Development of Wavelet and ANN-Based Algorithm in LabVIEW Environment for Classifying the Power Quality Disturbances

This paper presents an effective approach for categorizing Power Quality disturbances (PQDs) in the LabVIEW environment using Wavelet Transform (WT) and Artificial Neural Networks (ANN). Power quality has now become a key issue in the emerging power system. In order to improve the Power Quality (PQ), nowadays both utility and end user needs to monitor the PQ. Classification of PQDs is the main step involved in power quality monitoring. The work presented in this paper focuses on the development of power quality monitoring and classification algorithm in the LabVIEW environment. The voltage sag, swell, and interruptions are the most common PQDs that are considered in this research work. These disturbances are generated using the integral mathematical models of PQDs. The voltage signals corresponding to these PQDs are analyzed using the WT for feature extraction. The back propagation algorithm-based ANN classifier is then trained and tested using the extracted features. The results demonstrate that the proposed algorithm can effectively and efficiently classify the PQDs.