Pump scheduling optimization for water supply system using adaptive weighted sum genetic algorithm

Water supply system has an inherently high operational cost. This is significantly due to the high amount of electric energy expended by the pumps of the system and the cost of their maintenance in cause of delivering water for the daily use by the consumers. Scheduling the operations of the pumps i...

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Main Author:	Abiodun, Folorunso Taliha
Format:	Thesis
Language:	English
Published:	2013
Subjects:	T Technology (General)
Online Access:	http://eprints.utm.my/id/eprint/33264/5/FolorunsoTalihaAbiodunMFKE2013.pdf
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spelling	my-utm-ep.332642017-07-24T07:49:54Z Pump scheduling optimization for water supply system using adaptive weighted sum genetic algorithm 2013-01 Abiodun, Folorunso Taliha T Technology (General) Water supply system has an inherently high operational cost. This is significantly due to the high amount of electric energy expended by the pumps of the system and the cost of their maintenance in cause of delivering water for the daily use by the consumers. Scheduling the operations of the pumps in the system ensures that the cost of energy consumed is minimized and also prevents the increased wear and tear in the pumps. Thus, creating an optimal schedule for the pumps is of paramount importance in order to save more electric cost which in turn leads to a reduced operational cost for the system. This work adopts the use of an Adaptive Weighted-sum Genetic Algorithm (AWGA), based on popular weighted sum approach Genetic Algorithm (GA) for multi-objective optimization problem. The AWGA weights multipliers of the individual cost functions are adaptively formed using the information of the fitness function on every generation of the GA process. This study adopts a water supply system consisting of 5 fixed speed pumps and a reservoir with the objective of minimizing the electric energy cost as well as the maintenance cost associated with the operating pumps subject to satisfaction of the maximum and minimum levels in the system reservoir. With the application of the AWGA a schedule that satisfies the demand requirement as well as the system requirement was obtained. Thereafter as a means for the validation and comparison of the results obtained, two other well known weighted sum GA approaches namely the Fixed Weighted-sum GA (FWGA) and Random Weighted-sum GA (RWGA) approaches were also simulated.. The results show that AWGA produces a schedule with a 16.2% reduction in terms of the fitness index parameter as compared 7.23% and 7.74% of the FWGA and RWGA respectively. 2013-01 Thesis http://eprints.utm.my/id/eprint/33264/ http://eprints.utm.my/id/eprint/33264/5/FolorunsoTalihaAbiodunMFKE2013.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:70028?site_name=Restricted Repository masters Universiti Teknologi Malaysia, Faculty of Electrical Engineering Faculty of Electrical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	T Technology (General)
spellingShingle	T Technology (General) Abiodun, Folorunso Taliha Pump scheduling optimization for water supply system using adaptive weighted sum genetic algorithm
description	Water supply system has an inherently high operational cost. This is significantly due to the high amount of electric energy expended by the pumps of the system and the cost of their maintenance in cause of delivering water for the daily use by the consumers. Scheduling the operations of the pumps in the system ensures that the cost of energy consumed is minimized and also prevents the increased wear and tear in the pumps. Thus, creating an optimal schedule for the pumps is of paramount importance in order to save more electric cost which in turn leads to a reduced operational cost for the system. This work adopts the use of an Adaptive Weighted-sum Genetic Algorithm (AWGA), based on popular weighted sum approach Genetic Algorithm (GA) for multi-objective optimization problem. The AWGA weights multipliers of the individual cost functions are adaptively formed using the information of the fitness function on every generation of the GA process. This study adopts a water supply system consisting of 5 fixed speed pumps and a reservoir with the objective of minimizing the electric energy cost as well as the maintenance cost associated with the operating pumps subject to satisfaction of the maximum and minimum levels in the system reservoir. With the application of the AWGA a schedule that satisfies the demand requirement as well as the system requirement was obtained. Thereafter as a means for the validation and comparison of the results obtained, two other well known weighted sum GA approaches namely the Fixed Weighted-sum GA (FWGA) and Random Weighted-sum GA (RWGA) approaches were also simulated.. The results show that AWGA produces a schedule with a 16.2% reduction in terms of the fitness index parameter as compared 7.23% and 7.74% of the FWGA and RWGA respectively.
format	Thesis
qualification_level	Master's degree
author	Abiodun, Folorunso Taliha
author_facet	Abiodun, Folorunso Taliha
author_sort	Abiodun, Folorunso Taliha
title	Pump scheduling optimization for water supply system using adaptive weighted sum genetic algorithm
title_short	Pump scheduling optimization for water supply system using adaptive weighted sum genetic algorithm
title_full	Pump scheduling optimization for water supply system using adaptive weighted sum genetic algorithm
title_fullStr	Pump scheduling optimization for water supply system using adaptive weighted sum genetic algorithm
title_full_unstemmed	Pump scheduling optimization for water supply system using adaptive weighted sum genetic algorithm
title_sort	pump scheduling optimization for water supply system using adaptive weighted sum genetic algorithm
granting_institution	Universiti Teknologi Malaysia, Faculty of Electrical Engineering
granting_department	Faculty of Electrical Engineering
publishDate	2013
url	http://eprints.utm.my/id/eprint/33264/5/FolorunsoTalihaAbiodunMFKE2013.pdf
_version_	1747816119280861184

Pump scheduling optimization for water supply system using adaptive weighted sum genetic algorithm

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