Optimization of Wind Farm Micro-Siting Based on Genetic Algorithm
This paper investigates the optimization problem of wind farm micro-siting based on genetic algorithm. Wind farm siting is a crucial part of the wind farm construction process, and the reasonableness of its siting directly affects the power generation efficiency and economic benefits of the wind farm. However, wind farm siting involves many complex factors, including terrain and geomorphology, wind resource distribution, environmental protection and construction cost, which makes it a complex optimization problem with multiple objectives and constraints. As a kind of bionic optimization algorithm, genetic algorithm has the advantages of strong global search ability and good adaptability, and has been widely used in various kinds of complex optimization problems. This paper firstly analyzes the theoretical foundation of wind farm siting, and then introduces the basic principle of genetic algorithm and its application in wind farm siting. By constructing the fitness function, the wind farm siting problem is transformed into the optimization problem of genetic algorithm. The experimental results show that the genetic algorithm can effectively improve the rationality and economy of wind farm siting. Finally, this paper summarizes the main results of the research and points out the advantages of genetic algorithm in wind farm siting optimization and its future research direction. Through the research of this paper, an effective optimization method is provided for wind farm siting, which has important theoretical significance and practical application value.
Authors
- Xiang Chen
- Hao Zhang (ORCID: https://orcid.org/0000-0002-8320-9066)
- Haotian Chen (ORCID: https://orcid.org/0000-0002-9516-183X)
- Guangwei Ji
- Mengni Guo
- Junhou Zhang
Institutions
- Powerchina Huadong Engineering Corporation (China) (CN)
Publication Details
- Published
- 2024-10-18
- DOI
- https://doi.org/10.1109/icdacai65086.2024.00162
- Primary Topic
- Wind Energy Research and Development
- Type
- article
- Field-Weighted Citation Impact
- 0.00