Wind Turbine Research Articles

Renewable energy waste recycling

Broad field of technologies is related to the countries’ green hydrogen goals, with renewable energy technology (RE) being a significant part. RE’s ability to support green hydrogen industry is now being hindered by the waste issue. By 2050, the world will have accumulated up to 60–70 million tons of waste photovoltaic (PV) modules, 43.4 million tons of wind turbine blades and up to 1 million tons of lithium batteries. Existing technologies and recycling capacities are not capable of recycling these volumes, since the readiness levels of most of the applicable technologies is assessed as TRL3 - TRL8 and their economic efficiency is below the profitability level. The purpose of this work is to substantiate the need and calculate the amount of state support for the development of RE waste recycling technologies. It has been determined that the sum of economic losses in the absence of recycling by 2050 will amount to up to 215 billion dollars, including in the RE leading regions: in China 81 billion dollars, in the EU 42 billion dollars, in the USA 26 billion dollars. The regional distribution of waste volume is presented, grouped by energy price level: 30% of waste in countries with the highest energy tariffs, mainly in Europe, 20% in regions with an average price, including all of North America, 50% in regions with the lowest price - most Asian countries. The dynamics of the increase in the number of patents for the selected IPCs for the period 2000–2024 was obtained, and the leading countries were identified. It is shown that the effect of state support for recycling will be obtained in three industries: firstly, a cost-effective industry for recycling renewable energy waste will be formed, secondly, the availability of scarce materials for renewable energy producers will increase, and thirdly, unconstrained RE deployment will back the green hydrogen development. The timeframe of the countries’ hydrogen goals and the RE waste accumulation trends jointly indicate the urgency of the waste issue, when the analysis proves the reasonable request for the state involvement.

Opportunistic maintenance strategy for wind turbine systems based on fault correlation and multi-level maintenance theory

Opportunistic maintenance strategy for wind turbine systems based on fault correlation and multi-level maintenance theory

Validation of vibration reduction in barge-type floating offshore wind turbines with oscillating water columns through experimental and numerical analyses

Floating offshore wind turbines (FOWTs) are highly susceptible to vibrations caused by wind and sea wave oscillations, necessitating effective vibration reduction strategies to ensure stability and optimal performance. This study investigates the effectiveness of a barge-type FOWT integrated with oscillating water columns (OWCs) in reducing oscillations, particularly in rotational modes. A hybrid FOWT-OWCs system was designed, and its vibration mitigation capabilities were assessed through both numerical simulations and experimental tests. The numerical approach focused on controlling airflow in the OWCs, while the experimental tests validated these results under similar conditions. A strong agreement between the simulations and experiments was observed, particularly in reducing platform pitch oscillations, even under irregular wave conditions. The open OWC-based platform outperformed the closed design, reducing pitch angle oscillations from 17.51° to 14.38° for waves with a 10-s dominant frequency. Benchmark tests confirmed this trend, with the open moonpool-based platform achieving a reduction from 18.41° to 12.23°. These findings demonstrate the potential of OWCs to improve the stability and performance of FOWTs, with experimental validation providing confidence in the numerical predictions.

Evaluating priority strategies for decarbonising offshore wind turbine blades through lifecycle assessment

Abstract While offshore wind is at the early stage of expansion, global capacity is expected to increase rapidly, reaching 330 GW by 2031. This work uses lifecycle assessment (LCA) to evaluate the opportunity for offshore wind energy decarbonisation through wind blade sustainable developments. The findings from the LCA are used to give informed recommendations towards priority areas of development across the blade lifecycle, that are critical to accelerate the sector’s transition towards net-zero targets. The production of raw materials was found to be the largest contributor to cradle-to-gave global warming potential (GWP). The sector should prioritise the utilisation of more sustainable materials, with an emphasis on the decarbonisation of carbon fibre production. Waste produced during blade manufacturing alone accounts for 10% of the blade’s GWP; therefore, increasing the material efficiency in this phase of the lifecycle is a significant opportunity for blade decarbonisation and should be a focus for the sector going forward. O&M was found to be the second largest contributor to GWP, with full decarbonisation of O&M practices potentially realising an 8% reduction in GWP. A range of alterative blade material scenarios were analysed, finding that recyclable resin systems have the greatest potential to decarbonise offshore blades. There are currently no commercial recycling operations for these resins therefore scale up of the recycling technologies is needed before they can be recycled in practice. Additionally, the development of low impact, economically viable circular solutions for legacy blade waste must be an immediate priority for the wind energy sector, given the anticipated exponential growth in global wind turbine blade waste generation. Graphical abstract

Data-driven surrogate model for wind turbine damage equivalent load

Abstract. Aeroelastic simulations are employed to assess wind turbines in accordance with IEC standards in the time domain. These analyses enable the evaluation of fatigue and extreme loads experienced by wind turbine components. Such simulations are essential for several reasons, including but not limited to reducing safety margins in wind turbine component design by accounting for a wide range of uncertainties in wind and wave conditions and fulfilling the requirements of the digital twin, which necessitates a comprehensive set of simulations for calibration. Thus, it is essential to develop computationally efficient yet accurate models that can replace costly aeroelastic simulations and data processing. To address this challenge, we propose a data-driven approach to construct surrogate models for the damage equivalent load (DEL) based on aeroelastic simulation outputs. Our method provides a quick and efficient way to calculate DEL using wind input signals without the need for time-consuming aeroelastic simulations. Our study focuses on utilizing a sequential machine learning (ML) method to map wind speed time series to DEL. Additionally, we demonstrate the versatility of the developed and trained surrogate models by testing them on a wind turbine in the wake and applying transfer learning to enhance their predictive accuracy.

Wind flow characteristics through shrouded wind turbine using ANSYS

Wind energy is a mature and quick-growing source of energy in many nations. New wind energy technologies must be introduced to increase effectiveness and reduce the cost of wind turbine installation. Wind energy conversion experts believe that shrouded wind turbines are a promising technology that could raise output and efficiency levels. A low-pressure zone is created behind the turbine because of the shroud’s unique structure. As a result, the turbine draws in greater mass flow to produce more power. With the aid of this innovative technology, wind turbines could be used in populated areas where lower wind speeds could be captured. In this study, a comparative analysis has been applied to different designs of shrouded wind turbines to analyze the wind speed, pressure, output power and power coefficient. ANSYS software has been conducted in this paper where the results obtained that the output power could be increased up to 3-4 times compared to the bare wind turbine, also a high-power coefficient could be reached up to 83.1%.

A Numerical Investigation on the Aeroacoustic Noise Emission from Offshore Wind Turbine Wake Interference

Offshore wind turbine (WT) wake interference will reduce power generation and increase the fatigue loads of downstream WTs. Wake interference detection based on aeroacoustic noise is believed to solve these challenges in offshore wind farms. However, aeroacoustic noise is closely related to the aerodynamics around WT blades, and the acoustic detection method requires the mastery of noise emission characteristics. In this paper, FAST.Farm, combined with the acoustic model in OpenFAST, is utilized to investigate the acoustic noise emission characteristics from two 3.4 MW-130 WTs with wake interference. Multi-microphone positions were investigated for the optimal reception selection under 8 m/s and 12 m/s wind speeds with a typical offshore atmospheric turbulence intensity of 6%. The numerical simulation results indicate that wake deficit reduces the total noise emission by about 6 dBA in the overall sound pressure level (OASPL) at 8 m/s, while wake turbulence marginally increases it and its fluctuation. There is a mutual influence between these effects, and the wake deficit effect can be 100% compensated for in the OASPL at 12 m/s. Additionally, downstream observer locations are suggested based on comparisons. These investigations provide new insights into wake interference in offshore wind farms.

Modeling of partially oriented spring-exchange magnetic composites

PurposeThe presentation refers to simulations of magnetization processes of the spring-exchange magnetic composites containing magnetically soft and ultra-high coercive phases. In particular, the aim of this study is to investigate the possibility of reducing expensive rare earth (RE) in the so-called neodymium magnets and improving their efficiency.Design/methodology/approachIn order to model hysteresis loops, a special disorder-based Monte Carlo procedure, suitable for irregular geometry of the composites, was applied. The chosen system parameters were defined in order to model Nd2Fe14B/Fe composites.FindingsThe results suggest potential for optimizing hard magnetic composites. Magnetization curve parameters are sensitive to grain coupling and easy magnetization axis ordering. Strong coupling for a single-phase hysteresis loop is unachievable for grains above a certain size, i.e. found to be a few hundred nanometers. Considering these factors and their interdependencies, it’s possible to enhance the |BH|max parameter or reduce the RE content.Research limitations/implicationsThe research was carried out using computer simulations, which by their nature are only approximations of physical processes. The next stage of research is to produce the described composites and test their actual properties.Practical implicationsThe research enhances permanent magnets, boosting efficiency in technologies like wind turbines and electric motors, indirectly benefiting the environment. It also reduces RE elements in magnets for environmental, economic and political gains.Originality/valueThe unique approach is to consider the random orientation of the magnetic anisotropy of the hard magnetic grains, which is close to real powder composites. The results provide valuable guidance for the production process of permanent magnets.

The Way Forward in the Energy Transition; Good Practices and Challenges at Wageningen University & Research

Reducing greenhouse gas (GHG) emissions due to changes in energy used in buildings is the most successful part of Wageningen University & Research’s (WUR) climate policy. In this article the authors evaluate sustainable energy measures and identify key success factors, based on internal documents, discussions with stakeholders, and relevant literature. The role of government and stakeholders has been important, as were the technical possibilities to take a major step with constructing the heat and cold storage on campus. Geopolitical factors also played a role, in particular the war in Ukraine, which increased energy prices, acutely reinforcing the need for energy savings. In addition to saving energy, WUR aims to contribute to the energy transition by generating green electricity with wind turbines and solar panels. Furthermore, electricity used from the grid is offset with wind energy through Dutch guarantees of origin (GVOs), allowing us to offset GHG emissions for electricity at zero. Yet, there are other reasons to save as much as possible on electricity use. In addition to environmental considerations, congestion of the electricity grid plays a role, which is largely associated with the energy transition. Looking ahead, we describe further potential energy reduction opportunities and related challenges. As described in the Rough Outline of WUR Energy Transition 2050 [1], WUR provides clear targets and possible sets of measures to achieve these targets. Challenges include the uncertainty surrounding technological solutions and the availability of funding. We recently expanded our carbon footprint to include Scope 3 emissions of purchased goods and services. An additional challenge is the reduction of implicit energy use through the purchasing chain.

Impact of Leading-Edge Tubercles on Airfoil Aerodynamic Performance and Flow Patterns at Different Reynolds Numbers

In recent years, leading-edge tubercles have gained significant attention as an innovative biomimetic flow control technique. This paper explores their impact on the aerodynamic performance and flow patterns of an airfoil through wind tunnel experiments, utilizing force measurements and tuft visualization at Reynolds numbers between 2.7 × 105 and 6.3 × 105. The baseline airfoil exhibits a hysteresis loop near the stall angle, with sharp changes in lift coefficient during variations in the angle of attack (AOA). In contrast, the airfoil with leading-edge tubercles demonstrates a smoother stall process and enhanced post-stall performance, though its pre-stall performance is slightly reduced. The study identifies four distinct flow regimes on the modified airfoil, corresponding to different segments of the lift coefficient curve. As the AOA increases, the flow transitions through stages of full attachment, trailing-edge separation, and local leading-edge separation across some or all valley sections. Additionally, the study suggests that normalizing aerodynamic performance based on the valley section chord length is more effective, supporting the idea that leading-edge tubercles function like a series of delta wings in front of a straight-leading-edge wing. These insights provide valuable guidance for the design of blades with leading-edge tubercles in applications such as wind and tidal turbines.

Experimental study and finite element analysis on shear performance of offshore wind turbine jacket foundation

The repeated extrusion of sea ice leads to the continuous collapse of marine structures. In this paper, the shear test and finite element simulation analysis of jacket foundation of large offshore wind turbine are carried out. The results show that the bearing capacity of the structure under positive displacement loading is slightly larger than that under negative displacement loading, and the bearing capacity is increased by 5.3 %. The lateral support joints have compression bending and fracture failure modes, and the maximum strain is 3600 με. Finally, the failure mode, energy dissipation capacity and bearing capacity of jacket foundation under reciprocating displacement load are obtained. At the same time, the ice load action model is established, and the research results provide experimental basis for improving the shear performance of offshore wind turbine.

Ultra-Short-Term Wind Power Forecasting in Complex Terrain: A Physics-Based Approach

This paper proposes a method based on Computational Fluid Dynamics (CFD) and the detection of Wind Energy Extraction Latency for a given wind turbine (WT) designed for ultra-short-term (UST) wind energy forecasting over complex terrain. The core of the suggested modeling approach is the Wind Spatial Extrapolation model (WiSpEx). Measured vertical wind profile data are used as the inlet for stationary CFD simulations to reconstruct the wind flow over a wind farm (WF). This wind field reconstruction helps operators obtain the wind speed and available wind energy at the hub height of the installed WTs, enabling the estimation of their energy production. WT power output is calculated by accounting for the average time it takes for the turbine to adjust its power output in response to changes in wind speed. The proposed method is evaluated with data from two WTs (E40-500, NM 750/48). The wind speed dataset used for this study contains ramp events and wind speeds that range in magnitude from 3 m/s to 18 m/s. The results show that the proposed method can achieve a Symmetric Mean Absolute Percentage Error (SMAPE) of 8.44% for E40-500 and 9.26% for NM 750/48, even with significant simplifications, while the SMAPE of the persistence model is above 15.03% for E40-500 and 16.12% for NM 750/48. Each forecast requires less than two minutes of computational time on a low-cost commercial platform. This performance is comparable to state-of-the-art methods and significantly faster than time-dependent simulations. Such simulations necessitate excessive computational resources, making them impractical for online forecasting.

Study on a novel dual-row vertical axis wind turbine with J-Shaped blades by using numerical methods and optimization

ABSTRACT This study investigates the benefits of using J-shaped blades in a dual-row Darrieus wind turbine (DDWT), combining two innovative ideas whose simultaneous impact on the self-starting capability of Darrieus turbines has not been explored. The performance of the turbine was simulated using Computational Fluid Dynamics (CFD) and optimized using both the Taguchi method and a combined Machine Learning and Genetic Algorithm (ML-GA) technique. Three main operating parameters, including the tip speed ratio (λ), radial ratio (δ), and angular distance (ϕ) of dual-row turbines, were investigated across four levels using an L25 orthogonal array design. According to the Taguchi analysis, the impact of the parameters on the power output was ranked in the order of λ > δ > ϕ. After comparison, the optimized turbine predicted by the ML-GA technique was chosen due to the superior accuracy of the ML-GA approach compared to the Taguchi method. The optimal configuration of a DDWT with J-shaped blades was predicted at λ = 2.15, δ = 1.39, and ϕ = 75.36, with a Cp of 0.49 based on CFD simulations. Compared to the single-row wind turbine, the proposed turbine showed a significant increase of the Cp at low tip speed ratios resulting in a better self-starting performance.

Enhancing wind turbine energy efficiency: Tribo-dynamics modeling and shape modification

The performance of wind turbine gearbox planetary bearing systems directly influences the turbine's reliability, energy production efficiency, and economy. However, the lack of a comprehensive model to accurately predict the dynamic meshing load of helical gears and temperature effects hinders a deeper understanding of the transient performance of gearbox planetary sliding bearings. Additionally, research on shape modification for gearbox planetary sliding bearings remains inadequate. This study develops a comprehensive tribo-dynamics model that considers the dynamic meshing forces of helical gears and temperature effects. The model's effectiveness is validated by the concordance between experimental data from a full-size test bench and simulation results on oil film thickness and temperature. Transient simulation analysis indicates insufficient lubrication, high-temperature rise, and severe contact that reduce electricity production efficiency and reliability, all of which occur at bearing axial edges. Therefore, three modified bearing designs (super-elliptical, trapezoidal, and quadratic shapes) are introduced, and their improved performance is thoroughly compared. The super-elliptical design increases the minimum oil film thickness by 2.60 μm. The trapezoidal design reduces cumulative friction energy losses by 4.34 %. Each modified design successfully reduces the edge wear and oil temperature. The underlying enhancing mechanisms are revealed to be oil film uniform redistribution and lubrication states' alteration. This work can contribute to the reliability, electricity production efficiency, and economy of global wind turbines and support the achievement of net-zero emission goals.

Techno-economic analysis of hybrid renewable energy systems for cost reduction and reliability improvement using dwarf mongoose optimization algorithm

The global energy crisis, particularly in isolated and remote regions, has increased interest in renewable energy sources (RESs) to meet growing energy demands. Integrating RESs with energy storage systems offers a promising solution to mitigate fluctuations and intermittency, but concerns about cost and reliability remain. This study explores the optimal design of various microgrid configurations, combining photovoltaic (PV), wind turbine (WT), battery energy storage system (BESS), and diesel generator (DG) systems for Najran city, Saudi Arabia, via real-world meteorological and load demand data. The Dwarf Mongoose Optimization Algorithm (DMOA), alongside the salp swarm algorithm (SSA) and whale optimization algorithm (WOA), was applied to minimize the levelized cost of energy (LCOE) while improving system reliability. The results demonstrate that the PV/BESS configuration, although cost-effective with an LCOE of 0.038 USD/kWh, fail to meet reliability constraints with a loss of power supply probability (LPSP) of 0.679. In contrast, the PV, WT, BESS, and DG configurations achieved an LPSP of 1.9 × 10^--8% with an LCOE of 0.199 USD/kWh, offering a robust and reliable solution for the region's energy needs. This paper presents a novel application of the DMOA for optimizing hybrid renewable energy systems, demonstrating its effectiveness in achieving a balance between cost and reliability. This strategy provides a viable approach for sustainable energy planning in similar regions facing energy challenges.

A comprehensive numerical model for aero-hydro-mooring analysis of a floating offshore wind turbine

This paper presents a comprehensive study of a Floating Offshore Wind Turbine (FOWT), requiring multidisciplinary expertise in floating platform hydrodynamics, mooring system dynamics, and wind turbine aerodynamics. We introduce a fully coupled numerical model, focusing specifically on the NREL's (National Renewable Energy Laboratory's) 5 MW OC4 FOWT. The model is validated through both numerical simulations using the Computational Fluid Dynamics (CFD) based software OpenFOAM and experimental results. Key findings demonstrate the model's accuracy in forecasting the aerodynamic behaviors of the turbine, the platform's response to motions, and the behavior of the mooring system across diverse wind and sea state scenarios. Furthermore, the study enhances the understanding of FOWT's stability and efficiency by examining the influence of different Center of gravity (COG) heights. Results show that reduction in COG height has a minor effect on heave and surge motion but significantly decreases pitch motion and mooring line tension, thereby improving static stability and reducing the impact of wave loads on dynamic responses. Additionally, the results show that this reduction in COG height enhances the aerodynamic power output, suggesting that optimized FOWT designs could achieve improved energy capture efficiency. These insights optimize FOWT design and efficiency, enhancing renewable energy performance.

Efficient multi-objective optimization approach for solving optimal DG placement and sizing problem in distribution systems

Incorporating distributed generations (DGs) like wind turbines (WTs) and solar photovoltaic (SPV) arrays into radial distribution systems (RDSs) has become increasingly important due to their benefits in enhancing system performance. This requires determining appropriate DG locations and their optimal power output when injected into the RDS. To this end, this paper introduces a novel application of a recent multi-objective optimization technique called multi-objective grey wolf optimization (MOGWO) to allocate multiple DG units into the RDS optimally. The primary objectives are to minimize total real power losses (RPL), reduce voltage deviation (VD), and enhance the voltage stability index (VSI) across the entire system while adhering to operational constraints. Various DG operational power factor (PF) scenarios are considered, including unity-PF, fixed-PF, and optimal-PF. The proposed approach generates a Pareto-optimal set of solutions to address the multi-objective problem, after which a fuzzy decision-making method is adopted to select the best trade-off solution from the set. The technique has been implemented on 69-bus and the actual Portuguese 94-bus RDS. Numerical results are compared with other recent well-known algorithms from the literature, highlighting the efficacy of the suggested MOGWO method in handling multi-objective problems. Comparatively speaking, it provides superior solutions than other methods in selecting appropriate control parameters.

A Novel Diagnostic Feature for a Wind Turbine Imbalance Under Variable Speed Conditions.

Dependency between the conventional imbalance diagnostic feature and the shaft rotational speed makes imbalance diagnosis challenging for variable-speed machines. This paper focuses on an investigation of this dependency and on a proposal for a novel imbalance diagnostic feature and a novel simplified version for this feature, which are independent of shaft rotational speed. An equivalent mass-spring-damper system is investigated to find a closed-form expression describing this dependency. By normalizing the conventional imbalance diagnostic feature by the obtained dependency, a diagnostic feature is proposed. By conducting comprehensive experimental trials with a wind turbine with a permissible imbalance, it is justified that the proposed simplified version of imbalance diagnostic feature is speed-invariant.

A novel trajectory planning method for mobile robotic grinding wind turbine blade

As the core component of wind turbine, wind turbine blade requires two grinding processes in the production. However, mobile robotic automation grinding of wind turbine blades is considered to be a challenging task due to the high aspect ratio and compound surface of the wind turbine blade. The trajectories generated by most robotic grinding trajectory planning algorithms are often found to be inferior in grinding large compound surface workpieces, as they are typically designed for robotic machining with a fixed base. In this paper, a novel iso-planar algorithm based on oriented bounding box (OBB) of the workpiece is developed to plan the grinding trajectories by taking into consideration the characteristics of blade. The constant chord length algorithm employing Taylor quadratic expansion is then developed to discretize trajectory into grinding points. Considering the characteristics of compound surface, a post-processing strategy is proposed to eliminate redundant grinding points and generate consistent tool orientations on compound surface. Based on these three steps, a workstation location optimization model for improving robot manipulability is introduced to determine a series of workstation locations. Furthermore, the grinding and movement synchronization strategy based on mobile platform trajectory interpolation is proposed to enhance the efficiency of machining large workpieces. The simulation and experiments demonstrate the effectiveness of the proposed trajectory planning method for mobile robotic grinding wind turbine blade, the rationality of the optimization model and the feasibility of grinding and movement synchronization strategy.

Deep analysis of power regulation on fatigue loads and platform motion in floating wind turbines

The fatigue loads experienced by components and the motion of the platform in floating wind turbines are likely influenced by their operation within a limited power state; however, the corresponding effects remain inadequately understood. This study investigates a 5 MW semi-submersible floating wind turbine, aiming to provide a comprehensive analysis of fatigue loads on critical components and platform motion under various power regulation modes. To achieve this objective, we employed the NREL 5 MW semi-submersible wind turbine model, generated three-dimensional wind fields using TurbSim, and conducted dynamic simulations with OpenFAST, utilizing MLife software for post-processing of fatigue load analysis. We assessed the fatigue loads of four key components—the blade root, tower base, drivetrain, and mooring cables—alongside the platform motion under varying generator speeds, yaw angles, and active power levels. Additionally, correlation analysis was performed to explore the relationship between component fatigue loads and platform motion under different yaw conditions. The results demonstrate that both generator speed and yaw angle significantly influence fatigue loads and platform motion, while the effect of active power appears to be relatively minor. Specifically, an increase in generator speed markedly elevates fatigue loads on the blade root and drivetrain, while concurrently reducing loads on the tower base and certain mooring lines. The analysis further reveals that, under various yaw angles, the fatigue loads on symmetrically positioned mooring cables exhibit a symmetric response, impacting fatigue damage in the blade root and tower base moments. Moreover, the relationship between maximum horizontal platform displacement and fatigue loads varies. The findings of this study provide critical insights into the operational optimization of floating wind turbines.