Optimization Design of Wind Turbine Blade Based on an Improved Particle Swarm Optimization Algorithm Combined with Non-Gaussian Distribution
To overcome the problem of particle swarm optimization (PSO) being trapped in local minima, a particle swarm optimization algorithm combined with non-Gaussian stochastic distribution is presented for optimization design of wind turbine blade. Before updating the particle velocity, a limited test was...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2021/6699797 |
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| author | Fangjin Sun Zhonghao Xu Daming Zhang |
| author_facet | Fangjin Sun Zhonghao Xu Daming Zhang |
| author_sort | Fangjin Sun |
| collection | DOAJ |
| description | To overcome the problem of particle swarm optimization (PSO) being trapped in local minima, a particle swarm optimization algorithm combined with non-Gaussian stochastic distribution is presented for optimization design of wind turbine blade. Before updating the particle velocity, a limited test was performed for every particle to search for the global best solution. Taking the maximum wind turbine annual power generation as the final objective, a 1.3 MW wind turbine blade was optimized. The results were compared with those of the original wind turbine blades and traditional particle swarm optimization. Compared with the original output power, the year output power increased by 5.3% with non-Gaussian stochastic distribution combined with PSO, whereas the computation time was 65% of the traditional PSO. As time step increased, residuals of non-Gaussian stochastic distribution combined with PSO greatly diminished, with improved computation efficiency. It is shown that the non-Gaussian distribution combined with PSO ensures the global best solution. The non-Gaussian distribution combined with PSO provides a more reliable theoretical basis for the design of wind turbine blades. |
| format | Article |
| id | doaj-art-f9c43618dbf6433cb332d94daf2e61b0 |
| institution | Kabale University |
| issn | 1687-8086 1687-8094 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-f9c43618dbf6433cb332d94daf2e61b02025-02-03T01:25:00ZengWileyAdvances in Civil Engineering1687-80861687-80942021-01-01202110.1155/2021/66997976699797Optimization Design of Wind Turbine Blade Based on an Improved Particle Swarm Optimization Algorithm Combined with Non-Gaussian DistributionFangjin Sun0Zhonghao Xu1Daming Zhang2Guangxi Key Laboratory of Geomechanics and Geotechnical Engineering, Guilin University of Technology, Guilin 541004, ChinaCollege of Civil Engineering, Liaoning Technical University, Fuxin 123000, ChinaGuangxi Key Laboratory of Embedded Technology and Intelligence, Guilin 541006, ChinaTo overcome the problem of particle swarm optimization (PSO) being trapped in local minima, a particle swarm optimization algorithm combined with non-Gaussian stochastic distribution is presented for optimization design of wind turbine blade. Before updating the particle velocity, a limited test was performed for every particle to search for the global best solution. Taking the maximum wind turbine annual power generation as the final objective, a 1.3 MW wind turbine blade was optimized. The results were compared with those of the original wind turbine blades and traditional particle swarm optimization. Compared with the original output power, the year output power increased by 5.3% with non-Gaussian stochastic distribution combined with PSO, whereas the computation time was 65% of the traditional PSO. As time step increased, residuals of non-Gaussian stochastic distribution combined with PSO greatly diminished, with improved computation efficiency. It is shown that the non-Gaussian distribution combined with PSO ensures the global best solution. The non-Gaussian distribution combined with PSO provides a more reliable theoretical basis for the design of wind turbine blades.http://dx.doi.org/10.1155/2021/6699797 |
| spellingShingle | Fangjin Sun Zhonghao Xu Daming Zhang Optimization Design of Wind Turbine Blade Based on an Improved Particle Swarm Optimization Algorithm Combined with Non-Gaussian Distribution Advances in Civil Engineering |
| title | Optimization Design of Wind Turbine Blade Based on an Improved Particle Swarm Optimization Algorithm Combined with Non-Gaussian Distribution |
| title_full | Optimization Design of Wind Turbine Blade Based on an Improved Particle Swarm Optimization Algorithm Combined with Non-Gaussian Distribution |
| title_fullStr | Optimization Design of Wind Turbine Blade Based on an Improved Particle Swarm Optimization Algorithm Combined with Non-Gaussian Distribution |
| title_full_unstemmed | Optimization Design of Wind Turbine Blade Based on an Improved Particle Swarm Optimization Algorithm Combined with Non-Gaussian Distribution |
| title_short | Optimization Design of Wind Turbine Blade Based on an Improved Particle Swarm Optimization Algorithm Combined with Non-Gaussian Distribution |
| title_sort | optimization design of wind turbine blade based on an improved particle swarm optimization algorithm combined with non gaussian distribution |
| url | http://dx.doi.org/10.1155/2021/6699797 |
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