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
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Online Access:http://eprints.utm.my/id/eprint/33264/5/FolorunsoTalihaAbiodunMFKE2013.pdf
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Institution: Universiti Teknologi Malaysia
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spelling my.utm.332642017-07-24T07:49:54Z http://eprints.utm.my/id/eprint/33264/ Pump scheduling optimization for water supply system using adaptive weighted sum genetic algorithm 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 NonPeerReviewed application/pdf en http://eprints.utm.my/id/eprint/33264/5/FolorunsoTalihaAbiodunMFKE2013.pdf Abiodun, Folorunso Taliha (2013) Pump scheduling optimization for water supply system using adaptive weighted sum genetic algorithm. Masters thesis, Universiti Teknologi Malaysia, Faculty of Electrical Engineering. http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:70028?site_name=Restricted Repository
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
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
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
publishDate 2013
url http://eprints.utm.my/id/eprint/33264/5/FolorunsoTalihaAbiodunMFKE2013.pdf
http://eprints.utm.my/id/eprint/33264/
http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:70028?site_name=Restricted Repository
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