Comparative study of bat & flower pollination optimization algorithms in highly stressed large power system

K. S. Pandya; D. A. Dabhi; S. K. Joshi

doi:10.1109/PSC.2015.7101677

Comparative study of bat & flower pollination optimization algorithms in highly stressed large power system

K. S. Pandya
, D. A. Dabhi
, S. K. Joshi

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

Abstract

Optimal power flow is an important non-linear optimization task in power systems. In this process, the total power demand is distributed amongst the generating units such that each unit satisfies its generation limit constraints and the cost of power production is minimized. This paper presents a comparative study of new meta-heuristic optimization techniques namely bat and flower pollination algorithm for the optimal solution of optimal power flow problem such as minimizing the fuel cost of a thermal power plant. In this paper PSO is also taken just as a reference for measure the performance of the above two techniques. The numerical results clearly show that the bat algorithm gives better results than flower pollination algorithm in terms of fuel cost value and time required to reach global best solution. In order to illustrate the effectiveness of the proposed algorithm, it has been tested on highly stressed modified IEEE 300-bus test system.

Original language	English
Title of host publication	2015 Clemson University Power Systems Conference, PSC 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781479919512
DOIs	https://doi.org/10.1109/PSC.2015.7101677
Publication status	Published - 4 May 2015
Externally published	Yes
Event	2015 Clemson University Power Systems Conference, PSC 2015 - Clemson, United States Duration: 10 Mar 2015 → 13 Mar 2015

Publication series

Name	2015 Clemson University Power Systems Conference, PSC 2015

Conference

Conference	2015 Clemson University Power Systems Conference, PSC 2015
Country/Territory	United States
City	Clemson
Period	10/03/15 → 13/03/15

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Bat Algorithm (BA)
Flower Pollination Algorithm (FPA)
Optimal Power Flow (OPF)
Particle Swarm Optimization (PSO)

Access to Document

10.1109/PSC.2015.7101677

Cite this

Pandya, K. S., Dabhi, D. A., & Joshi, S. K. (2015). Comparative study of bat & flower pollination optimization algorithms in highly stressed large power system. In 2015 Clemson University Power Systems Conference, PSC 2015 Article 7101677 (2015 Clemson University Power Systems Conference, PSC 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/PSC.2015.7101677

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title = "Comparative study of bat \& flower pollination optimization algorithms in highly stressed large power system",

abstract = "Optimal power flow is an important non-linear optimization task in power systems. In this process, the total power demand is distributed amongst the generating units such that each unit satisfies its generation limit constraints and the cost of power production is minimized. This paper presents a comparative study of new meta-heuristic optimization techniques namely bat and flower pollination algorithm for the optimal solution of optimal power flow problem such as minimizing the fuel cost of a thermal power plant. In this paper PSO is also taken just as a reference for measure the performance of the above two techniques. The numerical results clearly show that the bat algorithm gives better results than flower pollination algorithm in terms of fuel cost value and time required to reach global best solution. In order to illustrate the effectiveness of the proposed algorithm, it has been tested on highly stressed modified IEEE 300-bus test system.",

keywords = "Bat Algorithm (BA), Flower Pollination Algorithm (FPA), Optimal Power Flow (OPF), Particle Swarm Optimization (PSO)",

author = "Pandya, \{K. S.\} and Dabhi, \{D. A.\} and Joshi, \{S. K.\}",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 2015 Clemson University Power Systems Conference, PSC 2015 ; Conference date: 10-03-2015 Through 13-03-2015",

year = "2015",

month = may,

day = "4",

doi = "10.1109/PSC.2015.7101677",

language = "English",

series = "2015 Clemson University Power Systems Conference, PSC 2015",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2015 Clemson University Power Systems Conference, PSC 2015",

address = "United States",

}

Pandya, KS, Dabhi, DA & Joshi, SK 2015, Comparative study of bat & flower pollination optimization algorithms in highly stressed large power system. in 2015 Clemson University Power Systems Conference, PSC 2015., 7101677, 2015 Clemson University Power Systems Conference, PSC 2015, Institute of Electrical and Electronics Engineers Inc., 2015 Clemson University Power Systems Conference, PSC 2015, Clemson, United States, 10/03/15. https://doi.org/10.1109/PSC.2015.7101677

Comparative study of bat & flower pollination optimization algorithms in highly stressed large power system. / Pandya, K. S.; Dabhi, D. A.; Joshi, S. K.
2015 Clemson University Power Systems Conference, PSC 2015. Institute of Electrical and Electronics Engineers Inc., 2015. 7101677 (2015 Clemson University Power Systems Conference, PSC 2015).

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

TY - GEN

T1 - Comparative study of bat & flower pollination optimization algorithms in highly stressed large power system

AU - Pandya, K. S.

AU - Dabhi, D. A.

AU - Joshi, S. K.

PY - 2015/5/4

Y1 - 2015/5/4

N2 - Optimal power flow is an important non-linear optimization task in power systems. In this process, the total power demand is distributed amongst the generating units such that each unit satisfies its generation limit constraints and the cost of power production is minimized. This paper presents a comparative study of new meta-heuristic optimization techniques namely bat and flower pollination algorithm for the optimal solution of optimal power flow problem such as minimizing the fuel cost of a thermal power plant. In this paper PSO is also taken just as a reference for measure the performance of the above two techniques. The numerical results clearly show that the bat algorithm gives better results than flower pollination algorithm in terms of fuel cost value and time required to reach global best solution. In order to illustrate the effectiveness of the proposed algorithm, it has been tested on highly stressed modified IEEE 300-bus test system.

AB - Optimal power flow is an important non-linear optimization task in power systems. In this process, the total power demand is distributed amongst the generating units such that each unit satisfies its generation limit constraints and the cost of power production is minimized. This paper presents a comparative study of new meta-heuristic optimization techniques namely bat and flower pollination algorithm for the optimal solution of optimal power flow problem such as minimizing the fuel cost of a thermal power plant. In this paper PSO is also taken just as a reference for measure the performance of the above two techniques. The numerical results clearly show that the bat algorithm gives better results than flower pollination algorithm in terms of fuel cost value and time required to reach global best solution. In order to illustrate the effectiveness of the proposed algorithm, it has been tested on highly stressed modified IEEE 300-bus test system.

KW - Bat Algorithm (BA)

KW - Flower Pollination Algorithm (FPA)

KW - Optimal Power Flow (OPF)

KW - Particle Swarm Optimization (PSO)

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DO - 10.1109/PSC.2015.7101677

M3 - Conference contribution

AN - SCOPUS:84933565409

T3 - 2015 Clemson University Power Systems Conference, PSC 2015

BT - 2015 Clemson University Power Systems Conference, PSC 2015

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2015 Clemson University Power Systems Conference, PSC 2015

Y2 - 10 March 2015 through 13 March 2015

ER -

Comparative study of bat & flower pollination optimization algorithms in highly stressed large power system

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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Cite this