energies-logo

Journal Browser

Journal Browser

Advanced Wind Energy Systems: Comprehensive Insights into Analysis, Design, Control, 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: 28 February 2025 | Viewed by 2191

Special Issue Editor


E-Mail Website
Guest Editor
The Energy Production and Infrastructure Center, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC 28223, USA
Interests: renewable energy systems; design, operation, manufacturing, and maintenance of wind energy systems; adaptive wind turbine blades; energy production and mitigate loads optimization; societal and environmental phenomena; microgrids; technical, societal, and economic aspects of rural electrification

Special Issue Information

Dear Colleagues,

In this era of advanced wind energy systems, the dominant presence of wind turbines on our landscapes signifies more than just a shift towards cleaner energy. They stand as beacons of modern advancements in design, control, analysis, and optimization. This progression is not limited to the vast stretches of traditional onshore farms but also spans to offshore behemoths, urbanized wind solutions, and elevated wind energy mechanisms. The integration of sophisticated aerodynamic models, innovative design strategies, and refined control mechanisms has culminated in wind turbines that boast unparalleled efficiency, tenacity, and versatility. Alongside this, the diversification of their deployment across varying terrains and conditions has intensified research on their monitoring, longevity, and fault tolerance, leading to the inception of turbines that are increasingly intelligent and durable.

The intention of this special issue is to curate and distribute the most contemporary breakthroughs concerning the design, optimization, application, control, and health monitoring of wind energy systems.

Areas that are particularly compelling for publication include, but are not limited to, the following:

  • High-fidelity, efficient aerodynamic models for wind turbines;
  • Socio-economic considerations in wind energy deployment and management;
  • AI and machine learning in wind turbine design and predictive control;
  • Digital twins for real-time turbine performance monitoring;
  • Eco-design principles for sustainable wind turbine lifecycle;
  • Additive manufacturing for turbine component enhancement;
  • Topology optimization for peak turbine efficiency;
  • Integrated design approaches emphasizing total turbine optimization;
  • IoT in turbine design for real-time data and adaptive operation;
  • Cloud-based tools for turbine modeling and optimization.

Your insights, research, and innovations in these domains would greatly enrich this collaborative endeavor. Let's continue to propel wind energy into the zenith of its potential!

Dr. John Hall
Guest Editor

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 energy
  • wind turbines
  • aerodynamic
  • onshore and offshore wind farms
  • AI and machine learning
  • modeling and optimization

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Review

Jump to: Other

20 pages, 689 KiB  
Review
Review of Data-Driven Models in Wind Energy: Demonstration of Blade Twist Optimization Based on Aerodynamic Loads
by James Roetzer, Xingjie Li and John Hall
Energies 2024, 17(16), 3897; https://doi.org/10.3390/en17163897 - 7 Aug 2024
Viewed by 588
Abstract
With the increasing use of data-driven modeling methods, new approaches to complex problems in the field of wind energy can be addressed. Topics reviewed through the literature include wake modeling, performance monitoring and controls applications, condition monitoring and fault detection, and other data-driven [...] Read more.
With the increasing use of data-driven modeling methods, new approaches to complex problems in the field of wind energy can be addressed. Topics reviewed through the literature include wake modeling, performance monitoring and controls applications, condition monitoring and fault detection, and other data-driven research. The literature shows the advantages of data-driven methods: a reduction in computational expense or complexity, particularly in the cases of wake modeling and controls, as well as various data-driven methodologies’ aptitudes for predictive modeling and classification, as in the cases of fault detection and diagnosis. Significant work exists for fault detection, while less work is found for controls applications. A methodology for creating data-driven wind turbine models for arbitrary performance parameters is proposed. Results are presented utilizing the methodology to create wind turbine models relating active adaptive twist to steady-state rotor thrust as a performance parameter of interest. Resulting models are evaluated by comparing root-mean-square-error (RMSE) on both the training and validation datasets, with Gaussian process regression (GPR), deemed an accurate model for this application. The resulting model undergoes particle swarm optimization to determine the optimal aerostructure twist shape at a given wind speed with respect to the modeled performance parameter, aerodynamic thrust load. The optimization process shows an improvement of 3.15% in thrust loading for the 10 MW reference turbine, and 2.66% for the 15 MW reference turbine. Full article
Show Figures

Figure 1

Other

Jump to: Review

30 pages, 1776 KiB  
Systematic Review
Advancing Wind Energy Efficiency: A Systematic Review of Aerodynamic Optimization in Wind Turbine Blade Design
by Ali Akbar Firoozi, Farzad Hejazi and Ali Asghar Firoozi
Energies 2024, 17(12), 2919; https://doi.org/10.3390/en17122919 - 13 Jun 2024
Viewed by 1204
Abstract
Amid rising global demand for sustainable energy, wind energy emerges as a crucial renewable resource, with the aerodynamic optimization of wind turbine blades playing a key role in enhancing energy efficiency. This systematic review scrutinizes recent advancements in blade aerodynamics, focusing on the [...] Read more.
Amid rising global demand for sustainable energy, wind energy emerges as a crucial renewable resource, with the aerodynamic optimization of wind turbine blades playing a key role in enhancing energy efficiency. This systematic review scrutinizes recent advancements in blade aerodynamics, focusing on the integration of cutting-edge aerodynamic profiles, variable pitch and twist technologies, and innovative materials. It extensively explores the impact of Computational Fluid Dynamics (CFD) and Artificial Intelligence (AI) on blade design enhancements, illustrating their significant contributions to aerodynamic efficiency improvements. By reviewing research from the last decade, this paper provides a comprehensive overview of current trends, addresses ongoing challenges, and suggests potential future developments in wind turbine blade optimization. Aimed at researchers, engineers, and policymakers, this review serves as a crucial resource, guiding further innovations and aligning with global renewable energy objectives. Ultimately, this work seeks to facilitate technological advancements that enhance the efficiency and viability of wind energy solutions. Full article
Show Figures

Figure 1

Back to TopTop