Advances in Hydraulic, Wind, and Photovoltaic Power Generation Systems

Topic Information

Dear Colleagues,

The rapid transition toward low-carbon and sustainable energy systems has positioned hydraulic, wind, and photovoltaic (PV) power generation as cornerstone technologies of the modern power sector. As these renewable resources continue to scale up and penetrate power grids worldwide, new challenges and opportunities have emerged in system design, performance enhancement, operational reliability, forecasting accuracy, and coordinated integration. Addressing the inherent variability, uncertainty, and complexity of water–wind–solar systems requires continuous advances to be made in theory, technology, and practical methodologies.

In this Topic, we invite contributions that explore recent advances in hydraulic, wind, and photovoltaic power generation systems, covering the full lifecycle from resource assessment and system design to operation, maintenance, and end-of-life management. We particularly welcome studies on innovative technologies and methods such as advanced turbines and converters, intelligent control strategies, high-fidelity modeling and simulation, data-driven forecasting of power output and resources, condition monitoring, fault diagnosis, predictive maintenance, and lifecycle performance assessment. Research leveraging artificial intelligence, digital twins, uncertainty quantification, and multi-source data fusion to improve reliability, efficiency, and resilience is especially encouraged.

Furthermore, this Topic seeks contributions on the coordinated planning and optimal operation of hydro–wind–solar hybrid systems and their integration with energy storage, power grids, and emerging energy markets. Topics of interest include joint optimization of generation scheduling, water–energy coupling, flexibility provision, grid support services, and strategies to enhance system stability under high renewable penetration. Case studies, real-world applications, and comparative analyses that bridge theory and practice are highly valued.

By collating interdisciplinary research, methodological innovations, and practical experiences, this Topic aims to foster a deeper understanding of next-generation hydraulic, wind, and photovoltaic power generation systems. We welcome original research articles, reviews, and technical communications that contribute to advancing efficient, reliable, and sustainable renewable energy systems, supporting the global transition toward a resilient and carbon-neutral energy future.

Prof. Dr. Chaoshun Li
Prof. Dr. Xiaoyuan Zhang
Dr. Wenlong Fu
Dr. Xiangqu Xiao
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.9	6.1	2011	15 Days	CHF 2400	Submit
Energies energies	3.9	8.3	2008	16.7 Days	CHF 2600	Submit
Energy Storage and Applications esa	-	-	2024	15.0 days *	CHF 1000	Submit
Processes processes	3.4	5.7	2013	14.7 Days	CHF 2400	Submit
Sci sci	4.1	5.4	2019	28.2 Days	CHF 1400	Submit
Sustainability sustainability	4.1	8.9	2009	16.9 Days	CHF 2400	Submit
Wind wind	2.7	4.5	2021	23.7 Days	CHF 1200	Submit

* Median value for all MDPI journals in the first half of 2026.

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Published Papers (7 papers)

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All Processes Sci Sustainability Wind ESA Applied Sciences Energies

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16 pages, 1988 KB  

Open AccessArticle

Structural Design and Photoelectric Performance of Vertical Sunlight-Tracking Mid-Pane Photovoltaic Louver Window

by Hongwei Gong, Zhixian Zhu, Shuwang Li and Yi Han

Energies 2026, 19(14), 3296; https://doi.org/10.3390/en19143296 - 13 Jul 2026

Viewed by 237

Abstract

To address the bottleneck that traditional building blinds struggle with, namely synergistically achieve shading control and energy recovery, a vertical mid-pane photovoltaic (PV) louver based on a self-powered feedback mechanism was designed. This system utilizes the voltage difference generated by differential light exposure [...] Read more.

To address the bottleneck that traditional building blinds struggle with, namely synergistically achieve shading control and energy recovery, a vertical mid-pane photovoltaic (PV) louver based on a self-powered feedback mechanism was designed. This system utilizes the voltage difference generated by differential light exposure on photovoltaic thin-film cells to drive a motor, realizing zero-energy automatic tracking of the solar azimuth and dynamic adjustment of component angles. By establishing a mathematical model for sunlight-tracking power generation and combining it with COMSOL multiphysics simulation, the coupling effects of the PV louver angle, operating conditions, and solar terms on photoelectric performance were thoroughly analyzed. The research results indicate that when the PV louver angle increases from 60° to 150°, the power generation significantly improves by 80%. Compared with the non-tracking mode, the all-day power generation efficiency gain of the vertical tracking center-mounted PV louver can reach up to 19.68%. Driven by the seasonal evolution of the solar elevation angle, the direct radiation irradiance during the tracking period across four typical solar terms exhibits a distribution pattern characterized as “higher in winter, lower in summer, and intermediate in spring and autumn.” These findings provide a technical pathway integrating dynamic shading, passive photothermal regulation, and clean power generation for south-facing facades in hot summer and cold winter zones, offering significant reference value for enhancing the energy autonomy and low-carbon level of building envelopes. Full article

(This article belongs to the Topic Advances in Hydraulic, Wind, and Photovoltaic Power Generation Systems)

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39 pages, 3294 KB  

Open AccessArticle

Development in Surrogate-Based Polynomial Chaos with Adaptive Sobol Sensitivity Analysis for Uncertainty Quantification and Offshore 15 MW Wind Turbine Performance Prediction: Comparative, Icing, and Wind Farm Optimization Studies

by Mohamed Haris Baghli, Tewfik Baghdadli and Zakarya Ziani

Wind 2026, 6(2), 30; https://doi.org/10.3390/wind6020030 - 10 Jun 2026

Viewed by 421

Abstract

Accurate performance prediction for large offshore wind turbines requires a principled treatment of uncertainty in both the wind resource and the rotor design parameters. In the present work, we develop a surrogate-based, multi-level uncertainty quantification (UQ) framework coupling a physics-based Blade Element Momentum [...] Read more.

Accurate performance prediction for large offshore wind turbines requires a principled treatment of uncertainty in both the wind resource and the rotor design parameters. In the present work, we develop a surrogate-based, multi-level uncertainty quantification (UQ) framework coupling a physics-based Blade Element Momentum (BEM) solver with a spectral Polynomial Chaos Expansion (PCE) surrogate that replaces the expensive Monte Carlo loop and apply it to the IEA 15 MW offshore reference wind turbine. The framework is completed by Sobol variance-based global sensitivity analysis. The contribution is methodological rather than algorithmic: although each individual ingredient (PCE, Sobol, BEM, and Jensen) is well established, their joint deployment in a single, internally consistent, end-to-end probabilistic workflow that simultaneously delivers (i) aerodynamic–structural UQ with analytical Sobol ranking, (ii) a like-for-like cross-comparison of three reference turbines, (iii) a quantitative leading-edge icing degradation study, and (iv) a farm-level wake-steering optimization on the same IEA 15 MW reference rotor yields a unified probabilistic envelope from which manufacturing tolerances, cold-climate investment thresholds, and farm-layout/control trade-offs can be read off consistently. Five input parameters are treated as random variables: hub-height wind speed (Weibull, k = 2.2, c = 9.8 m/s), air density, blade chord length, twist angle, and rotor speed. A degree-4 sparse PCE is built by non-intrusive spectral projection using N = 5000 Sobol quasi-random realizations, which allows the Sobol indices to be recovered analytically from the expansion coefficients at essentially no extra cost. Three parallel engineering studies complement the core UQ analysis: (A) a head-to-head comparison of the NREL 5 MW, DTU 10 MW, and IEA 15 MW reference turbines; (B) a quantitative assessment of leading-edge ice accretion at four severity levels; and (C) a Jensen-based wake optimization for a 25-turbine offshore array with static wake steering. The main results are as follows: the turbine reaches C_p,max = 0.480 at λ_opt = 8.51, and an annual energy production (AEP) of 71,261 MWh/year (PCE: 70,840 ± 2,140 MWh/year, 95% CI). Wind speed emerges as the dominant driver of C_p variance (S₁ = 0.412), followed by blade twist (0.198) and chord (0.143). Severe icing (30 kg/m) reduces C_p by 18.2% and increases the blade-root Damage Equivalent Load (DEL) by 18.5%. For the array, the optimal spacing (s_x = 8D, s_y = 6D) gives a farm efficiency of 89.6% and 1296 GWh/year, and a 15° wake-steering offset adds a further +3.2% to farm AEP. Compared with plain Monte Carlo, the sparse PCE delivers the same statistics with about 36% fewer model evaluations and a relative error below 0.8%. Full article

(This article belongs to the Topic Advances in Hydraulic, Wind, and Photovoltaic Power Generation Systems)

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32 pages, 7359 KB  

Open AccessArticle

Towards Water and Energy Security in Rural Agriculture: Technical Analysis of an Autonomous Photovoltaic Pumping System

by Erick Galicia Vargas, Alfredo González Ortega, Jesús Aguayo Alquicira, Mario Ponce Silva and Susana Estefany de León Aldaco

Sci 2026, 8(6), 126; https://doi.org/10.3390/sci8060126 - 29 May 2026

Viewed by 641

Abstract

This study evaluates the technical feasibility of an autonomous photovoltaic pumping system for agricultural use in isolated communities, using a representative region of the Mixteca Poblana, Mexico, as a case study. A reference sizing methodology reported in the literature was adopted for the [...] Read more.

This study evaluates the technical feasibility of an autonomous photovoltaic pumping system for agricultural use in isolated communities, using a representative region of the Mixteca Poblana, Mexico, as a case study. A reference sizing methodology reported in the literature was adopted for the sizing of isolated systems, and subsequently enhanced through a structured methodological extension, applied in the final stage of the design, focused on the technical validation and commercial selection of system components. The base framework incorporates site characterization and crop selection criteria. Subsequent stages define the hydraulic and electrical design requirements for the extension of the methodology, such as the calculation of water demand, the determination of pump power, and the estimation of energy requirements. These parameters enable the integrated correlation between hydraulic demand and electrical system constraints in the selection of the main system components, including the pump, photovoltaic array, battery storage system, water storage tank, and inverter. The technical robustness of the combined approach was validated through a simulation performed using specialized solar pumping software, confirming the operational feasibility and replication potential in rural communities with similar conditions. Full article

(This article belongs to the Topic Advances in Hydraulic, Wind, and Photovoltaic Power Generation Systems)

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23 pages, 10063 KB  

Open AccessArticle

CFD Analysis and Performance Evaluation of an Interlocked (Negative-Gap) Savonius Dual-Rotor Configuration

by Konrad M. Hartung, Marvin Stumpe and Karsten Oehlert

Wind 2026, 6(2), 23; https://doi.org/10.3390/wind6020023 - 18 May 2026

Viewed by 1097

Abstract

This study investigates whether aerodynamic interaction effects in an interlocked (negative-gap) counter-rotating dual Savonius rotor configuration can improve the efficiency of drag-based vertical-axis wind turbines in urban wind conditions. Two-dimensional Computational Fluid Dynamics (CFD) simulations were performed in ANSYS Fluent 2025 R2 using [...] Read more.

This study investigates whether aerodynamic interaction effects in an interlocked (negative-gap) counter-rotating dual Savonius rotor configuration can improve the efficiency of drag-based vertical-axis wind turbines in urban wind conditions. Two-dimensional Computational Fluid Dynamics (CFD) simulations were performed in ANSYS Fluent 2025 R2 using both steady and unsteady RANS approaches, including dynamic meshing to enable collision-free rotation in the interlocked overlap region. The numerical setup was first validated for a single two-bucket reference rotor against published experimental data of torque and power coefficients and subsequently applied to dual-rotor configurations with negative gap distances. The results show that the dual-rotor arrangement redistributes torque production over the azimuth angle and yields a smoother and consistently positive mean static torque coefficient, indicating improved self-starting behavior compared to the single rotor. Under transient operation, the dual-rotor configuration yields higher power coefficient values across the entire investigated tip-speed ratio range. The highest performance gain is observed at a tip-speed ratio of $\lambda \approx 1.0$, where the peak power coefficient increases from $c_p\approx 0.25$ (single-rotor) to $c_p\approx 0.32$ (dual-rotor), corresponding to an improvement of the power coefficient of about $\mathsf{\Delta }{c}_p/c_{p0}\approx 28\%$. Full article

(This article belongs to the Topic Advances in Hydraulic, Wind, and Photovoltaic Power Generation Systems)

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26 pages, 4792 KB  

Open AccessArticle

An Equivalent Model for Cooling Tower Boundary Conditions in Industrial Recirculating Cooling Water Systems

by Wei Huang, Yucong Chen, Huokun Li, Zhongzheng He, Zhe Li, Bo Liu and Gang Wang

Energies 2026, 19(10), 2400; https://doi.org/10.3390/en19102400 - 16 May 2026

Cited by 1 | Viewed by 455

Abstract

To mitigate the risks of pressure surges and water hammer during accidental pump trips in industrial cooling water systems, accurate boundary modeling of cooling towers is essential. This study employs the Method of Characteristics (MOC) to evaluate four equivalent models for the central [...] Read more.

To mitigate the risks of pressure surges and water hammer during accidental pump trips in industrial cooling water systems, accurate boundary modeling of cooling towers is essential. This study employs the Method of Characteristics (MOC) to evaluate four equivalent models for the central riser shaft: Model A (constant level), Model B (two-way surge tank), Model C (dynamic coupling of shaft and distribution channel), and Model D (composite structure). Results indicate that Model A fails to reflect actual hydraulic states, producing an unrealistic pump reverse speed of −253.24 r/min and overly conservative estimates. While Models B, C, and D exhibit similar pressure trends, Model C most accurately captures the physical drainage process, realistically simulating how the shaft level stabilizes at the distribution channel elevation before declining. By accurately reflecting engineering hydraulics, Model C provides the most reliable basis for water hammer safety assessments. It is recommended for optimizing pump valve closure strategies, vacuum breaker installations, and siphon protection designs in power plant systems. Full article

(This article belongs to the Topic Advances in Hydraulic, Wind, and Photovoltaic Power Generation Systems)

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18 pages, 9634 KB  

Open AccessArticle

Load-Predictive Pitch Control Strategy for Wind Turbines Under Turbulent Wind Conditions Based on Long Short-Term Memory Neural Networks

by Daorina Bao, Peng Li, Jun Zhang, Zhongyu Shi, Yongshui Luo, Xiaohu Ao, Ruijun Cui and Xiaodong Guo

Energies 2026, 19(9), 2044; https://doi.org/10.3390/en19092044 - 23 Apr 2026

Viewed by 476

Abstract

Under turbulent wind conditions, rapid wind speed fluctuations can markedly increase the fatigue loads borne by wind turbine blades and towers. In practice, conventional PID pitch control based on speed feedback often struggles to deliver satisfactory load mitigation, mainly because the wind turbine [...] Read more.

Under turbulent wind conditions, rapid wind speed fluctuations can markedly increase the fatigue loads borne by wind turbine blades and towers. In practice, conventional PID pitch control based on speed feedback often struggles to deliver satisfactory load mitigation, mainly because the wind turbine system is highly nonlinear, strongly coupled, and subject to time-delay effects. To overcome these limitations, this paper proposes a load-predictive pitch control strategy built on a Long Short-Term Memory (LSTM) network. Specifically, the LSTM model is first employed to predict the hub-fixed tilt and yaw moments ahead of time. These predicted values are then introduced as feedforward signals and combined with the conventional speed-based pitch control signal as well as a proportional feedback term. After that, the inverse Coleman transformation is used to generate the individual pitch commands for each blade. To verify the effectiveness of the proposed method, co-simulations were carried out in FAST and MATLAB/Simulink on a 5000 KW distributed pitch-controlled wind turbine under IEC Kaimal spectrum wind conditions, with a mean wind speed of 18 m/s and Class B turbulence intensity. The results show that the LSTM prediction model achieves an R² of 0.998 on the test dataset, with an RMSE as low as 0.0051. Compared with the conventional pitch-based power control strategy, the proposed approach maintains the same average power output while significantly reducing fatigue loads, thereby contributing to a longer service life for the wind turbine. Full article

(This article belongs to the Topic Advances in Hydraulic, Wind, and Photovoltaic Power Generation Systems)

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26 pages, 12237 KB  

Open AccessArticle

SAMCM-SR: Applying SAM3 Under Data-Scarce Conditions for Cross-Modal Segmentation of Power Equipment Infrared Images with Super-Resolution Enhancement

by Junchao Wang, Xiang Wu, Tianrui Yang, Yin Wang, Mengru Xiao and Gaoxing Zheng

Appl. Sci. 2026, 16(5), 2351; https://doi.org/10.3390/app16052351 - 28 Feb 2026

Cited by 1 | Viewed by 775

Abstract

Infrared thermography is a significant and extensively utilized method for assessing the operational condition of power equipment. Nonetheless, the constrained spatial resolution of infrared imaging systems, imaging noise, and the inadequate representational capacity of single-modality data render the precise segmentation of power equipment [...] Read more.

Infrared thermography is a significant and extensively utilized method for assessing the operational condition of power equipment. Nonetheless, the constrained spatial resolution of infrared imaging systems, imaging noise, and the inadequate representational capacity of single-modality data render the precise segmentation of power equipment targets difficult, particularly in intricate backdrops and settings with weak structures. Simultaneously, obtaining high-quality pixel-level annotations for power equipment is expensive and laborious, leading to a scarcity of training samples and thus diminishing the efficacy of conventional supervised segmentation techniques. This research offers a super-resolution guided cross-modal segmentation strategy to tackle these issues in data-scarce circumstances and examines the applicability of the general-purpose segmentation model Segment Anything Model 3 (SAM3) for infrared image segmentation of power equipment. A super-resolution reconstruction framework based on a high-order degradation model is built to enhance low-resolution infrared images collected in real-world contexts. An Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN) -based network incorporating residual-in-residual dense blocks (RRDB) is utilized to reconstruct infrared thermograms, hence improving structural features and boundary representations. Secondly, the concurrently obtained visible-light images are improved by low-light enhancement methods, and an anchor-free object detection framework is employed to ensure accurate localization of power equipment targets. The identified areas in visible images are aligned with the coordinate system of infrared super-resolution images via cross-modal geometric transformation, establishing a cross-modal spatial prior that efficiently limits the search space for infrared segmentation and mitigates background interference. The general-purpose segmentation model SAM3 is introduced, utilizing cross-modal detection boxes as prompts to facilitate precise segmentation of power equipment targets in infrared super-resolution images, achieving high-accuracy segmentation without the necessity for extensive task-specific annotated data. The experimental results demonstrate that our proposed approach significantly improves both the accuracy and robustness of infrared image segmentation for power equipment under complex conditions, attaining a Jaccard index of 89.86% and a Dice coefficient of 91.12%, thereby validating its efficacy and practical applicability in data-scarce environments. Full article

(This article belongs to the Topic Advances in Hydraulic, Wind, and Photovoltaic Power Generation Systems)

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