Parameter Identification of Load Models Using the CQDPSO Algorithm

Zhen Shu Wang; Shao Run Bian; Da Hai Zhang; Xiao Di Wang; Xiao Yu Liu; Kai Yu

doi:10.4028/www.scientific.net/AMR.986-987.1375

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 986-987Parameter Identification of Load Models Using the...

Parameter Identification of Load Models Using the CQDPSO Algorithm

Abstract:

Load modeling has become a critical problem that is urgent to be solved in power system modeling. In this paper, CQDPSO algorithm, a hybrid optimization algorithm that combines quantum delta-potential-well-based particle swarm optimization (QDPSO) algorithm and chaotic optimization algorithm (COA), is proposed to identify parameters of the selected load model. Numerical results illustrate that the proposed method can improve the accuracy and reduce the computation complexity for load model parameter identifications.

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Energy Research and Power Engineering 2014

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Advanced Materials Research (Volumes 986-987)

Pages:

1375-1378

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.986-987.1375

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Online since:

July 2014

Authors:

Zhen Shu Wang*, Shao Run Bian, Da Hai Zhang, Xiao Di Wang, Xiao Yu Liu, Kai Yu

Keywords:

CQDPSO, Load Modeling, Parameter Identification, PFR, Power System Modeling

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* - Corresponding Author

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