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Advancements in Wind Farm Design and Optimization
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Advancements in Wind Farm Design and Optimization

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A3: Wind, Wave and Tidal Energy".

Deadline for manuscript submissions: 15 September 2025 | Viewed by 441

Special Issue Editors

Prof. Dr. Angel G. Gonzalez-Rodriguez

E-Mail Website
Guest Editor
Department of Electronic Engineering and Automation, University of Jaen, 23009 Jaen, Spain
Interests: wind farm; automation; control of electrical machines
Prof. Dr. Juan-Manuel Roldan-Fernandez

E-Mail Website
Guest Editor
Department of Electrical Engineering, Universidad de Sevilla, Camino de los Descubrimientos, 41092, Spain
Interests: electrical engineering; power systems; energy efficiency (electricity); renewable energies; electricity markets; life cycle cost analysis; data analytics

Special Issue Information

Dear Colleagues,

Although wind energy is technologically mature and commercially viable, the increasing size of the wind farms currently being installed and planned presents new challenges for the scientific community, which must offer solutions that can be effectively utilized by developers. This Special Issue aims to gather research that can be readily applied to a wide range of scenarios, rather than being limited to overly constrained test environments.

In this context, we welcome reviews or original research articles focusing on onshore or offshore wind farms with a single turbine model, addressing topics such as the following:

  • Optimization of annual energy production.
  • Wake characterization.
  • Wind resource assessment.
  • Mitigation of noise or visual impacts.
  • Optimization of electrical infrastructure.
  • Costs associated with wind farm construction and operation.
  • Interaction between nearby wind farms.

The expected contribution of these studies is to establish a solid foundation for understanding turbine interactions caused by wake effects and to realistically address wind farm design, considering technical, economic, environmental, and social acceptance factors.

Prof. Dr. Angel G. Gonzalez-Rodriguez
Prof. Dr. Juan-Manuel Roldan-Fernandez
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • wind farms
  • optimization
  • micro-siting
  • visual impact
  • noise impact
  • wake models
  • electrical infrastructure
  • techno-economic assessment

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Published Papers (3 papers)

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Research

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22 pages, 9821 KiB  
Article
Edge–Cloud Intelligence for Sustainable Wind Turbine Blade Transportation: Machine-Vision-Driven Safety Monitoring in Renewable Energy Systems
by Yajun Wang, Xiaodan Wang, Yihai Wang and Shibiao Fang
Energies 2025, 18(8), 2138; https://doi.org/10.3390/en18082138 - 21 Apr 2025
Viewed by 257
Abstract
The transportation of wind turbine blades in remote wind farm areas poses significant safety risks to both personnel and infrastructure. These risks arise from collision hazards, complex terrain, and the difficulty of real-time monitoring under adverse environmental conditions. To address these challenges, this [...] Read more.
The transportation of wind turbine blades in remote wind farm areas poses significant safety risks to both personnel and infrastructure. These risks arise from collision hazards, complex terrain, and the difficulty of real-time monitoring under adverse environmental conditions. To address these challenges, this study proposes an intelligent safety monitoring framework that combines machine vision with edge–cloud collaboration. The framework employs an optimized YOLOv7-Tiny model. It is enhanced with convolutional block attention modules (CBAMs) for feature refinement, CARAFE upsampling for better contextual detail, and bidirectional feature pyramid networks (BiFPNs) for multi-scale object detection. The system was validated at the Lingbi Wind Farm in China. It achieved over 95% precision in detecting safety violations, such as unauthorized vehicle intrusions and personnel proximity violations within 2 m, while operating at 48 frames per second. The edge–cloud architecture reduces latency by 30% compared to centralized systems. It enables alert generation within 150 milliseconds. Dynamic risk heatmaps derived from real-time data help reduce collision probability by 42% in high-risk zones. Enhanced spatial resolution further minimizes false alarms in mountainous areas with poor signal conditions. Overall, these improvements reduce operational downtime by 25% and lower maintenance costs by 18% through proactive hazard mitigation. The proposed framework provides a scalable and energy-efficient solution for safety enhancement in renewable energy logistics. It balances computational performance with flexible deployment and addresses key gaps in intelligent monitoring for large-scale wind energy projects. This work offers valuable insights for sustainable infrastructure management. Full article
(This article belongs to the Special Issue Advancements in Wind Farm Design and Optimization)
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27 pages, 7559 KiB  
Article
Multi-Column Semi-Submersible Floating Body Hydrodynamic Performance Analysis
by Wei Wang, Jingyi Hu, Cheng Zhao, Yonghe Xie, Xiwu Gong and Dingliang Jiang
Energies 2025, 18(8), 1884; https://doi.org/10.3390/en18081884 - 8 Apr 2025
Viewed by 258
Abstract
Due to the limited availability of land resources, offshore wind turbines have become a crucial technology for the development of deep-water renewable energy. The multi-floating body platform, characterized by its shallow draft and main body located near the sea surface, is prone to [...] Read more.
Due to the limited availability of land resources, offshore wind turbines have become a crucial technology for the development of deep-water renewable energy. The multi-floating body platform, characterized by its shallow draft and main body located near the sea surface, is prone to significant motion in marine environments. The proper chamfering of the heave plate can effectively enhance its resistance during wave action, thereby improving the stability of the floating platform. The optimal chamfer angle is 35°. Considering the complexity of the floating body’s motion response, this study focuses on the damping characteristics of the heave plate with 35° chamfered perforations. Using the NREL 5 MW three-column semi-submersible floating wind turbine platform as the research model, the hydrodynamic characteristics of the floating body with a perforated heave plate are systematically studied through theoretical analysis, numerical simulation, and physical tests. The amplitude of vertical force under various working conditions is measured. Through theoretical analysis, the additional mass coefficient and additional damping coefficient for different working conditions and models are determined. The study confirms that the heave plate with 35° chamfered perforations significantly reduces heave in the multi-floating body. Full article
(This article belongs to the Special Issue Advancements in Wind Farm Design and Optimization)
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Review

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29 pages, 1050 KiB  
Review
Advancements in Wind Farm Control: Modelling and Multi-Objective Optimization Through Yaw-Based Wake Steering
by Tiago R. Lucas Frutuoso, Rui Castro, Ricardo B. Santos Pereira and Alexandra Moutinho
Energies 2025, 18(9), 2247; https://doi.org/10.3390/en18092247 - 28 Apr 2025
Viewed by 31
Abstract
Wind energy is paramount to the European Union’s decarbonization and electrification goals. As wind farms expand with larger turbines and more powerful generators, conventional ‘greedy’ control strategies become insufficient. Coordinated control approaches are increasingly needed to optimize not only power output but also [...] Read more.
Wind energy is paramount to the European Union’s decarbonization and electrification goals. As wind farms expand with larger turbines and more powerful generators, conventional ‘greedy’ control strategies become insufficient. Coordinated control approaches are increasingly needed to optimize not only power output but also structural loads, supporting longer asset lifetimes and enhanced profitability. Despite recent progress, the effective implementation of multi-objective wind farm control strategies—especially those involving yaw-based wake steering—remains limited and fragmented. This study addresses this gap through a structured review of recent developments that consider both power maximization and fatigue load mitigation. Key concepts are introduced to support interdisciplinary understanding. A comparative analysis of recent studies is conducted, highlighting optimization strategies, modelling approaches, and fidelity levels. The review identifies a shift towards surrogate-based optimization frameworks that balance computational cost and physical realism. The reported benefits include power gains of up to 12.5% and blade root fatigue load reductions exceeding 30% under specific scenarios. However, challenges in model validation, generalizability, and real-world deployment remain. AI emerges as a key enabler in strategy optimization and fatigue damage prediction. The findings underscore the need for integrated approaches that combine physics-based models, AI techniques, and instrumentation to fully leverage the potential of wind farm control. Full article
(This article belongs to the Special Issue Advancements in Wind Farm Design and Optimization)
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