Computational intelligence based technique for congestion management and compensation scheme in power system / Nur Zahirah Mohd Ali

Congestion problem is a crucial issue in power system. Its occurrence is closely related to loss increment and voltage decay in power system. The increment of load in a transmission system is one of the main factors that causes current increase. This leads to loss increment, while at the same time a...

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التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Mohd Ali, Nur Zahirah
التنسيق:	أطروحة
اللغة:	English
منشور في:	2019
الموضوعات:	TK Electrical engineering Electronics Nuclear engineering
الوصول للمادة أونلاين:	https://ir.uitm.edu.my/id/eprint/82783/1/82783.pdf
الوسوم:	إضافة وسم لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!

id	my-uitm-ir.82783
record_format	uketd_dc
spelling	my-uitm-ir.827832024-01-10T00:47:30Z Computational intelligence based technique for congestion management and compensation scheme in power system / Nur Zahirah Mohd Ali 2019 Mohd Ali, Nur Zahirah TK Electrical engineering. Electronics. Nuclear engineering Congestion problem is a crucial issue in power system. Its occurrence is closely related to loss increment and voltage decay in power system. The increment of load in a transmission system is one of the main factors that causes current increase. This leads to loss increment, while at the same time affecting the congestion event in the system. The impact leads to the increment in generation cost during congestion. Therefore, congestion management needs to be performed properly in order to deliver enough power to the system resulted by transmission line congestion. Failure to handle this situation may lead to bigger problems such as voltage collapse and cascading blackout. This thesis presents computational intelligence-based technique for congestion management and compensation scheme in power systems. In this study, a new model termed as Integrated Multilayer Artificial Neural Networks (IMLANNs) is developed to predict congested line and voltage stability index separately. Consequently, a new optimization technique termed as Clonal Evolutionary Particle Swarm Optimization (CEPSO) was developed. CEPSO integrates the element of cloning and swarm in the original Evolutionary Programming algorithm. CEPSO is initially used to optimize the location and sizing of FACTS devices for compensation scheme. In this study, Static VAR Compensator (SVC) and Thyristor Control Static Compensator (TCSC) are the two chosen Flexible AC Transmission System (FACTS) devices used in this compensation scheme. 2019 Thesis https://ir.uitm.edu.my/id/eprint/82783/ https://ir.uitm.edu.my/id/eprint/82783/1/82783.pdf text en public phd doctoral Universiti Teknologi MARA (UiTM) Faculty of Electrical Engineering Musirin, Ismail
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Musirin, Ismail
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Mohd Ali, Nur Zahirah Computational intelligence based technique for congestion management and compensation scheme in power system / Nur Zahirah Mohd Ali
description	Congestion problem is a crucial issue in power system. Its occurrence is closely related to loss increment and voltage decay in power system. The increment of load in a transmission system is one of the main factors that causes current increase. This leads to loss increment, while at the same time affecting the congestion event in the system. The impact leads to the increment in generation cost during congestion. Therefore, congestion management needs to be performed properly in order to deliver enough power to the system resulted by transmission line congestion. Failure to handle this situation may lead to bigger problems such as voltage collapse and cascading blackout. This thesis presents computational intelligence-based technique for congestion management and compensation scheme in power systems. In this study, a new model termed as Integrated Multilayer Artificial Neural Networks (IMLANNs) is developed to predict congested line and voltage stability index separately. Consequently, a new optimization technique termed as Clonal Evolutionary Particle Swarm Optimization (CEPSO) was developed. CEPSO integrates the element of cloning and swarm in the original Evolutionary Programming algorithm. CEPSO is initially used to optimize the location and sizing of FACTS devices for compensation scheme. In this study, Static VAR Compensator (SVC) and Thyristor Control Static Compensator (TCSC) are the two chosen Flexible AC Transmission System (FACTS) devices used in this compensation scheme.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Mohd Ali, Nur Zahirah
author_facet	Mohd Ali, Nur Zahirah
author_sort	Mohd Ali, Nur Zahirah
title	Computational intelligence based technique for congestion management and compensation scheme in power system / Nur Zahirah Mohd Ali
title_short	Computational intelligence based technique for congestion management and compensation scheme in power system / Nur Zahirah Mohd Ali
title_full	Computational intelligence based technique for congestion management and compensation scheme in power system / Nur Zahirah Mohd Ali
title_fullStr	Computational intelligence based technique for congestion management and compensation scheme in power system / Nur Zahirah Mohd Ali
title_full_unstemmed	Computational intelligence based technique for congestion management and compensation scheme in power system / Nur Zahirah Mohd Ali
title_sort	computational intelligence based technique for congestion management and compensation scheme in power system / nur zahirah mohd ali
granting_institution	Universiti Teknologi MARA (UiTM)
granting_department	Faculty of Electrical Engineering
publishDate	2019
url	https://ir.uitm.edu.my/id/eprint/82783/1/82783.pdf
_version_	1794191945655910400

Computational intelligence based technique for congestion management and compensation scheme in power system / Nur Zahirah Mohd Ali

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