Hybrid intelligent methods for parameter identification and load frequency control in power system

The accuracy of the parameter identification of power system model and efficiency of frequency control are part of the challenging work in power system operation and control area. Whereas, the complexity and high non-linearty of the power system model have led to the continuing research for improvem...

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Main Author:	Aqeel Sakhy, Jaber
Format:	Thesis
Language:	English
Published:	2014
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/11510/19/Hybrid%20intelligent%20methods%20for%20parameter%20identification%20and%20load%20frequency%20control%20in%20power%20system.pdf
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spelling	my-ump-ir.115102021-11-02T04:35:23Z Hybrid intelligent methods for parameter identification and load frequency control in power system 2014-11 Aqeel Sakhy, Jaber TK Electrical engineering. Electronics Nuclear engineering The accuracy of the parameter identification of power system model and efficiency of frequency control are part of the challenging work in power system operation and control area. Whereas, the complexity and high non-linearty of the power system model have led to the continuing research for improvement that still extensively active, especially for load frequency control (LFC). Generally, LFC is responsible to maintain the zero steady-state errors in the frequency changing and restoring the natural frequency to its normal position. Many methods have been proposed and implemented in identification of power system and LFC, however, they may not be appropriate. For example, the classical methods for parameter identification (LSE and MLE), the classical methods for LFC (PI, PD and PID) and the intelligent methods (fuzzy logic, neural network, genetic algorithm, and PSO). Thus, motivated from the topics, this Thesis is brought to present the improvement of the parameter identification of power system model and the response of the LFC in power system. The Thesis is divided into two parts in accordance to the topic. Where, in the first part, the coherent identification algorithm for single and multi-area power systems with disturbances is proposed. A new method from the improvement of Particle Swarm Optimization (PSO) is developed in order to find the best global optimal value. Meanwhile, part two presents three developed control methods for FLC from the improvement of fuzzy control (named as scaled fuzzy using PSO, parallel conventional PI/PD with Scaled Fuzzy PI/PD and Mirror Fuzzy controller) by adapting the utilization of PSO to optimize the scaled gain of fuzzy controllers. These proposed control methods in LFC will be examined and verified in two and four areas power system. The outcomes of the proposed parameters identification and LFC control methods are presented the results through simulation using Matlab by making a comparison on the frequency transient response. Various analyses are shown and the discussions on the results are done appropriately. Lastly, the Thesis is given the concluding remarks and the contributions which can be specified into two, a modification of PSO for parameters identification named as PSO segmentation and a new fuzzy control named as a Mirror Fuzzy controller for LFC 2014-11 Thesis http://umpir.ump.edu.my/id/eprint/11510/ http://umpir.ump.edu.my/id/eprint/11510/19/Hybrid%20intelligent%20methods%20for%20parameter%20identification%20and%20load%20frequency%20control%20in%20power%20system.pdf pdf en public phd doctoral Universiti Malaysia Pahang Faculty of Electrical & Electronics Engineering
institution	Universiti Malaysia Pahang Al-Sultan Abdullah
collection	UMPSA Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Aqeel Sakhy, Jaber Hybrid intelligent methods for parameter identification and load frequency control in power system
description	The accuracy of the parameter identification of power system model and efficiency of frequency control are part of the challenging work in power system operation and control area. Whereas, the complexity and high non-linearty of the power system model have led to the continuing research for improvement that still extensively active, especially for load frequency control (LFC). Generally, LFC is responsible to maintain the zero steady-state errors in the frequency changing and restoring the natural frequency to its normal position. Many methods have been proposed and implemented in identification of power system and LFC, however, they may not be appropriate. For example, the classical methods for parameter identification (LSE and MLE), the classical methods for LFC (PI, PD and PID) and the intelligent methods (fuzzy logic, neural network, genetic algorithm, and PSO). Thus, motivated from the topics, this Thesis is brought to present the improvement of the parameter identification of power system model and the response of the LFC in power system. The Thesis is divided into two parts in accordance to the topic. Where, in the first part, the coherent identification algorithm for single and multi-area power systems with disturbances is proposed. A new method from the improvement of Particle Swarm Optimization (PSO) is developed in order to find the best global optimal value. Meanwhile, part two presents three developed control methods for FLC from the improvement of fuzzy control (named as scaled fuzzy using PSO, parallel conventional PI/PD with Scaled Fuzzy PI/PD and Mirror Fuzzy controller) by adapting the utilization of PSO to optimize the scaled gain of fuzzy controllers. These proposed control methods in LFC will be examined and verified in two and four areas power system. The outcomes of the proposed parameters identification and LFC control methods are presented the results through simulation using Matlab by making a comparison on the frequency transient response. Various analyses are shown and the discussions on the results are done appropriately. Lastly, the Thesis is given the concluding remarks and the contributions which can be specified into two, a modification of PSO for parameters identification named as PSO segmentation and a new fuzzy control named as a Mirror Fuzzy controller for LFC
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Aqeel Sakhy, Jaber
author_facet	Aqeel Sakhy, Jaber
author_sort	Aqeel Sakhy, Jaber
title	Hybrid intelligent methods for parameter identification and load frequency control in power system
title_short	Hybrid intelligent methods for parameter identification and load frequency control in power system
title_full	Hybrid intelligent methods for parameter identification and load frequency control in power system
title_fullStr	Hybrid intelligent methods for parameter identification and load frequency control in power system
title_full_unstemmed	Hybrid intelligent methods for parameter identification and load frequency control in power system
title_sort	hybrid intelligent methods for parameter identification and load frequency control in power system
granting_institution	Universiti Malaysia Pahang
granting_department	Faculty of Electrical & Electronics Engineering
publishDate	2014
url	http://umpir.ump.edu.my/id/eprint/11510/19/Hybrid%20intelligent%20methods%20for%20parameter%20identification%20and%20load%20frequency%20control%20in%20power%20system.pdf
_version_	1783731941647843328

Hybrid intelligent methods for parameter identification and load frequency control in power system

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