Wind Farm Layout Optimization Based on Dynamic Opposite Learning-Enhanced Sparrow Search Algorithm

In recent years, the proportion of wind power in new energy generation has gradually increased. The natural wind in wind farms is subject to velocity attenuation by the wake effect, so improving the efficiency of wind farm power generation has become a problem that must be solved for wind power gene...

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Vydáno v:International journal of energy research Ročník 2024; číslo 1
Hlavní autoři: Zhu, Yun, Guo, Yahui, Hu, Tianyu, Wu, Chengke, Zhang, Lidong
Médium: Journal Article
Jazyk:angličtina
Vydáno: Bognor Regis Hindawi 2024
John Wiley & Sons, Inc
Témata:
Accuracy
Algorithms
Alternative energy sources
Clean technology
Electric power generation
Electricity
Layouts
Local optimization
Machine learning
Optimization algorithms
Renewable resources
Search algorithms
Turbines
Wind direction
Wind farms
Wind power
Wind power generation
Wind speed
ISSN:0363-907X, 1099-114X
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Shrnutí:In recent years, the proportion of wind power in new energy generation has gradually increased. The natural wind in wind farms is subject to velocity attenuation by the wake effect, so improving the efficiency of wind farm power generation has become a problem that must be solved for wind power generation. Considering the uncertainty of wind farms, we regard wind farm layout optimization (WFLO) as a strongly nonlinear problem. In this paper, we improve the sparrow search algorithm (SSA) using dynamic opposite learning (DOL) strategy. Twenty-eight benchmark test results prove that compared with other algorithms, the improved algorithm DOLSSA has excellent robustness and the ability of searching for a better solution when solving a strongly nonlinear optimization problem, and the DOL strategy effectively improves the shortcomings of the original algorithm which is prone to local optimization and space limitation. In this paper, the authors establish the dynamic rotational coordinates of wind farms and set six different physical scenarios by considering the wind direction and wind speed variables, and the results prove that the performance of DOLSSA is optimal.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0363-907X
1099-114X
DOI:10.1155/2024/4322211