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© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

The wind farm layout optimization problem (WFLOP) aims to maximize wind energy utilization efficiency and mitigate energy losses caused by wake effects by optimizing the spatial layout of wind turbines. Although Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) have been widely used in WFLOP due to their discrete optimization characteristics, they still have limitations in global exploration capability and optimization depth. Meanwhile, the Differential Evolution algorithm (DE), known for its strong global optimization ability and excellent performance in handling complex nonlinear problems, is well recognized in continuous optimization issues. However, since DE was originally designed for continuous optimization scenarios, it shows insufficient adaptability under the discrete nature of WFLOP, limiting its potential advantages. In this paper, we propose a Fractional-Order Difference-driven DE Optimization Algorithm called FODE. By introducing the memory and non-local properties of fractional-order differences, FODE effectively overcomes the adaptability issues of advanced DE variants in WFLOP’s discreteness while organically applying their global optimization capabilities for complex nonlinear problems to WFLOP to achieve more efficient overall optimization performance. Experimental results show that under 10 complex wind farm conditions, FODE significantly outperforms various current state-of-the-art WFLOP algorithms including GA, PSO, and DE variants in terms of optimization performance, robustness, and applicability. Incorporating more realistic wind speed distribution and wind condition data into modeling and experiments, further enhancing the realism of WFLOP studies presented here, provides a new technical pathway for optimizing wind farm layouts.

Details

Title
A State-of-the-Art Fractional Order-Driven Differential Evolution for Wind Farm Layout Optimization
Author
Tao, Sichen 1   VIAFID ORCID Logo  ; Liu, Sicheng 1   VIAFID ORCID Logo  ; Zhao, Ruihan 2   VIAFID ORCID Logo  ; Yang, Yifei 3   VIAFID ORCID Logo  ; Todo, Hiroyoshi 4 ; Yang, Haichuan 5   VIAFID ORCID Logo 

 Faculty of Engineering, University of Toyama, Toyama-shi 930-8555, Japan; [email protected] 
 School of Mechanical Engineering, Tongji University, Shanghai 200082, China; [email protected] 
 Faculty of Science and Technology, Hirosaki University, Hirosaki 036-8560, Japan; [email protected] 
 Wicresoft Co., Ltd., Tokyo 163-0445, Japan; [email protected] 
 Graduate School of Technology, Industrial and Social Sciences, Tokushima University, Tokushima 770-8506, Japan 
First page
282
Publication year
2025
Publication date
2025
Publisher
MDPI AG
e-ISSN
22277390
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3159529829
Copyright
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.