期刊名：Wind

期刊主页: https://www.mdpi.com/journal/wind

1. A Survey of Numerical Simulation Tools for Offshore Wind Turbine Systems

海上风力涡轮机系统数值模拟工具综述

https://www.mdpi.com/2674-032X/4/1/1

Fadaei, S.; Afagh, F.F.; Langlois, R.G. A Survey of Numerical Simulation Tools for Offshore Wind Turbine Systems. Wind 2024, 4, 1-24.

2. A Novel Approach to Wavelet Neural Network-Based Wind Power Forecasting

基于小波神经网络的风电预测新方法

https://www.mdpi.com/2674-032X/5/2/14

Dias, F.L.; Naik, A.J. A Novel Approach to Wavelet Neural Network-Based Wind Power Forecasting. Wind 2025, 5, 14.

3. Maximizing Wind Turbine Power Generation Through Adaptive Fuzzy Logic Control for Optimal Efficiency and Performance

通过自适应模糊逻辑控制最大化风力涡轮机发电量，以实现最佳效率和性能

https://www.mdpi.com/2674-032X/5/1/4

Aranizadeh, A.; Mirmozaffari, M.; Khalatabadi Farahani, B. Maximizing Wind Turbine Power Generation Through Adaptive Fuzzy Logic Control for Optimal Efficiency and Performance. Wind 2025, 5, 4.

4. Comparative Performance Evaluation of Wind Energy Systems Using Doubly Fed Induction Generator and Permanent Magnet Synchronous Generator

双馈感应发电机与永磁同步发电机风能系统性能对比评估

https://www.mdpi.com/2674-032X/5/4/31

Elngar, A.E.; Sabik, A.S.; Adel, A.H.; Nada, A.S. Comparative Performance Evaluation of Wind Energy Systems Using Doubly Fed Induction Generator and Permanent Magnet Synchronous Generator. Wind 2025, 5, 31.

5. A Systematic Review of Wind Energy Forecasting Models Based on Deep Neural Networks

基于深度神经网络的风能预测模型的系统综述

https://www.mdpi.com/2674-032X/5/4/29

Manzano, E.A.; Nogales, R.E.; Rios, A. A Systematic Review of Wind Energy Forecasting Models Based on Deep Neural Networks. Wind 2025, 5, 29.

6. Floater Assembly and Turbine Integration Strategy for Floating Offshore Wind Energy: Considerations and Recommendations

浮式海上风能发电系统的浮体组件与涡轮机集成策略：相关考虑与建议

https://www.mdpi.com/2674-032X/4/4/19

Ivanov, G.; Ma, K.-T. Floater Assembly and Turbine Integration Strategy for Floating Offshore Wind Energy: Considerations and Recommendations. Wind 2024, 4, 376-394.

7. Review of Artificial Intelligence-Based Design Optimization of Wind Power Systems

基于人工智能的风力发电系统设计优化综述

https://www.mdpi.com/2674-032X/5/3/18

Jiang, Z.; Li, H.; Yang, H.; Wu, H.; Liu, W.; Chen, Z. Review of Artificial Intelligence-Based Design Optimization of Wind Power Systems. Wind 2025, 5, 18.

8. Insights on the Optimization of Short- and Long-Term Maintenance Decisions for Floating Offshore Wind Using Nested Genetic Algorithms

基于嵌套遗传算法的浮动海上风电短期与长期维护决策优化研究

https://www.mdpi.com/2674-032X/4/3/12

Vieira, M.; Djurdjanovic, D. Insights on the Optimization of Short- and Long-Term Maintenance Decisions for Floating Offshore Wind Using Nested Genetic Algorithms. Wind 2024, 4, 227-250.

9. Current Status and Sustainable Utilization of Wind Energy Resources in Mexico: A Systematic Review

墨西哥风能资源的现状及可持续利用情况：系统综述

https://www.mdpi.com/2674-032X/5/4/22

Batun, U.C.; Zayed, M.E.; Ghazy, M.; Rehman, S. Current Status and Sustainable Utilization of Wind Energy Resources in Mexico: A Systematic Review. Wind 2025, 5, 22.

10. System-Level Offshore Wind Energy and Hydrogen Generation Availability and Operations and Maintenance Costs

系统级海上风电与氢能发电的可用性及运行和维护成本

https://www.mdpi.com/2674-032X/4/2/7

Lochhead, R.; Donnelly, O.; Carroll, J. System-Level Offshore Wind Energy and Hydrogen Generation Availability and Operations and Maintenance Costs. Wind 2024, 4, 135-154.

Wind 期刊介绍

主编

Prof. Dr. Horia Hangan

安大略理工大學

研究领域：流体力学；湍流；风工程；大气流动；空气动力学；流量控制

Wind (ISSN 2674-032X) 是开放获取期刊，专注于风相关领域的科学研究。期刊目标是为展示与风相关的跨学科领域的最新研究成果提供一个动态的平台，涵盖概念开发、设计工具、工程技术、能源市场、经济学、政策、社会和环境影响以及生态等方面。近日，2025年CiteScore与IF正式发布，期刊最新CiteScore为4.5，IF为2.7，两项核心指标较上一年度均实现稳步提升，反映出期刊在风相关领域的学术关注度与影响力正持续增强。