Optimal under voltage load shedding based on stability index by using artificial neural network

Power system is exceptionally sensitive at the generation and consumer side. Inconsistent power requirement under general power production environment may cause power system to approach breakdown or power outages. Load shedding is deliberated as the final choice from the numerous techniques which ha...

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Main Author:	Sharman, Sundarajoo
Format:	Thesis
Language:	English English English
Published:	2020
Subjects:	QA76 Computer software
Online Access:	http://eprints.uthm.edu.my/397/1/24p%20SUNDARAJOO%20SHARMAN.pdf http://eprints.uthm.edu.my/397/2/SUNDARAJOO%20SHARMAN%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/397/3/SUNDARAJOO%20SHARMAN%20WATERMARK.pdf
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spelling	my-uthm-ep.3972021-07-25T02:19:52Z Optimal under voltage load shedding based on stability index by using artificial neural network 2020-01 Sharman, Sundarajoo QA76 Computer software Power system is exceptionally sensitive at the generation and consumer side. Inconsistent power requirement under general power production environment may cause power system to approach breakdown or power outages. Load shedding is deliberated as the final choice from the numerous techniques which have been achieved to prevent voltage breakdown. Various studies have been led on this part of the issue. Still, there are possibilities for other ways through optimization of the load shedding. Consequently, the primary reason for this work is to come up with an optimal undervoltage load shedding strategy. Voltage stability is one of the significant worries in functional and preparation of present-day power system. Nevertheless, to obtain the lowest amount to be shed in order to avoid voltage instability, optimization is required. An algorithm was developed to shed the optimal load by considering the load priority whereby the load with least priority will be shed first. The algorithm is working in one step to shed the load. The developed algorithm was tested on IEEE 33-Bus and IEEE 69-Bus radial distribution systems. The results show the equal accuracy of the application of the developed algorithm. In this project, a powerful technique is exhibited for evaluating the optimal amount of load to be shed in a radial distribution system by using artificial neural network. The results of these test cases confirm that 6.57% of bus voltage is increased at the weakest bus in the IEEE 33-Bus system and 10.23% of bus voltage is increased at the weakest bus in the IEEE 69-Bus system. This optimal load shedding algorithm does not over shed or under shed the load. Other achievement includes reduction in load shedding steps. For each test case, the complete load shedding was achieved in 1 step only and the amount of load shed is suitable in each test case respectively. In this project, 29.4% of load is curtailed to stabilize the system which is less compared to other works where about 30% of load is shed to stabilize the system. 2020-01 Thesis http://eprints.uthm.edu.my/397/ http://eprints.uthm.edu.my/397/1/24p%20SUNDARAJOO%20SHARMAN.pdf text en public http://eprints.uthm.edu.my/397/2/SUNDARAJOO%20SHARMAN%20COPYRIGHT%20DECLARATION.pdf text en staffonly http://eprints.uthm.edu.my/397/3/SUNDARAJOO%20SHARMAN%20WATERMARK.pdf text en validuser mphil masters Universiti Tun Hussein Onn Malaysia Fakulti Kejuruteraan Elektrik dan Elektronik
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English English English
topic	QA76 Computer software
spellingShingle	QA76 Computer software Sharman, Sundarajoo Optimal under voltage load shedding based on stability index by using artificial neural network
description	Power system is exceptionally sensitive at the generation and consumer side. Inconsistent power requirement under general power production environment may cause power system to approach breakdown or power outages. Load shedding is deliberated as the final choice from the numerous techniques which have been achieved to prevent voltage breakdown. Various studies have been led on this part of the issue. Still, there are possibilities for other ways through optimization of the load shedding. Consequently, the primary reason for this work is to come up with an optimal undervoltage load shedding strategy. Voltage stability is one of the significant worries in functional and preparation of present-day power system. Nevertheless, to obtain the lowest amount to be shed in order to avoid voltage instability, optimization is required. An algorithm was developed to shed the optimal load by considering the load priority whereby the load with least priority will be shed first. The algorithm is working in one step to shed the load. The developed algorithm was tested on IEEE 33-Bus and IEEE 69-Bus radial distribution systems. The results show the equal accuracy of the application of the developed algorithm. In this project, a powerful technique is exhibited for evaluating the optimal amount of load to be shed in a radial distribution system by using artificial neural network. The results of these test cases confirm that 6.57% of bus voltage is increased at the weakest bus in the IEEE 33-Bus system and 10.23% of bus voltage is increased at the weakest bus in the IEEE 69-Bus system. This optimal load shedding algorithm does not over shed or under shed the load. Other achievement includes reduction in load shedding steps. For each test case, the complete load shedding was achieved in 1 step only and the amount of load shed is suitable in each test case respectively. In this project, 29.4% of load is curtailed to stabilize the system which is less compared to other works where about 30% of load is shed to stabilize the system.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Sharman, Sundarajoo
author_facet	Sharman, Sundarajoo
author_sort	Sharman, Sundarajoo
title	Optimal under voltage load shedding based on stability index by using artificial neural network
title_short	Optimal under voltage load shedding based on stability index by using artificial neural network
title_full	Optimal under voltage load shedding based on stability index by using artificial neural network
title_fullStr	Optimal under voltage load shedding based on stability index by using artificial neural network
title_full_unstemmed	Optimal under voltage load shedding based on stability index by using artificial neural network
title_sort	optimal under voltage load shedding based on stability index by using artificial neural network
granting_institution	Universiti Tun Hussein Onn Malaysia
granting_department	Fakulti Kejuruteraan Elektrik dan Elektronik
publishDate	2020
url	http://eprints.uthm.edu.my/397/1/24p%20SUNDARAJOO%20SHARMAN.pdf http://eprints.uthm.edu.my/397/2/SUNDARAJOO%20SHARMAN%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/397/3/SUNDARAJOO%20SHARMAN%20WATERMARK.pdf
_version_	1747830600618737664

Optimal under voltage load shedding based on stability index by using artificial neural network

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