Evaluating windowing based continuous S-transform with deep learning classifier for detection and classifying interrupt and transient disturbances / Muhamad Badrul Amin Mansor

Mansor, Muhamad Badrul Amin (2020) Evaluating windowing based continuous S-transform with deep learning classifier for detection and classifying interrupt and transient disturbances / Muhamad Badrul Amin Mansor. [Student Project] (Unpublished)

Abstract

This project discuss the performance of evaluating windowing based continuous S-Transform with deep learning classifier for detection and classifying interrupt and transient disturbances. The primary purpose was to analyse the detection and classification of voltage interrupt and transient using S-Transform (ST) as signal processing technique. The detection technique was divided into half-cycle and one- cycle windowing technique (WT), both cycle is used for the purpose of comparison. The disturbances signal was create using MATLAB programming language and set in form of m-file. S-Transform (ST) was used to extract the significant feature in a form of scattering data from disturbances signal. Then, the scattering data was used to build the detection interface inside the disturbances signal. The scattering data also was actual an input for extreme learning machine neural network (ELMNN) in order to classify the percentage accuracy of the disturbance signal. In addition, this analysis was verified that by using windowing technique of half-cycle and one-cycle it can provide clear detection in detecting the existent of disturbance within electrical distribution signal. Extreme learning machine was one of the suitable neural network to be used in classifying power quality disturbance due to its advantage in processing large dataset and able to produces high percentages accuracy. This project also includes 3 type of different sample for each disturbance which were interrupt and transient for the comparison purpose.

Metadata

Item Type: Student Project
Creators:
Creators
Email / ID Num.
Mansor, Muhamad Badrul Amin
2017668584
Contributors:
Contribution
Name
Email / ID Num.
Thesis advisor
Haji Daud, Kamarulazhar
UNSPECIFIED
Subjects: T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Electronics
T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Electronics > Apparatus and materials
T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Electronics > Apparatus and materials > Detectors. Sensors. Sensor networks
T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Electronics > Applications of electronics
Divisions: Universiti Teknologi MARA, Pulau Pinang > Permatang Pauh Campus > Faculty of Electrical Engineering
Programme: Bachelor of Engineering (Hons) Electrical And Electronic Engineering
Keywords: S-Transform (ST), Windowing Technique (WT), MATLAB, Extreme Learning Machine Neural Network (ELMNN)
Date: July 2020
URI: https://ir.uitm.edu.my/id/eprint/39879
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