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Comparative Study of Evolutionary Computing Methods for Parameter Estimation of Power Quality Signals

Publication Type : Book Chapter

Publisher : International Journal of Applied Evolutionary Computation (IJAEC)

Source : International Journal of Applied Evolutionary Computation (IJAEC), Volume 1 (2010)

Campus : Amritapuri

School : School of Engineering

Department : Electrical and Electronics

Year : 2010

Abstract : pRecently utilities and end users become more concerned about power quality issues because the load equipments are more sensitive to various power quality disturbances, such as harmonics and voltage fluctuation. Harmonic distortion and voltage flicker are the major causes in growing concern about electric power quality. Power quality disturbance monitoring plays an important role in the deregulated power market scenario due to competitiveness among the utilities. This paper presents an evolutionary algorithm approach based on Adaptive Particle Swarm Optimization (APSO) to determine the amplitude, phase and frequency of a power quality signal. In this APSO algorithm the time varying inertia weight is modified as rank based, and re-initialization is used to increase the diversity. In this paper, to the authors highlight the efficacy of different evolutionary optimization techniques like classical PSO, Constriction based PSO, Clonal Algorithm (CLONALOG), Adaptive Bacterial Foraging (ABF) and the proposed Adaptive Particle Swarm Optimization (APSO) to extract different parameters like amplitude, phase and frequency of harmonic distorted power quality signal and voltage flicker./p

Cite this Research Publication : Dr. V. Ravikumar Pandi and Panigrahi, B. K., “Comparative Study of Evolutionary Computing Methods for Parameter Estimation of Power Quality Signals”, in International Journal of Applied Evolutionary Computation (IJAEC), vol. 1, 2010.

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