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
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 un...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2014-03-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1996-1073/7/3/1706 |
| _version_ | 1811300390548799488 |
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| author | Jui-Ho Chen Her-Terng Yau Weir Hung |
| author_facet | Jui-Ho Chen Her-Terng Yau Weir Hung |
| author_sort | Jui-Ho Chen |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T06:51:32Z |
| format | Article |
| id | doaj.art-9458ef50cea24e2b800310b9800d7d20 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-13T06:51:32Z |
| publishDate | 2014-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-9458ef50cea24e2b800310b9800d7d202022-12-22T02:57:24ZengMDPI AGEnergies1996-10732014-03-01731706172010.3390/en7031706en7031706Design and Study on Sliding Mode Extremum Seeking Control of the Chaos Embedded Particle Swarm Optimization for Maximum Power Point Tracking in Wind Power SystemsJui-Ho Chen0Her-Terng Yau1Weir Hung2Department of Electrical Engineering, National Chin-Yi University of Technology, Taichung 41170, TaiwanDepartment of Electrical Engineering, National Chin-Yi University of Technology, Taichung 41170, TaiwanDepartment of Electrical Engineering, National Chin-Yi University of Technology, Taichung 41170, TaiwanThis 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.http://www.mdpi.com/1996-1073/7/3/1706extremum seeking control (ESC)sliding mode extremum seeking control (SMESC)maximum power point tracking (MPPT)particle swarm optimization (PSO)chaoswind power |
| spellingShingle | Jui-Ho Chen Her-Terng Yau Weir Hung 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 extremum seeking control (ESC) sliding mode extremum seeking control (SMESC) maximum power point tracking (MPPT) particle swarm optimization (PSO) chaos wind power |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | extremum seeking control (ESC) sliding mode extremum seeking control (SMESC) maximum power point tracking (MPPT) particle swarm optimization (PSO) chaos wind power |
| url | http://www.mdpi.com/1996-1073/7/3/1706 |
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