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Modeling, Control and Optimization of Wind Power Systems

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: 20 November 2025 | Viewed by 584

Special Issue Editor


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Guest Editor
Department of Electrical Engineering, Polytechnic University of Timisoara, Timisoara, Romania
Interests: electric machines and drives; renewable energies; industrial automation; sensors and measurements; virtual instrumentation

Special Issue Information

Dear Colleagues,

As the world transitions towards sustainable energy solutions, wind power systems have emerged as a cornerstone of modern renewable energy infrastructure. This shift necessitates significant advancements in the modeling, control, and optimization of wind power systems to meet the growing demand for efficient, reliable, and cost-effective energy solutions.

This Special Issue focuses on the latest innovations in the design, analysis, and management of wind power systems, aiming to improve their performance, reliability, and adaptability in diverse operating conditions. Topics of interest include novel techniques in system modeling, state-of-the-art control methodologies, and optimization approaches that maximize energy output while minimizing operational costs and environmental impacts.

Topics of interest for publication include, but are not limited to, the following:

  • Advanced modeling techniques for wind turbines and entire wind power systems;
  • Innovative control strategies for enhancing stability and efficiency;
  • Optimization of energy capture and integration into power grids;
  • Fault detection, diagnosis, and tolerance methods for wind power systems;
  • Hybrid systems combining wind power with other renewable energy sources;
  • AI and machine learning applications in wind power system management;
  • Novel materials and designs for enhanced turbine performance;
  • Grid-friendly technologies for seamless power integration;
  • Economic and environmental analyses for wind energy optimization.

We invite you to contribute your groundbreaking research to this Special Issue, helping to shape the future of wind power technology.

Dr. Ciprian Sorandaru
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 power systems
  • system modeling
  • control strategies
  • optimization
  • fault tolerance
  • grid integration
  • hybrid renewable systems
  • AI applications
  • advanced materials

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Published Papers (2 papers)

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Research

18 pages, 1533 KiB  
Article
A Data-Driven Observer for Wind Farm Power Gain Potential: A Sparse Koopman Operator Approach
by Yue Chen, Bingchen Wang, Kaiyue Zeng, Lifu Ding, Yingming Lin, Ying Chen and Qiuyu Lu
Energies 2025, 18(14), 3751; https://doi.org/10.3390/en18143751 (registering DOI) - 15 Jul 2025
Abstract
Maximizing the power output of wind farms is critical for improving the economic viability and grid integration of renewable energy. Active wake control (AWC) strategies, such as yaw-based wake steering, offer significant potential for power generation increase but require predictive models that are [...] Read more.
Maximizing the power output of wind farms is critical for improving the economic viability and grid integration of renewable energy. Active wake control (AWC) strategies, such as yaw-based wake steering, offer significant potential for power generation increase but require predictive models that are both accurate and computationally efficient for real-time implementation. This paper proposes a data-driven observer to rapidly estimate the potential power gain achievable through AWC as a function of the ambient wind direction. The approach is rooted in Koopman operator theory, which allows a linear representation of nonlinear dynamics. Specifically, a model is developed using an Input–Output Extended Dynamic Mode Decomposition framework combined with Sparse Identification (IOEDMDSINDy). This method lifts the low-dimensional wind direction input into a high-dimensional space of observable functions and then employs iterative sparse regression to identify a minimal, interpretable linear model in this lifted space. By training on offline simulation data, the resulting observer serves as an ultra-fast surrogate model, capable of providing instantaneous predictions to inform online control decisions. The methodology is demonstrated and its performance is validated using two case studies: a 9-turbine and a 20-turbine wind farm. The results show that the observer accurately captures the complex, nonlinear relationship between wind direction and power gain, significantly outperforming simpler models. This work provides a key enabling technology for advanced, real-time wind farm control systems. Full article
(This article belongs to the Special Issue Modeling, Control and Optimization of Wind Power Systems)
19 pages, 5580 KiB  
Article
Stand-Alone Operation of Multi-Phase Doubly-Fed Induction Generator Supplied by SiC-Based Current Source Converter
by Łukasz Sienkiewicz, Filip Wilczyński and Szymon Racewicz
Energies 2025, 18(11), 2753; https://doi.org/10.3390/en18112753 - 26 May 2025
Viewed by 357
Abstract
This paper investigates the performance of a five-phase silicon carbide (SiC)-based current-source converter (CSC) integrated with a Doubly Fed Induction Generator (DFIG) for wind energy applications. The study explores both healthy and faulty operation, focusing on system behavior under transient conditions and various [...] Read more.
This paper investigates the performance of a five-phase silicon carbide (SiC)-based current-source converter (CSC) integrated with a Doubly Fed Induction Generator (DFIG) for wind energy applications. The study explores both healthy and faulty operation, focusing on system behavior under transient conditions and various load scenarios in stand-alone mode. A novel five-phase space vector PWM strategy in dual coordinate planes is introduced, which enables stable control during normal and open-phase fault conditions. Experimental results demonstrate improved stator voltage and current quality, particularly in terms of reduced Total Harmonic Distortion (THD), compared to traditional voltage-source converter-based systems. Furthermore, the system maintains operational stability under a single-phase open fault, despite increased oscillations in stator quantities. The results highlight the potential of five-phase CSC-DFIG systems as a robust and efficient alternative for wind power plants, particularly in configurations involving long cable connections and requiring low generator losses. Future work will focus on enhancing fault-tolerant capabilities and expanding control strategies for improved performance under different operating conditions. Full article
(This article belongs to the Special Issue Modeling, Control and Optimization of Wind Power Systems)
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