Power disturbance classifier using wavelet-based neural network

Abstract

This paper presents a wavelet-based neural network technology for the detection and classification of the various types of power quality disturbances. For the detection and classification of the PQ transient signals, it should be done at the same time and the automatic methodology is recommended. In this paper, the hardware and software of the power quality data acquisition system (PQDAS) is proposed. In this system, the auto-classifying system combines the properties of the wavelet transform and the advantages of neural networks. Especially, the additional feature extraction to improve the recognition rate is considered. The configuration of the hardware of PQDAS and some case studies are also described.