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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: closed (20 April 2026) | Viewed by 5281

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Special Issue Editor

Dr. Ciprian Sorandaru

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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

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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 (6 papers)

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Research

24 pages, 5318 KB

Open AccessArticle

Assessment of Potential Wind Sites for Power Integration in Ethiopia: A Case Study of Arerti, Sela Dingay, Debre Berhan, Mega, and Gode

by Solomon Feleke, Mulat Azene, Degarege Anteneh, Wenfa Kang, Yun Yu, Mahshid Javidsharifi, Solomon Mamo, Josep M. Guerrero, Juan C. Vasquez and Yajuan Guan

Energies 2026, 19(6), 1440; https://doi.org/10.3390/en19061440 - 12 Mar 2026

Viewed by 618

Abstract

With hydropower supplying nearly 94% of Ethiopia’s electricity, the national power grid is extremely vulnerable to recurrent droughts and erratic rainfall. To mitigate this risk, this study examines the wind power potential across five specific locations: Arerti, Sela Dingay, Debre Berhan, Mega, and Gode. By combining on-site mast measurements with datasets from NASA and the Global Wind Atlas, we evaluated wind characteristics at industry-standard hub heights of 80 m and 100 m. The analysis focused on wind power density (WPD), Weibull stability parameters (k and c), and directional consistency. The results indicate that Gode and Mega are the premier choices for commercial development, showing average speeds above 8.5 m/s and power densities exceeding 500 W/m2 at the 100 m level. Gode stands out as the most reliable site, with a Weibull shape factor (k) of 2.8 and a scale factor (c) of 9.1 m/s. We modeled a standard 3 MW turbine while factoring in a 20% loss for real-world conditions; this yielded net annual energy productions of 9461 MWh (36% CF) for Gode, 9040 MWh (34.4% CF) for Mega, and 8619 MWh (32.8% CF) for Arerti. While Sela Dingay and Debre Berhan have lower initial yields, their feasibility improves significantly when using towers taller than 80 m. Wind rose data reveals that Gode and Arerti have highly unidirectional flows, which simplifies turbine micro-siting. Notably, Arerti provides a unique economic advantage due to its location right next to existing 132/230 kV transmission infrastructure and industrial load centers. Overall, these findings provide a definitive technical roadmap for Ethiopia to diversify its energy portfolio and meet its Climate-Resilient Green Economy (CRGE) objectives. Full article

(This article belongs to the Special Issue Modeling, Control and Optimization of Wind Power Systems)

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18 pages, 48764 KB

Open AccessArticle

Roof Speed-Up Effects in Isolated and Interacting Building Setups: Implications for Energy Harvesting

by Vera Wilden, Mirko Friehe, Ole Gottwald and Frank Kemper

Energies 2026, 19(4), 890; https://doi.org/10.3390/en19040890 - 9 Feb 2026

Cited by 1 | Viewed by 417

Abstract

This study investigates direction-dependent roof speed-up factors for both isolated and interacting building configurations and evaluates their influence on the energy-yield potential of small wind turbines (SWTs) in urban environments. A combined approach was adopted: A theoretical framework and wind-tunnel experiments were developed to establish a general understanding of the meteorological, aerodynamic, and energetic parameters governing rooftop wind energy conversion and to derive characteristic roof speed-up factors for standardized flat-roof configurations. Wind-tunnel experiments were conducted for four distinct building scenarios, differing in layout and surrounding interaction, under three representative wind directions. High-resolution velocity measurements were acquired at multiple rooftop positions and elevations to capture detailed flow-acceleration and turbulence patterns. The resulting data were then applied in a case study for a representative urban site in Aachen, Germany. The measured directional speed-up factors were combined with a Weibull wind-speed distribution and a representative SWT power curve to estimate annual energy yields. The results reveal pronounced spatial and directional variability in wind acceleration, with localized increases of up to 25%. These variations translate into substantial differences in expected turbine performance depending on mounting height, placement, and prevailing wind direction. To facilitate further research and practical use, the complete dataset is published openly as a benchmark for computational fluid dynamics (CFD) validation and as a planning resource for rooftop turbine siting. The study underscores the importance of local aerodynamic effects in urban wind-energy design and provides a methodological framework that links controlled wind-tunnel data with real-world wind statistics. Full article

(This article belongs to the Special Issue Modeling, Control and Optimization of Wind Power Systems)

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18 pages, 1144 KB

Open AccessArticle

Hypersector-Based Method for Real-Time Classification of Wind Turbine Blade Defects

by Lesia Dubchak, Bohdan Rusyn, Carsten Wolff, Tomasz Ciszewski, Anatoliy Sachenko and Yevgeniy Bodyanskiy

Energies 2026, 19(2), 442; https://doi.org/10.3390/en19020442 - 16 Jan 2026

Cited by 1 | Viewed by 544

Abstract

This paper presents a novel hypersector-based method with Fuzzy Learning Vector Quantization (FLVQ) for the real-time classification of wind turbine blade defects using data acquired by unmanned aerial vehicles (UAVs). Unlike conventional prototype-based FLVQ approaches that rely on Euclidean distance in the feature space, the proposed method models each defect class as a hypersector on an n-dimensional hypersphere, where class boundaries are defined by angular similarity and fuzzy membership transitions. This geometric reinterpretation of FLVQ constitutes the core innovation of the study, enabling improved class separability, robustness to noise, and enhanced interpretability under uncertain operating conditions. Feature vectors extracted via the pre-trained SqueezeNet convolutional network are normalized onto the hypersphere, forming compact directional clusters that serve as the geometric foundation of the FLVQ classifier. A fuzzy softmax membership function and an adaptive prototype-updating mechanism are introduced to handle class overlap and improve learning stability. Experimental validation on a custom dataset of 900 UAV-acquired images achieved 95% classification accuracy on test data and 98.3% on an independent dataset, with an average F1-score of 0.91. Comparative analysis with the classical FLVQ prototype demonstrated superior performance and noise robustness. Owing to its low computational complexity and transparent geometric decision structure, the developed model is well-suited for real-time deployment on UAV embedded systems. Furthermore, the proposed hypersector FLVQ framework is generic and can be extended to other renewable-energy diagnostic tasks, including solar and hydropower asset monitoring, contributing to enhanced energy security and sustainability. Full article

(This article belongs to the Special Issue Modeling, Control and Optimization of Wind Power Systems)

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13 pages, 5894 KB

Open AccessArticle

Wind Turbine Electric Signals Simulator

by Sorin Sintea, Cornel Panait, Bogdan Hnatiuc, Marian Tirpan, Catalin Pomazan and Mihaela Hnatiuc

Energies 2025, 18(18), 4951; https://doi.org/10.3390/en18184951 - 17 Sep 2025

Cited by 1 | Viewed by 831

Abstract

The development of green technologies in recent years in the field of wind energy conversion into electricity implies a technology transfer from the static switching field to the energy field. This paper presents a wind turbine simulator using a hardware solution following the energy conversion of a real turbine. We implemented this solution for educational and research purposes to train students in the process of electrical conversion in wind turbines. For the simulation, we chose an E82/2300 turbine, installed by ENERCON in a nearby geographical area. The turbine has the capacity to generate 2300 kW of electricity into grids. It has a direct coupling structure of the propeller to the generator. The solution is implemented on a multi-processor architecture with analog signal processing. The structure of a wind turbine is divided into three consecutive blocks, namely TUGEN, DCDC4X, and SIN3F. Each block of the simulator is designed with electronic components. The input and output signals of these blocks have similar waveforms to real signals, and their succession is interconditioned by process parameters. The innovation of the proposed solution is provided by software engineering applied to a hardware structure. The ratio between the simulated and real values is 1:60 in order to visualize the signals on a digital oscilloscope, mainly for educational purposes. Full article

(This article belongs to the Special Issue Modeling, Control and Optimization of Wind Power Systems)

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18 pages, 1539 KB

Open AccessArticle

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 - 15 Jul 2025

Cited by 2 | Viewed by 971

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 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)

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19 pages, 5580 KB

Open AccessArticle

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

Cited by 3 | Viewed by 1274

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 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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