Voltage sags and transient detection and classification using half/one-cycle windowing techniques based on continuous s-transform with neural network

• Main Authors: Abdullah M.H.R.O (Author), Abidin, A.F (Author), Ali M.N.M (Author), Bermakai M. (Author), Daud, K. (Author), Hussain Z. (Author), Ibrahim A. (Author), Ismail, A.P (Author), Jamalludin D. (Author), Jumidali M.M (Author), Mahmood A. (Author), Mukhtar N.M (Author), Yusof A.M (Author), Yusoff M.Z.M (Author)
• Format: Article
• Language: English
• Published: American Institute of Physics Inc. 2017
• Online Access: View Fulltext in Publisher; View in Scopus

 This paper was conducted to detect and classify the different power quality disturbance (PQD) using Half and One-Cycle Windowing Technique (WT) based on Continuous S-Transform (CST) and Neural Network (NN). The system using 14 bus bars based on IEEE standard had been designing using MATLAB©Simulink to provide PQD data. The datum of PQD is analyzed by using WT based on CST to extract features and it characteristics. Besides, the study focused an important issue concerning the identification of PQD selection and detection, the feature and characteristics of two types of signals such as voltage sag and transient signal are obtained. After the feature extraction, the classified process had been done using NN to show the percentage of classification PQD either voltage sags or transients. The analysis show which selection of cycle for windowing technique can provide the smooth detection of PQD and the suitable characteristic to provide the highest percentage of classification of PQD. © 2017 Author(s).