Open AccessEvaluating windowing based continuous ST with deep learning for detection and classifying PQDs
Author(s) -
Kamarulazhar Daud,
M.S.F. Mansor,
ZH Che Soh,
AA Abd Samat,
M. L. Shafie,
AbdelRahman Ismail,
Mahreen Abdullah
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1088/1/012060
Subject(s) - interrupt , artificial intelligence , computer science , signal (programming language) , classifier (uml) , matlab , transient (computer programming) , signal processing , pattern recognition (psychology) , artificial neural network , feature (linguistics) , detector , electronic engineering , real time computing , engineering , computer hardware , digital signal processing , telecommunications , microcontroller , linguistics , philosophy , operating system , programming language
This paper discusses the performance of evaluating a windowing based continuous S-Transform (ST) with deep learning classifier for the detection and classifying of interrupt and transient disturbances. The primary purpose is to analyze the detection and classification of voltage interrupt and transient using ST as a signal processing technique. The detection technique is divided into half-cycle and one-cycle windowing techniques (WT with both cycles used for the purpose of comparison. The disturbances signal was create using MATLAB programming language and set in the form of m-file. ST was used to extract the significant feature in a form of scattering data from the disturbances signal. Then, the scattering data was used to build the detection interface inside the disturbances signal. The scattering data is an input for neural network (NN) to classify the percentage accuracy of the disturbances signal. This analysis presents the suitable windowing technique that can provide smooth detection and suitable characteristics to produce high accuracy percentages in the classification of power quality disturbances (PQDs).