Advances in Wind Energy Technology: 2nd Edition

Topic Information

Dear Colleagues,

The popularity of wind energy has increased considerably in recent decades due to the awareness of clean energy sources and the motivation to minimize the effects of global warming. This raises challenges in the continuous and consistent development of wind turbine technology, ranging from blade design, logistical efficiency, and maintenance to the measurement and numerical tools being used for the holistic evaluation of wind turbine performance. We invite submissions for a Topic that addresses research, development, and industrial implementations, and perspectives focusing on, though not limited to, the following fields:

• Large wind turbines;
• Innovative wind turbine aerodynamic and structural designs;
• Nonconventional wind turbine technology;
• Wind turbine and wind farm control;
• Grid and system integration;
• The development of advanced measurement systems;
• Improved numerical prediction tools for wind energy analysis;
• Improved wind turbine maintenance, scheduling, lifetime assessment and health monitoring;
• Multidisciplinary approaches in wind energy socio-eco-technical aspects;
• Usage of data-driven approaches.

Dr. Galih Bangga
Dr. Martin Otto Laver Hansen
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.5	2011	16 Days	CHF 2400	Submit
Clean Technologies cleantechnol	4.7	8.3	2019	20 Days	CHF 1800	Submit
Electronics electronics	2.6	6.1	2012	16.4 Days	CHF 2400	Submit
Energies energies	3.2	7.3	2008	16.8 Days	CHF 2600	Submit
Journal of Marine Science and Engineering jmse	2.8	5.0	2013	16.5 Days	CHF 2600	Submit

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Published Papers (5 papers)

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All Applied Sciences Clean Technol. Electronics Energies JMSE

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22 pages, 5795 KB  

Open AccessArticle

Extreme Wind Power Output Scenario Generation Method Guided and Constrained by Statistical Features

by Dan Li, Xiangyang Liang, Minghan Qu, Yawen Zhen, Zhaoxi Lin and Bin Yao

Energies 2026, 19(4), 1020; https://doi.org/10.3390/en19041020 - 14 Feb 2026

Viewed by 166

Abstract

The increasing penetration of renewable energy and the frequent occurrence of extreme weather events have significantly heightened the uncertainty in power system operations. Simultaneously, the scarcity of renewable energy output samples under extreme meteorological conditions constrains the accurate assessment of extreme risks in [...] Read more.

The increasing penetration of renewable energy and the frequent occurrence of extreme weather events have significantly heightened the uncertainty in power system operations. Simultaneously, the scarcity of renewable energy output samples under extreme meteorological conditions constrains the accurate assessment of extreme risks in system planning and dispatch. To bridge this gap, this work aims to propose a method for generating extreme wind power output scenarios that possess both diversity and statistical accuracy under limited sample conditions. To address this, this paper proposes a method for generating scenarios of extreme wind power output guided and constrained by statistical features. First, multidimensional statistical features are extracted from historical wind power output scenarios and combined, and a quantile threshold method is applied to screen out extreme wind power output scenarios. Subsequently, based on differentiated application requirements of the power system, extreme scenarios undergo preliminary classification followed by category-specific clustering analysis, achieving refined classification of the scenario set. Building on this, an improved generative adversarial network model is constructed, and the Wasserstein distance and gradient penalty mechanism are introduced to enhance training stability. Additionally, a statistical feature self-attention mechanism and feature loss function are designed to effectively constrain the consistency between generated scenarios and real scenarios in key statistical features. Results demonstrate that the proposed method can generate a set of extreme wind power output scenarios with both diversity and statistical accuracy under limited sample conditions, providing effective data support for system safety operation and risk prevention and control. Full article

(This article belongs to the Topic Advances in Wind Energy Technology: 2nd Edition)

30 pages, 1869 KB  

Open AccessArticle

Airfoil Performance of Small-Scale Vertical Axis Wind Turbines Under Urban Low Wind Speeds Using DMST and LLFVW Models

by Raul Alberto Bernal-Orozco, Oliver Marcel Huerta-Chavez, Daniel Enrique Constantino-Recillas and Jorge Diaz-Salgado

Energies 2026, 19(4), 945; https://doi.org/10.3390/en19040945 - 11 Feb 2026

Viewed by 144

Abstract

This work presents a comparative analysis of six airfoil profiles for small-scale vertical axis wind turbines (VAWTs) operating under low wind speeds (2–8 m/s) typical of urban environments. Aerodynamic performance during startup and nominal operation is investigated using two widely adopted modeling approaches, [...] Read more.

This work presents a comparative analysis of six airfoil profiles for small-scale vertical axis wind turbines (VAWTs) operating under low wind speeds (2–8 m/s) typical of urban environments. Aerodynamic performance during startup and nominal operation is investigated using two widely adopted modeling approaches, the Double Multiple Streamtube (DMST) and the Lifting Line Free Vortex Wake (LLFVW) methods, implemented in the open-source QBlade framework. The objective of the study is to evaluate relative airfoil performance and the consistency of observed trends across aerodynamic models commonly used in early-stage VAWT design. The results demonstrate a fundamental trade-off between self-starting capability at low tip-speed ratios ($\lambda <2$) and power efficiency at nominal operating conditions ($2\le \lambda \le 4$). Low-Reynolds-number and VAWT-oriented airfoils (S1210, E387, and DU 06-W-200) show enhanced startup torque under weak inflow conditions, whereas symmetric NACA airfoils (NACA 0015 and NACA 0018) deliver higher power coefficients once operational tip-speed ratios are achieved. Comparison with experimental benchmark data indicates that the transient LLFVW model yields improved agreement relative to the stationary DMST approach, which tends to overestimate performance at moderate and high tip-speed ratios. Overall, the study provides practical guidance for airfoil selection in micro-scale VAWTs intended for urban applications, where reliable self-starting and efficient operation must be carefully balanced. Full article

(This article belongs to the Topic Advances in Wind Energy Technology: 2nd Edition)

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19 pages, 2065 KB  

Open AccessArticle

Multiscale Wind Forecasting Using Explainable-Adaptive Hybrid Deep Learning

by Fatih Serttas

Appl. Sci. 2026, 16(2), 1020; https://doi.org/10.3390/app16021020 - 19 Jan 2026

Viewed by 236

Abstract

This study presents a multiscale, uncertainty-aware hybrid deep learning approach addressing the short-term wind speed prediction problem, which is critical for the reliable planning and operation of wind energy systems. Wind signals are decomposed using adaptive variational mode decomposition (VMD), and the resulting [...] Read more.

This study presents a multiscale, uncertainty-aware hybrid deep learning approach addressing the short-term wind speed prediction problem, which is critical for the reliable planning and operation of wind energy systems. Wind signals are decomposed using adaptive variational mode decomposition (VMD), and the resulting wind components are processed together with meteorological data through a dual-stream CNN–BiLSTM architecture. Based on this multiscale representation, probabilistic forecasts are generated using quantile regression to capture best- and worst-case scenarios for decision-making purposes. Unlike fixed prediction intervals, the proposed approach produces adaptive prediction bands that expand during unstable wind conditions and contract during calm periods. The developed model is evaluated using four years of meteorological data from the Afyonkarahisar region of Türkiye. While the proposed model achieves competitive point forecasting performance (RMSE = 0.700 m/s and MAE = 0.54 m/s), its main contribution lies in providing reliable probabilistic forecasts through well-calibrated uncertainty quantification, offering decision-relevant information beyond single-point predictions. The proposed method is compared with a classical CNN–LSTM and several structural variants. Furthermore, SHAP-based explainability analysis indicates that seasonal and solar-related variables play a dominant role in the forecasting process. Full article

(This article belongs to the Topic Advances in Wind Energy Technology: 2nd Edition)

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16 pages, 2302 KB  

Open AccessArticle

A Day-Ahead Wind Power Dynamic Explainable Prediction Method Based on SHAP Analysis and Mixture of Experts

by Hao Zhang, Guoyuan Qin, Xiangyan Chen, Linhai Lu, Ziliang Zhang and Jiajiong Song

Energies 2026, 19(1), 124; https://doi.org/10.3390/en19010124 - 25 Dec 2025

Viewed by 295

Abstract

Traditional single-prediction models often exhibit limitations in meeting wind power prediction requirements in complex operational scenarios. Furthermore, the inherent “black-box” nature of deep learning models leads to limited interpretability of predictions, hindering effective support for grid dispatch planning. To address these issues, this [...] Read more.

Traditional single-prediction models often exhibit limitations in meeting wind power prediction requirements in complex operational scenarios. Furthermore, the inherent “black-box” nature of deep learning models leads to limited interpretability of predictions, hindering effective support for grid dispatch planning. To address these issues, this study proposes a novel day-ahead wind power prediction method, referred to as SHapley Additive exPlanations (SHAP)–Mixture of Experts (MoE), which integrates SHAP into an MoE framework. Here, SHAP is employed for interpretability purposes. This study innovatively transforms SHAP analysis into prior knowledge to guide the decision-making of the MoE gating network and proposes a two-layer dynamic interpretation mechanism based on the collaborative analysis of gating weights and SHAP values. This approach clarifies key meteorological factors and the model’s advantageous scenarios, while quantifying the uncertainty among multiple expert decisions. Firstly, each expert model was pre-trained, and its parameters were frozen to construct a candidate expert pool. Secondly, the SHAP vectors for each pre-trained expert were computed over all sample features to characterize their decision-making logic under varying scenarios. Thirdly, an augmented feature set was constructed by fusing the original meteorological features with SHAP attribution matrices from all experts; this set was used to train the gating network within the MoE framework. Finally, for new input samples, each frozen expert model generates a prediction along with its corresponding SHAP vector, and the gating network aggregates these predictions to produce the final forecast. The proposed method was validated using operational data from an offshore wind farm located in southeastern China. Compared with the best individual expert model and traditional ensemble forecasting models, the proposed method reduces the Root Mean Square Error (RMSE) by 0.23% to 4.92%. Furthermore, the method elucidates the influence of key features on each expert’s decisions, offering insights into how the gating network adaptively selects experts based on the input features and expert-specific characteristics across different scenarios. Full article

(This article belongs to the Topic Advances in Wind Energy Technology: 2nd Edition)

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24 pages, 7794 KB  

Open AccessArticle

Icing Monitoring of Wind Turbine Blade Based on Fiber Bragg Grating Sensors and Strain Ratio Index

by Yadi Tian, Zhaohui Zhang, Xiaojing Wang, Wanheng Li and Yang Xu

Energies 2025, 18(16), 4295; https://doi.org/10.3390/en18164295 - 12 Aug 2025

Cited by 4 | Viewed by 1375

Abstract

In cold regions, the power generation efficiency of wind turbines is affected by blade icing. Heavy icing on blades will change the aerodynamic configuration of the blades and can even cause blades to crack or break. Therefore, monitoring and deicing technologies are important [...] Read more.

In cold regions, the power generation efficiency of wind turbines is affected by blade icing. Heavy icing on blades will change the aerodynamic configuration of the blades and can even cause blades to crack or break. Therefore, monitoring and deicing technologies are important for the safe operation of wind turbines. This study proposes a novel strain ratio index based on mechanical analysis of icing, which causes the neutral axis shift and different strain ratio change between waving and shimmy directions. Data from the 5 kW wind turbine blade model in a low-temperature laboratory and the 1.5 MW full-scale field wind turbine monitoring over 1 year are used to validate the effectiveness of the proposed method. The proposed strain ratio index and icing detection criteria are derived from mechanical analysis with clear interpretability while reducing ambiguity from structural damage. The relationship between the strain ratio index and ice thickness is quantified through laboratory tests and validated by field applications, demonstrating the effectiveness and robustness under complex real-world service scenarios. Full article

(This article belongs to the Topic Advances in Wind Energy Technology: 2nd Edition)

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