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Grey Wolf Optimization to Solve Load Frequency Control of an Interconnected Power System: GWO Used to Solve LFC Problem

Grey Wolf Optimization to Solve Load Frequency Control of an Interconnected Power System: GWO Used to Solve LFC Problem

Dipayan Guha, Provas Kumar Roy, Subrata Banerjee
Copyright: © 2016 |Volume: 5 |Issue: 4 |Pages: 22
ISSN: 2160-9500|EISSN: 2160-9543|EISBN13: 9781466693531|DOI: 10.4018/IJEOE.2016100104
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MLA

Guha, Dipayan, et al. "Grey Wolf Optimization to Solve Load Frequency Control of an Interconnected Power System: GWO Used to Solve LFC Problem." IJEOE vol.5, no.4 2016: pp.62-83. http://doi.org/10.4018/IJEOE.2016100104

APA

Guha, D., Roy, P. K., & Banerjee, S. (2016). Grey Wolf Optimization to Solve Load Frequency Control of an Interconnected Power System: GWO Used to Solve LFC Problem. International Journal of Energy Optimization and Engineering (IJEOE), 5(4), 62-83. http://doi.org/10.4018/IJEOE.2016100104

Chicago

Guha, Dipayan, Provas Kumar Roy, and Subrata Banerjee. "Grey Wolf Optimization to Solve Load Frequency Control of an Interconnected Power System: GWO Used to Solve LFC Problem," International Journal of Energy Optimization and Engineering (IJEOE) 5, no.4: 62-83. http://doi.org/10.4018/IJEOE.2016100104

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Abstract

In this article, a novel optimization algorithm called grey wolf optimization (GWO) with the theory of quasi-oppositional based learning (Q-OBL) is proposed for the first time to solve load frequency control (LFC) problem. An equal two-area thermal power system equipped with classical PID-controller is considered for this study. The power system network is modeled with governor dead band and time delay nonlinearities to get better insight of LFC system. 1% load perturbation in area-1 is considered to appraise the dynamic behavior of concerned power system. Integral time absolute error and least average error based fitness functions are defined for fine tuning of PID-controller gains employing the proposed method. An extensive comparative analysis is performed to establish the superiority of proposed algorithm over other recently published algorithms. Finally, sensitivity analysis is performed to show the robustness of the designed controller with system uncertainties.

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