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Solving OPF Problems using Biogeography Based and Grey Wolf Optimization Techniques

Solving OPF Problems using Biogeography Based and Grey Wolf Optimization Techniques

Kingsuk Majumdar, Puja Das, Provas Kumar Roy, Subrata Banerjee
Copyright: © 2017 |Volume: 6 |Issue: 3 |Pages: 23
ISSN: 2160-9500|EISSN: 2160-9543|EISBN13: 9781522515234|DOI: 10.4018/IJEOE.2017070103
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MLA

Majumdar, Kingsuk, et al. "Solving OPF Problems using Biogeography Based and Grey Wolf Optimization Techniques." IJEOE vol.6, no.3 2017: pp.55-77. http://doi.org/10.4018/IJEOE.2017070103

APA

Majumdar, K., Das, P., Roy, P. K., & Banerjee, S. (2017). Solving OPF Problems using Biogeography Based and Grey Wolf Optimization Techniques. International Journal of Energy Optimization and Engineering (IJEOE), 6(3), 55-77. http://doi.org/10.4018/IJEOE.2017070103

Chicago

Majumdar, Kingsuk, et al. "Solving OPF Problems using Biogeography Based and Grey Wolf Optimization Techniques," International Journal of Energy Optimization and Engineering (IJEOE) 6, no.3: 55-77. http://doi.org/10.4018/IJEOE.2017070103

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Abstract

This paper presents biogeography-based optimization (BBO) and grey wolf Optimization(GWO) for solving the multi-constrained optimal power flow (OPF) problems in the power system. In this paper, the proposed algorithms have been tested in 9-bus system under various conditions along with IEEE 30 bus test system. A comparison of simulation results reveals optimization efficacy of the proposed scheme over evolutionary programming (EP), genetic algorithm (GA), mixed-integer particle swarm optimization (MIPSO) for the global optimization of multi-constraint OPF problems. It is observed that GWO is far better in comparison to other listed optimization techniques and can be used for aforesaid problems with high efficiency.

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