Assessment of genetic algorithm selection, crossover and mutation techniques in power loss optimization for a hydrocarbon facility

Muhammad Tami Al-Hajri; M. A. Abido; M. K. Darwish

doi:10.1109/UPEC.2015.7339894

Assessment of genetic algorithm selection, crossover and mutation techniques in power loss optimization for a hydrocarbon facility

Muhammad Tami Al-Hajri
, M. A. Abido
, M. K. Darwish

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

In this paper, different selection, crossover including deferential evolution and mutation techniques are considered for optimizing the electrical power loss in real hydrocarbon industrial plant using genetic algorithm (GA). The subject plant electrical system consists of 275 buses, two gas turbine generators, two steam turbine generators, large synchronous motors, and other rotational and static loads. The minimization of power losses objective is used to guide the optimization process. Eight GA selection, crossover and mutation techniques combination cases are simulated for optimizing the system real power loss. The potential of power loss optimization for each case versus the base case will be discussed in the results. The results obtained demonstrate the potential and effectiveness of the proposed techniques combination cases in optimizing the power consumption. Also, in this paper a cost appraisal for the potential daily, monthly and annual cost saving associated with the power loss optimization for each case will be addressed.

Original language	English
Title of host publication	Proceedings - 2015 50th International Universities Power Engineering Conference, UPEC 2015
Publisher	IEEE Computer Society
ISBN (Electronic)	9781467396820
DOIs	https://doi.org/10.1109/UPEC.2015.7339894
State	Published - 30 Nov 2015

Publication series

Name	Proceedings of the Universities Power Engineering Conference
Volume	2015-November

Bibliographical note

Publisher Copyright:
© 2015 IEEE.

Keywords

British thermal unit (BTU)
electrical submersible pump
genetic algorithm
hydrocarbon facility
millions of standard cubical feet of gas (MMscf)
power loss optimization

ASJC Scopus subject areas

Energy Engineering and Power Technology

Access to Document

10.1109/UPEC.2015.7339894

Cite this

Al-Hajri, M. T., Abido, M. A., & Darwish, M. K. (2015). Assessment of genetic algorithm selection, crossover and mutation techniques in power loss optimization for a hydrocarbon facility. In Proceedings - 2015 50th International Universities Power Engineering Conference, UPEC 2015 Article 7339894 (Proceedings of the Universities Power Engineering Conference; Vol. 2015-November). IEEE Computer Society. https://doi.org/10.1109/UPEC.2015.7339894

Al-Hajri, Muhammad Tami ; Abido, M. A. ; Darwish, M. K. / Assessment of genetic algorithm selection, crossover and mutation techniques in power loss optimization for a hydrocarbon facility. Proceedings - 2015 50th International Universities Power Engineering Conference, UPEC 2015. IEEE Computer Society, 2015. (Proceedings of the Universities Power Engineering Conference).

@inproceedings{63d000506561411dae3fbafb14e6a7a8,

title = "Assessment of genetic algorithm selection, crossover and mutation techniques in power loss optimization for a hydrocarbon facility",

abstract = "In this paper, different selection, crossover including deferential evolution and mutation techniques are considered for optimizing the electrical power loss in real hydrocarbon industrial plant using genetic algorithm (GA). The subject plant electrical system consists of 275 buses, two gas turbine generators, two steam turbine generators, large synchronous motors, and other rotational and static loads. The minimization of power losses objective is used to guide the optimization process. Eight GA selection, crossover and mutation techniques combination cases are simulated for optimizing the system real power loss. The potential of power loss optimization for each case versus the base case will be discussed in the results. The results obtained demonstrate the potential and effectiveness of the proposed techniques combination cases in optimizing the power consumption. Also, in this paper a cost appraisal for the potential daily, monthly and annual cost saving associated with the power loss optimization for each case will be addressed.",

keywords = "British thermal unit (BTU), electrical submersible pump, genetic algorithm, hydrocarbon facility, millions of standard cubical feet of gas (MMscf), power loss optimization",

author = "Al-Hajri, \{Muhammad Tami\} and Abido, \{M. A.\} and Darwish, \{M. K.\}",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.",

year = "2015",

month = nov,

day = "30",

doi = "10.1109/UPEC.2015.7339894",

language = "English",

series = "Proceedings of the Universities Power Engineering Conference",

publisher = "IEEE Computer Society",

booktitle = "Proceedings - 2015 50th International Universities Power Engineering Conference, UPEC 2015",

address = "United States",

}

Al-Hajri, MT, Abido, MA & Darwish, MK 2015, Assessment of genetic algorithm selection, crossover and mutation techniques in power loss optimization for a hydrocarbon facility. in Proceedings - 2015 50th International Universities Power Engineering Conference, UPEC 2015., 7339894, Proceedings of the Universities Power Engineering Conference, vol. 2015-November, IEEE Computer Society. https://doi.org/10.1109/UPEC.2015.7339894

Assessment of genetic algorithm selection, crossover and mutation techniques in power loss optimization for a hydrocarbon facility. / Al-Hajri, Muhammad Tami; Abido, M. A.; Darwish, M. K.
Proceedings - 2015 50th International Universities Power Engineering Conference, UPEC 2015. IEEE Computer Society, 2015. 7339894 (Proceedings of the Universities Power Engineering Conference; Vol. 2015-November).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Assessment of genetic algorithm selection, crossover and mutation techniques in power loss optimization for a hydrocarbon facility

AU - Al-Hajri, Muhammad Tami

AU - Abido, M. A.

AU - Darwish, M. K.

PY - 2015/11/30

Y1 - 2015/11/30

N2 - In this paper, different selection, crossover including deferential evolution and mutation techniques are considered for optimizing the electrical power loss in real hydrocarbon industrial plant using genetic algorithm (GA). The subject plant electrical system consists of 275 buses, two gas turbine generators, two steam turbine generators, large synchronous motors, and other rotational and static loads. The minimization of power losses objective is used to guide the optimization process. Eight GA selection, crossover and mutation techniques combination cases are simulated for optimizing the system real power loss. The potential of power loss optimization for each case versus the base case will be discussed in the results. The results obtained demonstrate the potential and effectiveness of the proposed techniques combination cases in optimizing the power consumption. Also, in this paper a cost appraisal for the potential daily, monthly and annual cost saving associated with the power loss optimization for each case will be addressed.

AB - In this paper, different selection, crossover including deferential evolution and mutation techniques are considered for optimizing the electrical power loss in real hydrocarbon industrial plant using genetic algorithm (GA). The subject plant electrical system consists of 275 buses, two gas turbine generators, two steam turbine generators, large synchronous motors, and other rotational and static loads. The minimization of power losses objective is used to guide the optimization process. Eight GA selection, crossover and mutation techniques combination cases are simulated for optimizing the system real power loss. The potential of power loss optimization for each case versus the base case will be discussed in the results. The results obtained demonstrate the potential and effectiveness of the proposed techniques combination cases in optimizing the power consumption. Also, in this paper a cost appraisal for the potential daily, monthly and annual cost saving associated with the power loss optimization for each case will be addressed.

KW - British thermal unit (BTU)

KW - electrical submersible pump

KW - genetic algorithm

KW - hydrocarbon facility

KW - millions of standard cubical feet of gas (MMscf)

KW - power loss optimization

UR - https://www.scopus.com/pages/publications/84958093271

U2 - 10.1109/UPEC.2015.7339894

DO - 10.1109/UPEC.2015.7339894

M3 - Conference contribution

AN - SCOPUS:84958093271

T3 - Proceedings of the Universities Power Engineering Conference

BT - Proceedings - 2015 50th International Universities Power Engineering Conference, UPEC 2015

PB - IEEE Computer Society

ER -

Al-Hajri MT, Abido MA, Darwish MK. Assessment of genetic algorithm selection, crossover and mutation techniques in power loss optimization for a hydrocarbon facility. In Proceedings - 2015 50th International Universities Power Engineering Conference, UPEC 2015. IEEE Computer Society. 2015. 7339894. (Proceedings of the Universities Power Engineering Conference). doi: 10.1109/UPEC.2015.7339894

Assessment of genetic algorithm selection, crossover and mutation techniques in power loss optimization for a hydrocarbon facility

Abstract

Publication series

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this