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Multi-Objective Structural Optimization Design of Horizontal-Axis Wind Turbine Blades Using the Non-Dominated Sorting Genetic Algorithm II and Finite Element Method
Energies 2014, 7(3), 1706-1720; doi:10.3390/en7031706
Article

Design and Study on Sliding Mode Extremum Seeking Control of the Chaos Embedded Particle Swarm Optimization for Maximum Power Point Tracking in Wind Power Systems

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Received: 26 January 2014 / Revised: 23 February 2014 / Accepted: 4 March 2014 / Published: 21 March 2014
(This article belongs to the Special Issue Wind Turbines 2014)

Abstract

This paper proposes a sliding mode extremum seeking control (SMESC) of chaos embedded particle swarm optimization (CEPSO) Algorithm, applied to the design of maximum power point tracking in wind power systems. Its features are that the control parameters in SMESC are optimized by CEPSO, making it unnecessary to change the output power of different wind turbines, the designed in-repetition rate is reduced, and the system control efficiency is increased. The wind power system control is designed by simulation, in comparison with the traditional wind power control method, and the simulated dynamic response obtained by the SMESC algorithm proposed in this paper is better than the traditional hill-climbing search (HCS) and extremum seeking control (ESC) algorithms in the transient or steady states, validating the advantages and practicability of the method proposed in this paper.
Keywords: extremum seeking control (ESC); sliding mode extremum seeking control (SMESC); maximum power point tracking (MPPT); particle swarm optimization (PSO); chaos; wind power extremum seeking control (ESC); sliding mode extremum seeking control (SMESC); maximum power point tracking (MPPT); particle swarm optimization (PSO); chaos; wind power
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Chen, J.-H.; Yau, H.-T.; Hung, W. Design and Study on Sliding Mode Extremum Seeking Control of the Chaos Embedded Particle Swarm Optimization for Maximum Power Point Tracking in Wind Power Systems. Energies 2014, 7, 1706-1720.

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