy distribution features of the distorted signal at different resolution levels. Then, 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 distorted signal features 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.