Classification of Voltage Sag Using Arduino Uno and One Versus One Support Vector Machine (OVO-SVM)

Classification of Voltage Sag Using Arduino Uno and One Versus One Support Vector Machine (OVO-SVM)

Voltage sags or dips are one of the power qualities (PQ) disturbances in distribution system (DN). This problem can disrupt sensitive equipment and, if severe enough, result in power outages. Extraction of voltage sag features, such as amplitude, duration, and frequency of sags, can assist power sys...

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Published in:2024 IEEE 4th International Conference in Power Engineering Applications: Powering the Future: Innovations for Sustainable Development, ICPEA 2024
Main Author: Yusoh M.A.T.M.; Sahari M.S.I.; Abidin A.F.; Mohamad N.Z.; Zakaria M.I.; Kassim A.H.
Format: Conference paper
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2024
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85191748599&doi=10.1109%2fICPEA60617.2024.10499163&partnerID=40&md5=d256834d741b4fc12d9248c28a96c4a3
id 2-s2.0-85191748599
spelling 2-s2.0-85191748599
Yusoh M.A.T.M.; Sahari M.S.I.; Abidin A.F.; Mohamad N.Z.; Zakaria M.I.; Kassim A.H.
Classification of Voltage Sag Using Arduino Uno and One Versus One Support Vector Machine (OVO-SVM)
2024
2024 IEEE 4th International Conference in Power Engineering Applications: Powering the Future: Innovations for Sustainable Development, ICPEA 2024


10.1109/ICPEA60617.2024.10499163
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85191748599&doi=10.1109%2fICPEA60617.2024.10499163&partnerID=40&md5=d256834d741b4fc12d9248c28a96c4a3
Voltage sags or dips are one of the power qualities (PQ) disturbances in distribution system (DN). This problem can disrupt sensitive equipment and, if severe enough, result in power outages. Extraction of voltage sag features, such as amplitude, duration, and frequency of sags, can assist power system operators and engineers in better understanding the causes and impacts of these events, as well as developing mitigation strategies. However, the device for monitoring the PQ disturbances is very expensive and cannot be affordable. This research paper focused on classification of voltage sag on different types of bulbs using low-cost microcontroller of Arduino Uno and one versus one support vector machine (OVOSVM) learning. So, AC Voltage module, Arduino Uno, and MATLAB software are the apparatus used to record the real-time signal of voltage sag. Then, advanced signal processing of S-transform (ST) is applied to extract significant features of voltage sag that used as an input for classifier tools of OVOSVM. After extracting the features, OVO-SVM will be performed using Linear Kernel and Radial Basis Function (RBF). The accuracy of these two Kernel SVMs will be compared and evaluated to determine the best method for classifying voltage sag characteristics. Result shows the classification OVO-SVM using RBF Kernel is the best compared to the Linear Kernel, where its accuracy is 90.0%. © 2024 IEEE.
Institute of Electrical and Electronics Engineers Inc.

English
Conference paper
author Yusoh M.A.T.M.; Sahari M.S.I.; Abidin A.F.; Mohamad N.Z.; Zakaria M.I.; Kassim A.H.
spellingShingle Yusoh M.A.T.M.; Sahari M.S.I.; Abidin A.F.; Mohamad N.Z.; Zakaria M.I.; Kassim A.H.
Classification of Voltage Sag Using Arduino Uno and One Versus One Support Vector Machine (OVO-SVM)
author_facet Yusoh M.A.T.M.; Sahari M.S.I.; Abidin A.F.; Mohamad N.Z.; Zakaria M.I.; Kassim A.H.
author_sort Yusoh M.A.T.M.; Sahari M.S.I.; Abidin A.F.; Mohamad N.Z.; Zakaria M.I.; Kassim A.H.
title Classification of Voltage Sag Using Arduino Uno and One Versus One Support Vector Machine (OVO-SVM)
title_short Classification of Voltage Sag Using Arduino Uno and One Versus One Support Vector Machine (OVO-SVM)
title_full Classification of Voltage Sag Using Arduino Uno and One Versus One Support Vector Machine (OVO-SVM)
title_fullStr Classification of Voltage Sag Using Arduino Uno and One Versus One Support Vector Machine (OVO-SVM)
title_full_unstemmed Classification of Voltage Sag Using Arduino Uno and One Versus One Support Vector Machine (OVO-SVM)
title_sort Classification of Voltage Sag Using Arduino Uno and One Versus One Support Vector Machine (OVO-SVM)
publishDate 2024
container_title 2024 IEEE 4th International Conference in Power Engineering Applications: Powering the Future: Innovations for Sustainable Development, ICPEA 2024
container_volume
container_issue
doi_str_mv 10.1109/ICPEA60617.2024.10499163
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85191748599&doi=10.1109%2fICPEA60617.2024.10499163&partnerID=40&md5=d256834d741b4fc12d9248c28a96c4a3
description Voltage sags or dips are one of the power qualities (PQ) disturbances in distribution system (DN). This problem can disrupt sensitive equipment and, if severe enough, result in power outages. Extraction of voltage sag features, such as amplitude, duration, and frequency of sags, can assist power system operators and engineers in better understanding the causes and impacts of these events, as well as developing mitigation strategies. However, the device for monitoring the PQ disturbances is very expensive and cannot be affordable. This research paper focused on classification of voltage sag on different types of bulbs using low-cost microcontroller of Arduino Uno and one versus one support vector machine (OVOSVM) learning. So, AC Voltage module, Arduino Uno, and MATLAB software are the apparatus used to record the real-time signal of voltage sag. Then, advanced signal processing of S-transform (ST) is applied to extract significant features of voltage sag that used as an input for classifier tools of OVOSVM. After extracting the features, OVO-SVM will be performed using Linear Kernel and Radial Basis Function (RBF). The accuracy of these two Kernel SVMs will be compared and evaluated to determine the best method for classifying voltage sag characteristics. Result shows the classification OVO-SVM using RBF Kernel is the best compared to the Linear Kernel, where its accuracy is 90.0%. © 2024 IEEE.
publisher Institute of Electrical and Electronics Engineers Inc.
issn
language English
format Conference paper
accesstype
record_format scopus
collection Scopus
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