Multi-objective mathematical model for optimal wind turbine placement in wind farm under uncertainty

doi:10.1016/j.jer.2024.09.014

淡江大學機構典藏 > 工學院 > 人工智慧學系 > 期刊論文 > Item 987654321/126616

請使用永久網址來引用或連結此文件: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/126616

題名:	Multi-objective mathematical model for optimal wind turbine placement in wind farm under uncertainty
作者:	Li, Guanting;Chen, Tzu-Chia
關鍵詞:	Wind turbine;Monte Carlo Simulation;Multi-objective optimization;Wake effect;Meta-heuristic aglrotithms
日期:	2024-09-28
上傳時間:	2024-12-27 12:05:21 (UTC+8)
摘要:	The main objective of this research is to introduce three energy risk management models grounded in optimization techniques for the strategic placement of wind turbines, considering wake effects and uncertainties in wind speed and direction. For this purpose, wind speed and direction data are gathered, and Monte Carlo simulation is employed to model the uncertainties. Subsequently, the risk management models undergo optimization using Non-Dominated Sorting Genetic Algorithm II (NSGA-II), Pareto envelope-based selection algorithm II (PESA-II), and Multi-Objective Particle Swarm Optimization (MOPSO) algorithms. Findings reveal that the wind farm's maximum power output reaches approximately 5.8 megawatts across all three algorithms and optimal turbine placements. A risk assessment was conducted using a tenth percentile criterion, revealing a significant production risk within the study area, with production falling below 1.8 megawatts in 90 % of cases. Regarding the performance evaluation of the algorithms across all three models, superior performance in terms of solution proximity to the ideal solution is exhibited by PESA-II, while enhanced diversity and solution spread compared to the other algorithms are demonstrated by NSGA-II.
關聯:	Journal of Engineering Research, Available online 28 September 2024
DOI:	10.1016/j.jer.2024.09.014
顯示於類別:	[人工智慧學系] 期刊論文

文件中的檔案:

檔案	描述	大小	格式	瀏覽次數
index.html		0Kb	HTML	23	檢視/開啟

在機構典藏中所有的資料項目都受到原著作權保護.

TAIR相關文章

資料載入中.....