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Wind energy is a prominent renewable energy source, and Vertical Axis Wind Turbines (VAWTs) offer distinct advantages, including adaptability to changing wind directions and reduced noise levels. This paper conducts a numerical investigation into the impact of flat and curved stator blades on VAWTs, specifically the Savonius turbine, under 2D, viscous, turbulent, and steady flow conditions. Four stator blade configurations were examined, including no stator blades, smooth stator blades, twisted stator blades (Case A), and both blades being concave (Case B). The study reveals that curved stator blades enhance VAWT performance, with Case B exhibiting the most efficient performance. The results show pressure distribution on the turbine blades is non-uniform, with high and low-pressure zones, predominantly on the windward side. The presence of stator blades enhances pressure on all turbine blades, with Case B exhibiting the most optimal pressure distribution. Detailed observation of streamline and velocity distribution reveals improved flow lines for Case B, leading to more effective turbine blade performance. Case B consistently produces the highest turbine torque, with a maximum value of approximately 2.1 N·m achieved at Re = 15750. The torque demonstrates a positive correlation with increasing Reynolds numbers. The study further introduces a non-dimensional torque ratio analysis, where Case B attains 7.59 times higher torque than the reference case at Reynolds number 15750. The sensitivity of torque increase with respect to Reynolds number change highlights that Case B (with a slope of torque increase at around 4.5e-04) is the most responsive within the studied Reynolds number range.

Abstract This paper presents a lifting‐line implementation in the framework of a Lagrangian vortex particle method (LL‐VP). The novelty of the present implementation lies in the fluid particles properties definition and in the particles shedding process. In spite of mimicking a panel method, the LL‐VP needs some peculiar treatments described in the paper. The present implementation converges rapidly and efficiently during the shedding sub‐iteration process. This LL‐VP method shows good accuracy, even with moderate numbers of sections. Compared to its panel or vortex filaments counterparts, more frequently encountered in the literature, the present implementation inherently accounts for the diffusion term of the Navier‐Stokes equations, possibly with a turbulent viscosity model. Additionally, the present implementation can also account for more complex onset flows: upstream ambient turbulence and upstream turbine wakes. After validation on an analytical elliptic wing configuration, the model is tested on the Mexnext‐III wind turbine application, for three reduced velocities. Accurate results are obtained both on the analytical elliptic wing and on the New MEXICO rotor cases in comparison with other similar numerical models. A focus is made on the Mexnext‐III wake analysis. The numerical wake obtained with the present LL‐VP is close to other numerical and experimental results. Finally, a last configuration with three tidal turbines in interaction is considered based on an experimental campaign carried out at the IFREMER wave and current flume tank. Enhanced turbine‐wake interactions are highlighted, with favourable comparisons with the experiment. Hence, such turbine interactions in a farm are accessible with this LL‐VP implementation, be it wind or tidal energy field.

This paper introduces a machine vision method for measuring the blade tip clearance in a wind turbine. An industrial personal computer (IPC) is installed in the nacelle of the wind turbine to continuously receive video data from a digital camera mounted at the bottom of the nacelle. Using the open-source computer vision (OpenCV) digital image processing library data base, the real-time trajectory of the turbine blades is determined from the video data. Furthermore, fast Fourier transform (FFT) analysis is performed for determining the operating frequency of the blades in the images. The amplitude analysis performed at this operating frequency reveals the pixel-based blade tip clearance, which is then used to calculate the actual clearance of the wind turbine. This value is subsequently transmitted to the main controller of the wind turbine. The main controller can enhance the operational safety of the wind turbine by implementing appropriate pitch control strategies to restrict and safeguard the blade tip clearance. The results obtained by conducting experiments on a 2.0 MW wind turbine unit validate the effectiveness of the proposed identification method. In this method, the blade tip clearance can be calculated effectively in real time, and both the video sampling rate and communication speed meet the requirements for controlling the blade pitch. [ABSTRACT FROM AUTHOR]

In wind turbines, to investigate the cause of failures and evaluate the remaining lifetime, it may be necessary to measure their loads. However, it is often difficult to do so with only strain gauges in terms of cost and time, so a method to evaluate loads by utilizing only simple measurements is quite useful. In this study, we investigated a method with machine learning to estimate hub center loads, which is important in terms of preventing damage to equipment inside the nacelle. Traditionally, measuring hub center loads requires performing complex strain measurements on rotating parts, such as the blades or the main shaft. On the other hand, the tower is a stationary body, so the strain measurement difficulty is relatively low. We tackled the problem as follows: First, machine learning models that predict the time history of hub center loads from the tower top loads and operating condition data were developed by using aeroelastic analysis. Next, the accuracy of the model was verified by using measurement data from an actual wind turbine. Finally, individual pitch control, which is one of the applications of the time history of hub center loads, was performed using aeroelastic analysis, and the load reduction effect with the model prediction values was equivalent to that of the conventional method. [ABSTRACT FROM AUTHOR]

Nowadays, wind turbines have become one of the most significant sources of clean electricity generation because of their renewability and being free. The wind turbine control techniques must be robust to both effective wind speed fluctuations and modeling uncertainty. In the presence of fault effects, the control system should decrease the fault effects of the control process on the system structure and the generated power characteristics by maintaining an acceptable level of performance. This paper presents a novel adaptive controller with type 2 fuzzy fault tolerance based on the Radial Basis Functions (RBF) adaptive neural network observer that guarantees the system reliability in the condition that the speed sensor and generator torque bias are faulty. Utilizing fast nonsingular terminal sliding surface make high speed tracking and remove the singularity problem associated with conventional terminal sliding mode control. Also utilizing the fractional order operator in sliding surface make more degree of freedom and also by utilizing of long-term memory characteristic of fractional operator, more stability and robustness will be made and also the chattering effect will be decreased. Subsequently, fuzzy logic type 2 is employed to derive the switching control law, ensuring stability and eliminating chattering phenomenon. Additionally, an adaptive radial basis functions is utilized to estimate faults and uncertainties in the system. For better performance and fair comparison between the proposed method and the other methods, all the coefficients for all controllers is adjusted with ant colony optimization. The results of the suggested method are compared to those of a classic adaptive sliding mode controller. The tracking error for the proposed method in under normal conditions (without fault) has been approximately 0.04. The performance of the proposed method under two faulty conditions scenario, compared to ASMC, has shown an average superiority of 2.5 times under the application of 0.8 fault and a superiority of 1.5 times under the application of 1.2 fault. [ABSTRACT FROM AUTHOR]

Linking nano with macro – opportunities for steel construction Nanostructured metallic multilayers have significantly higher strength, fatigue resistance and ductility than monolithic homogeneous metal cross sections. The superior structural properties of these nanostructured cross sections are known, and so it is surprising why no attempt has been made to date to use nanostructured cross sections in macro cross sections in structural engineering to improve the cross section properties. This paper links the advantages of nanostructured multilayers with the needs of homogeneous metallic macro‐cross sections and examines the question of the extent to which the high‐performance material nanolaminate can compensate for the structural weak points of metallic infrastructure. The following weak points of metallic infrastructure are addressed: 1) the welded connection subjected to fatigue and corrosion, 2) additively printed metallic cross sections exposed to fatigue and corrosion. The article provides food for thought on how nanostructured multilayer can potentially contribute to the future of steel construction. As an example, the design and maintenance of cyclically loaded metallic infrastructure structures, such as bridges and offshore wind turbines, are discussed and it is shown how sustainability, resource conservation, reduction of the CO2 footprint, availability, security of supply and economic viability of steel infrastructure structures can potentially be achieved. [ABSTRACT FROM AUTHOR]

With the rapid development of wind power industry, a large number of wind turbines are put into use. In order to make wind turbine operate efficiently and reduce the high maintenance cost due to faulty downtime, wind turbine fault prediction methods were researched. Based on the supervisory control and data acquisition( SCADA) data of a 2 000 kW doubly fed wind turbine for 14 months, a convolutional neural network-bidirectional long short-term memory(CNN-BiLSTM) algorithm model was established. Firstly, a convolutional neural network was used to reject the abnormal noise data in the input data, extract the key local features of the input data, and simplify the complexity of the input data. Then the processed input data was put into a bi-directional long and short-term memory network to perform active power prediction, and the active power prediction was performed in a bi-directional long and shortterm memory network. Finally, the processed input data was put into a two-way long-short time memory network for active power prediction, and the residual difference between the real and predicted values of active power was analyzed to complete the prediction of wind turbine faults. The results show that the constructed algorithm model has the stability of fault prediction, and can eliminate the misprediction caused by many factors, and make the fault prediction 4 days earlier than the SCADA system of the wind turbine, which provides a guarantee for avoiding the sudden shutdown due to the deterioration of the fault. [ABSTRACT FROM AUTHOR]

To improve wind turbines' reliability and lifespan, taking the two-stage fixed shaft gearbox of wind turbines as study object. The oil stiffness and meshing stiffness of the transmission system are calculated, and the composite stiffness is obtained with the consideration of lubricating film. Then analyzed the dynamic characteristics of wind turbines' transmission system before and after consider the oil stiffness in different operating conditions. The findings indicate that after considering the gear lubrication effect, the composite stiffness gradually decreases as rotational speed increase; within a certain rotational speed range, the tooth-surface load can be reduced, making the system run more smoothly. Therefore, the presence of oil film and its impact on its stiffness cannot be ignored. This study has certain guiding significance in improving the transmission efficiency and reducing noise of wind turbines. [ABSTRACT FROM AUTHOR]

In order to improve the safety of wind turbine, this paper takes the high-speed spur gear on output shaft in the transmission system of the wind turbine as the study object. The original signals of the gear in different fault states which obtained from the experimental platform decompose by the empirical mode decomposition method (EMD) and the variational mode decomposition method (VMD), respectively. Then the different gear fault types' eigenvectors were built. Fuzzy neural network (FNN) is adopted to learn the fault types and eigenvector samples of gears, and then the fault types of gears are identified. It is found that the fault features decomposed by VMD method have reached a high accurate recognition rate point to the fact that VMD has good applicability in the fault recognition of gear in wind turbine. [ABSTRACT FROM AUTHOR]

Environmental concerns and energy security are pressing issues of the 21st century, with a heavy reliance on fossil fuels causing significant environmental pollution and resource depletion. To mitigate these problems, it is crucial to explore and implement alternative clean energy sources. This manuscript proposes a novel crayfish optimization algorithm (COA) for optimal scheduling in a hybrid power system that incorporates various renewable energy sources, like battery energy storage systems (BESS), fuel cells (FC), wind turbines (WT), micro turbines (MT) and photovoltaic (PV) panels. The importance of the work lies in its ability to optimize the entire operating costs of a grid‐connected microgrid while improving the accuracy and efficiency of energy management. The COA method addresses economic dispatch problems and manages energy within the grid‐connected microgrid, accounting for high levels of uncertainty. The proposed approach, tested using MATLAB Simulink, achieved a cost value of 252, outperforming existing methods such as GTO, PSO, SSA, and ALO. This illustrates the potential of the proposed technique to provide more cost‐effective and efficient energy management solutions in hybrid power systems. [ABSTRACT FROM AUTHOR]

Aiming at the problem of high failure rate and inconvenient maintenance of wind turbine gearboxes, this paper establishes a stochastic differential equation model that can be used to fit the change of gearbox oil temperature and adopts an iterative computational method and Markov-based modified optimization to fit the prediction sequence in order to realize the accurate prediction of gearbox oil temperature. The model divides the oil temperature change of the gearbox into two parts, internal aging and external random perturbation, adopts the approximation theorem to establish the internal aging model, and uses Brownian motion to simulate the external random perturbation. The model parameters were calculated by the Newton–Raphson iterative method based on the gearbox oil temperature monitoring data. Iterative calculations and Markov-based corrections were performed on the model prediction data. The gearbox oil temperature variations were simulated in MATLAB, and the fitting and testing errors were calculated before and after the iterations. By comparing the fitting and testing errors with the ordinary differential equations and the stochastic differential equations before iteration, the iterated model can better reflect the gear oil temperature trend and predict the oil temperature at a specific time. The accuracy of the iterated model in terms of fitting and prediction is important for the development of preventive maintenance. [ABSTRACT FROM AUTHOR]

This study investigates the influence of atmospheric stability and ground effects on wind turbine wake recovery, challenging the conventional linear relationship between turbulence intensity and wake expansion coefficient. Through comprehensive field measurements and numerical simulations, we demonstrate that the linear wake expansion assumption is invalid at far-wake locations under high turbulence conditions, primarily due to ground effects. We propose a novel nonlinear wake expansion model that incorporates both atmospheric stability and ground effects by introducing a logarithmic relationship between the wake expansion coefficient and turbulence intensity. Validation results reveal the superior prediction accuracy of the proposed model compared to typical engineering wake models, with root mean square errors of wake wind speed predictions ranging from 0.04 to 0.063. The proposed model offers significant potential for optimizing wind farm layouts and enhancing overall wind energy production efficiency. [ABSTRACT FROM AUTHOR]

With the large-scale integration of wind power, it is essential to establish an electromagnetic transient (EMT) model of a wind turbine system. Focusing on the problem of the difficulty in obtaining the parameters of the direct-driven permanent magnet synchronous generator (PMSG) model, this manuscript proposes a method based on trajectory sensitivity analysis and improved gray wolf optimization (IGWO) for identifying the parameters of the PMSG EMT model. First, a model of a PMSG wind turbine is established on an EMT simulation platform. Then, the key parameters of the model are determined based on the sensitivity analysis. Five control parameters are selected as the key parameters for their higher sensitivity indexes. Finally, the key parameters are accurately identified, using the proposed IGWO algorithm. The final case study demonstrates that the proposed IGWO algorithm has better optimization performance compared with the GWO algorithm and particle swarm optimization (PSO) algorithm. In addition, the simulation waveforms show that the identified parameters are accurate and applicable to other operating conditions. [ABSTRACT FROM AUTHOR]

In this work, we present the first three-dimensional (3D) computational investigation of wind turbine airfoils over 360 ° angles of attack to predict unsteady aerodynamic loads and vortex-shedding characteristics. To this end, static–airfoil simulations are performed for the FFA-W3 airfoil family at a Reynolds number of 10 7 with the Improved Delayed Detached Eddy Simulation turbulence model. Aerodynamic forces reveal that the onset of boundary-layer instabilities and flow separation does not necessarily coincide with the onset of stall. In addition, a comparison with two-dimensional simulation data and flat plate theory extension of airfoil polars, suggest that, in the deep stall regime, 3D effects remain critical for predicting both the unsteady loads and the vortex-shedding dynamics. For all airfoils, the vortex-shedding frequencies are found to be inversely proportional to the wake width. In the case of slender airfoils, the frequencies are nearly independent of the airfoil thickness, and their corresponding Strouhal number S t is approximately 0.15 . Based on the calculated S t , the potential for shedding frequencies to coincide with the natural frequencies of the International Energy Agency 15 MW reference wind turbine blades is investigated. The analysis shows that vortex-induced vibrations occur primarily at angles of attack of around ± 90 ° for all airfoils. [ABSTRACT FROM AUTHOR]

The PWM voltage pulses output by the inverter reaches the stator winding of the wind turbine generator through the cable. Due to the impedance mismatch between the motor and the cable, the voltage at the motor end rises under the action of the circuit wave process; on the other hand, electromagnetic oscillation occurs within the motor winding. Higher overvoltage is generated under the combined effect of both, which greatly accelerates the aging and breakdown of the insulation layer of the permanent magnet synchronous wind turbine. In this paper, a three-dimensional electromagnetic model of the permanent magnet synchronous wind turbine generator is established. The distribution circuit parameters of the stator winding of the permanent magnet machine are calculated using the finite element method, and its circuit model is based. The voltage distribution of the stator winding under different PWM excitations is investigated by simulation software, and the effects of the time of the rising edge and the pulse width of the PWM pulse on the stator winding voltage to ground and the turn-to-turn voltage distribution are studied. The results show that the effect of the rising edge time is larger when the rising edge time is shorter, the maximum voltage to ground occurs in the first coil, and the amplitude can be up to 1.5 times of the output voltage. When the rising edge time is longer, the maximum voltage to ground occurs in the end coil, and the amplitude is slightly higher than the output voltage. The trend of turn-to-turn voltage variation is similar for different rising edges; only the maximum turn-to-turn voltage amplitude is different. Pulse width has a small effect on overvoltage and only occurs when the pulse width is less than 10 μs. The research results of this paper are of great significance in revealing the aging and breakdown mechanism of the generator stator insulation, as well as the insulation coordination and design. [ABSTRACT FROM AUTHOR]

Icing has posed the significant impact on the aerodynamic performance and power output in wind turbines, particularly on the airfoil at the blade tip position. Therefore, it is particularly important to evaluate the performance of wind turbines before and after icing for real operating. According to the conditions of GW87/1500 wind turbine, this study aims to analyze the icing behavior of the blade tip airfoils and the aerodynamic performance using computational fluid dynamics. The objects were also adopted as the commonly-used DU-96W-180, DU-95W-180, and S810 airfoils at the blade tips of the wind turbine. Three kinds of operating conditions were set as the light, medium and heavy icing. The results show that there were the similar icing mass, thickness and limit of DU95-W-180 and DU96-W-180 airfoils. The smaller icing mass and thickness were found on the S810 airfoils, which was concentrated mainly on the lower surface of the airfoils; Under the light icing conditions, the angles of attack corresponding to the maximum lift-to-drag ratios of the three airfoils were the same as those under the uniced condition, which were about 7°. The angles of attack were advanced from 13° to about 11°, corresponding to the peak lift coefficients of the DU96-W-180 and DU95-W-180 airfoils. While the aerodynamic performance of the S810 airfoil was deteriorated more significantly, where the angle of attack was advanced from 13° to about 9° corresponding to the peak lift coefficient; Under the moderate icing conditions, the angles of attack were still about 7° corresponding to the maximum lift-todrag ratios of DU95-W-180 and DU96-W-180 airfoils, while those of S810 airfoils were advanced to around 5°. The peak lift coefficients were advanced from 13° to 9° corresponding to the peak lift coefficients of the DU96-W-180 and DU95-W-180 airfoils, while those of the S810 airfoils were advanced from 13° to 5°. Moreover, the lift and drag coefficients varied even more drastically. The aerodynamic performance of the three airfoils decreased more significantly under heavy icing conditions. The pressure distribution and surface flow pattern of the blade tip airfoils were measured under different icing conditions. There was the destroyed distribution of surface pressure on the three icing airfoils. The pressure coefficient at the leading edge of the airfoils was oscillated to reduce the lift, indicating the decreasing difference of pressure. There was the great variation in the flow distribution on the airfoil surface before and after icing of the three blade tip airfoils at 5° angle of attack. There was the increase in the separation vortices between the leading and trailing edge position of the DU95-W-180, DU96-W-180, and S810, when the icing condition was ranged from the light to the heavy. The flow separation was aggravated at the boundary layer of the airfoil surface. The better aerodynamic performance of DU96-W-180 airfoil was achieved before and after icing. The finding can be applied to optimize the airfoils of wind turbine blades in cold regions, in order to improve the energy utilization efficiency of wind turbines. [ABSTRACT FROM AUTHOR]

Wind turbines can freeze due to exposure to cold air. Ice formation on the rotor blades of a wind turbine reduces their performance. In the present work, the effects of ice formation on rotor blades of straight-blade vertical-axis wind turbines (SBVAWT) with a three-blade rotor and a NACA 0021 airfoil are numerically evaluated under two-dimensional transient settings by solving the continuity, momentum and turbulence equations become in ANSYS FLUENT. Grid and time step independence was investigated. For validation, the numerical model was compared with experimental data. An experimental ice model from the literature was then used to numerically simulate the iced rotor in twodimensional transition settings. The numerical simulation of the icy rotor was compared with an ice-free rotor. It was found that ice formation on the rotor blades changed the velocity and pressure fields around the rotor blades at angles of 180-360°, changing the streamlines and increasing the vortices. Furthermore, the maximum and minimum reductions in moment coefficient during blade icing occurred at angles of 225-315° and 45-135°, respectively. Due to ice formation on the rotor blades, the power coefficient of the rotor blades at angles 180-360° decreased drastically, and the power coefficient of the iced rotor was smaller than that of an ice-free rotor. It was concluded that ice formation on the blades of the SBVAWT reduced the average power coefficient of the blades and rotor power coefficient by 94.2% and 95%, respectively. [ABSTRACT FROM AUTHOR]

The rapidly increasing proportion of renewable energy sources has led to a reduction in the relative share of synchronous units, which has resulted in a decline in the inertia of the power system and a decrease in its voltage support capacity. This has led to several issues related to the frequency and voltage stabilities of the power system. To ensure these frequency and voltage stabilities, it is necessary to maintain a number of synchronous units online in the day-ahead generation schedule. First, the dynamic frequency change process after a power system fault is discussed, and a linear expression is derived for the frequency stability constraints involving energy storage systems and wind turbines. Second, the short-circuit capacity of the bus is characterized to describe the strength of voltage support, and the minimum short-circuit capacity requirement of the bus is solved based on the transient voltage recovery problem after clearance of the short-circuit faults. The total short-circuit capacity provided by the unit to the bus is then calculated through the network reactance matrix to establish the voltage stability constraint. Subsequently, the security-constrained unit commitment model considering frequency and voltage stabilities (FVS-SCUC) is established. Finally, the effectiveness of the proposed model is demonstrated through a numerical simulation of the IEEE-39 system comprising storage and wind turbines. The model ensures the frequency and voltage securities of the system, and the renewable energy output is improved upon considering energy storage. Thus, the overall system cost was reduced by nearly 30% by considering the frequency regulation effects of the energy storage system and wind turbine as well as the voltage regulation effects of the energy storage system. [ABSTRACT FROM AUTHOR]

This article proposes a method to optimize generation capacity (GC) according to the load diagram for a generator system by adopting the Symbiotic Organisms Search algorithm (SOS). In this paper, the generator system operates as an off-grid including Diesel Generator (DG), Solar Panels (PV), and Wind Turbine (WT). Regarding the applied algorithm, the SOS algorithm is used to solve the optimization problem to minimize electricity generation costs while still ensuring meeting load capacity according to demand within 24 hours with many other scenarios. Calculation results are performed with 3 cases, each corresponding to a specific generator system. They will be compared with DE-HS, DSM, and FO algorithms. The comparison results show the effectiveness of the SOS algorithm compared to the DE-HS, DSM, and FO algorithms. On the other hand, analyzing the obtained results, the convergence speed of the SOS algorithm in finding solutions achieves stability when increasing the number of iterations by ten times. However, the SOS algorithm still has some problems that need to be improved. That is to minimize the time to find the optimal solution because the algorithm has to perform many intermediate solution steps. Generator systems operating in off-grid mode are increasingly popular. In this system, generators have a variety of energy sources, including Diesel generators, solar power, and wind power. Many energy sources are integrated into a system, requiring a method to handle the optimal problem. [ABSTRACT FROM AUTHOR]

This paper presents a novel methodology for detecting faults in wind turbine blades using computational learning techniques. The study evaluates two models: the first employs logistic regression, which outperformed neural networks, decision trees, and the naive Bayes method, demonstrating its effectiveness in identifying fault-related patterns. The second model leverages clustering and achieves superior performance in terms of precision and data segmentation. The results indicate that clustering may better capture the underlying data characteristics compared to supervised methods. The proposed methodology offers a new approach to early fault detection in wind turbine blades, highlighting the potential of integrating different computational learning techniques to enhance system reliability. The use of accessible tools like Orange Data Mining underscores the practical application of these advanced solutions within the wind energy sector. Future work will focus on combining these methods to improve detection accuracy further and extend the application of these techniques to other critical components in energy infrastructure. [ABSTRACT FROM AUTHOR]

This study investigates the impact of atmospheric stability on wind turbine flow dynamics, focusing on wake deflection and meandering. Using the high-fidelity large-eddy simulation coupled with the Actuator Line model, we explore three stability conditions for the Vestas V80 turbine, both with and without yaw. The results indicate that wake meandering occurs predominantly along the deflected wake axis. Despite varying wake deficits and meandering behaviors, neutral and stable conditions exhibit similar wake deflection trajectories during yawed turbine operations. Spectral analysis of meandering reveals comparable cutoff and peak frequencies between neutral and stable cases, with a consistent Strouhal number ( S t = 0.16 ). The unstable condition shows significant deviations, albeit with associated uncertainties. Overall, increased stability decreases both oscillation amplitude and frequency, highlighting the complex interplay between atmospheric stability and wind turbine wake dynamics. [ABSTRACT FROM AUTHOR]

The imminent depletion of oil resources and increasing environmental pollution have driven the use of clean energy, particularly wind energy. However, wind turbines (WTs) face significant challenges, such as critical component failures, which can cause unexpected shutdowns and affect energy production. To address this challenge, we analyzed the Supervisory Control and Data Acquisition (SCADA) data to identify significant differences between the relationship of variables based on data reconstruction errors between actual and predicted values. This study proposes a hybrid short- and long-term memory autoencoder model with multihead self-attention (LSTM-MA-AE) for WT converter fault detection. The proposed model identifies anomalies in the data by comparing the reconstruction errors of the variables involved. However, more is needed. To address this model limitation, we developed a fault prediction system that employs an adaptive threshold with an Exponentially Weighted Moving Average (EWMA) and a fixed threshold. This system analyzes the anomalies of several variables and generates fault warnings in advance time. Thus, we propose an outlier detection method through data preprocessing and unsupervised learning, using SCADA data collected from a wind farm located in complex terrain, including real faults in the converter. The LSTM-MA-AE is shown to be able to predict the converter failure 3.3 months in advance, and with an F1 greater than 90% in the tests performed. The results provide evidence of the potential of the proposed model to improve converter fault diagnosis with SCADA data in complex environments, highlighting its ability to increase the reliability and efficiency of WTs. [ABSTRACT FROM AUTHOR]

The effects of the erosion present on the leading edge of a wind turbine airfoil (DU 96-W-180) on its aerodynamic performances have been investigated numerically in the framework of a SST k–ω turbulence model based on the Reynolds Averaged Navier-Stokes equations (RANS). The results indicate that when sand-induced holes and small pits are involved as leading edge wear features, they have a minimal influence on the lift and drag coefficients of the airfoil. However, if delamination occurs in the same airfoil region, it significantly impacts the lift and resistance characteristics of the airfoil. Specifically, as the angle of attack grows, there is a significant decrease in the lift coefficient accompanied by a sharp increase in the drag coefficient. As wear intensifies, these effects gradually increase. Moreover, the leading edge wear can exacerbate flow separation near the trailing edge suction surface of the airfoil and cause forward displacement of the separation point. [ABSTRACT FROM AUTHOR]

The focus of this research was on the equivalent particle roughness height correction required to account for the presence of ice when determining the performances of wind turbines. In particular, two icing processes (frost ice and clear ice) were examined by combining the FENSAP-ICE and FLUENT analysis tools. The ice type on the blade surfaces was predicted by using a multi-time step method. Accordingly, the influence of variations in icing shape and ice surface roughness on the aerodynamic performance of blades during frost ice formation or clear ice formation was investigated. The results indicate that differences in blade surface roughness and heat flux lead to disparities in both ice formation rate and shape between frost ice and clear ice. Clear ice has a greater impact on aerodynamics compared to frost ice, while frost ice is significantly influenced by the roughness of its icy surface. [ABSTRACT FROM AUTHOR]

Energy access is crucial for rural development in developing countries, as electrification drives economic growth, creates employment opportunities, and enhances the quality of life for rural communities. This study aims to determine the feasibility of powering a remote community with a hybrid energy system (HRS) combining solar photovoltaic, wind, and biogas to generate electricity and meet the energy needs of the rural area. West Waru Village was selected as the case study area due to its abundance of renewable energy sources. The HOMER tool was employed to model and optimize the HRS, providing a detailed analysis of its technical, economic, and environmental aspects. Furthermore, the study's findings were analyzed through a sensitivity analysis, considering uncertainty factors such as village load consumption, solar radiation, wind speed, and biomass availability. The best configuration for an on-grid scheme included a 2,284 kW photovoltaic (PV) system, 388 unit vertical axis wind turbine (VAWT), and a 500 kW biogas generator, resulting in a net present cost (NPC) of $8,506,090, a cost of energy (COE) of $0.054/kWh, and a payback period of 5.79 years. This configuration also reduced carbon dioxide (CO2) emissions by 67.2% compared to grid electricity. The optimal configuration for an off-grid scheme consisted of a 5,491 kW PV system, 954 VAWT, a 500 kW biogas generator, and 4,850 batteries, with an NPC of $20,162,390 and a COE of $0.1601/kWh, reducing CO2 emissions by 99.993%. These findings can serve as a baseline for the government to develop renewable energy systems in West Waru. [ABSTRACT FROM AUTHOR]

The aerodynamic performance of wind turbine airfoils is crucial for the efficiency and reliability of wind energy systems, with leading-edge roughness significantly impacting blade performance. This study conducts numerical simulations on the DU 00-W-401 airfoil to investigate the effects of leading-edge roughness. Results reveal that the rough airfoil exhibits a distinctive "N"-shaped lift coefficient curve. The formation mechanism of this nonlinear lift curve is primarily attributed to the development of the trailing-edge separation vortex and variations in the adverse pressure gradient from the maximum thickness position to the trailing-edge confluence. The impact of different roughness heights is further investigated. It is discovered that when the roughness height is higher than 0.3 mm, the boundary layer can be considered fully turbulent, and the lift curve shows the "N" shape stably. When the roughness height is between 0.07 mm and 0.1 mm, a transitional state can be observed, with several saltation points in the lift curve. The main characteristics of different flow regimes based on different lift curve segments are summarized. This research enhances the understanding of the effects of leading-edge roughness on the aerodynamic performance of a thick wind turbine airfoil, and the simulation method for considering the effect of leading-edge roughness is practical to be applied on large-scale wind turbine blade to estimate the aerodynamic performance under rough leading-edge conditions, thereby supporting advancements in wind turbine technology and promoting the broader adoption of renewable energy. [ABSTRACT FROM AUTHOR]

This manuscript introduces a novel multilevel middle point clamped (MMPC) DC-DC converter and its associated switching scheme aimed at maintaining the desired medium-voltage DC (MVDC) collector grid within offshore all-DC wind farms. Building upon previous work by the authors, which proposed an all-DC structure serving as a benchmark system, this study explores the application of the MMPC DC-DC converter within this framework. Within the all-DC wind generation system, a 9-phase hybrid generator (HG) integrated into the wind turbine is linked to the MVDC collector grid through an AC-DC stage, which is a passive rectifier. This passive rectifier offers elevated voltage ratings and protection against back power flow. The conventional neutral point clamped (NPC) converter concept has been thoroughly investigated and expanded upon to develop the proposed MMPC DC-DC converter. The proposed MMPC DC-DC converter integrates boosting capabilities, facilitating the connection of the generator's rectified voltage to the MVDC collector grid while regulating variable rectified voltage to a fixed MVDC collector grid voltage. The MVDC collector grid is further interconnected with high-voltage DC (HVDC) through a DC-DC converter situated in an offshore substation. This paper further provides a comprehensive overview of the proposed MMPC DC-DC converter, detailing its operational modes and corresponding switching schemes. Through an in-depth examination of operational modes, duty cycles for each switch and mode are defined, subsequently establishing the relationship between rectified input voltage and MVDC output voltage for the MMPC DC-DC converter. Utilizing the middle point clamped architecture, this innovative converter offers several advantages, including low ripple voltage, a modular structure, and reduced switching stress because of the multilevel voltage and the incorporation of a hard point, which also facilitates the capacitor voltage balancing. Finally, the effectiveness of the proposed converter is evaluated via simulation studies of a wind turbine conversion system utilizing two cascaded MMPC DC-DC converters operating under variable input voltage conditions. The simulations confirm its efficacy, supported by promising results, and validating its performance. [ABSTRACT FROM AUTHOR]

The pitch bearings of wind turbines are slowly oscillating, grease-lubricated slewing bearings. They facilitate the pitching movements of blades which control aerodynamic loads. These bearings have diameters of several meters, their blade-side sealings can face the environment, bending moment loads can cause radial deformation of the bearing rings, and their highly variable operating temperatures can facilitate condensation of water inside them. All of this makes water ingress into the lubricant possible. There is limited public knowledge with regards to the maximum water content for safe operation in this application. This work presents the results of a series of scaled wind turbine time series tests with both 'dry' (no water contamination) and 'wet' (10 mass % demineralized water added) greases. A set of four commercially available greases were tested. The time series were scaled from wind turbine operation and represented a 13.7 h worst-case scenario of operation with small oscillation amplitudes and no longer lubrication runs in between. Three of the greases showed reduced friction and no or limited raceway damage in the wet condition, whereas one showed increased friction and raceway damages. [ABSTRACT FROM AUTHOR]

This study introduces a constrained many‐objective optimization approach for the optimal design of 20 MW direct drive (DD) permanent magnet synchronous generators (PMSGs). Designing a high‐performance, competitive DD‐PMSG requires considering the generator's performance as well as its weight and material cost. Therefore, we focus on four main characteristics as our design objectives: (1) specific power (power per weight), (2) power‐per‐cost, (3) efficiency, and (4) power factor. To achieve this, we apply an advanced constrained nondominated sorting genetic algorithm III (NSGA‐III), a many‐objective optimization method utilizing evolutionary computation, capable of optimizing four or more objectives with constraints. Additionally, the electromagnetic finite element method is employed to evaluate the generator's characteristics. Through our proposed design process, we optimize three distinct 20 MW DD‐PMSG configurations: a 320‐pole/300‐slot, a 350‐pole/300‐slot, and a 350‐pole/336‐slot topology. Following this optimization, we perform additional multiphysics simulations (covering electromagnetic, structural, overload, and thermal aspects) and control response simulations on four selected models from the Pareto‐optimal solutions to validate their effectiveness as preliminary DD‐PMSG designs. Finally, we conduct a comprehensive analysis of all simulation results. [ABSTRACT FROM AUTHOR]

For optimizing the operational efficiency of a wind farm in complex terrain, the wake study of wind turbines and the detailed analysis of the wind data are essential. In this research, The Manjil wind farm located in the Alborz Mountains with special topography and distinct wind regimes has been studied. OpenFOAM has been applied for computational fluid dynamics simulations of two wind speeds at different reference heights as case studies. Then, a comparison between the simulation results and the available supervisory control and data acquisition dataset was made. The result assumed that the velocity of the wake is recovered up to 85% after ten times the diameter of the turbine. In this distance, there is a 14.5% and 15.5% speed reduction on average for two simulations, with velocity on the hub height of 9 and 17 m/s respectively. The analysis of raw wind data shows that a high potential of wind energy is available in summer and there is almost no wind in wintertime, which causes some challenges for developing the wind farms under these conditions. [ABSTRACT FROM AUTHOR]

This study presents a detailed computational fluid dynamics (CFD) investigation into the aerodynamic performance of the NREL Phase VI wind turbine, focusing on torque and power generation under different turbulence models. The primary objective was to analyse the effect of various turbulence models and their responses in wind turbine torque generation. Furthermore, it also investigates the distance effect on wind velocity deficit. The research utilizes 2D and 3D simulations of the S809 airfoil and the full rotor, examining the predictive capabilities of the k-epsilon, k-omega, and k-omega SST turbulence models. The study incorporates both experimental validation and wake analysis using the Gaussian wake model to assess wind velocity deficits. Simulations were conducted for a wind speed range of (6–10 m/s), with results indicating that the k-epsilon model provided the closest match to experimental data, particularly at higher wind speeds within the targeted range. Even though k-epsilon results had better agreement when validated with experimental data, theoretically k-omega (SST) should perform better as it combines k-epsilon and k-omega advantages in predicting the flow regardless of its farness from the wall. However, in simulations using the k-omega (SST), the separation of flow and the shear stress transients were only visible at wind speeds of 10 m/s or higher. Wake effects, on the other hand, were found to cause significant velocity deficits behind the turbine, following an exponential decay pattern. The findings offer valuable insights into improving wind turbine performance through turbulence model selection and wake impact analysis, providing practical guidelines for future wind energy optimizations. [ABSTRACT FROM AUTHOR]

A hybrid approach is proposed for optimizing distribution systems (DSs) by integrating clean energy sources, specifically photovoltaic (PV) and wind power (WT). The proposed technique combines the mud ring algorithm (MRA) and quantum neural network (QNN), referred to as the MRA‐QNN technique. The primary objective is to minimize power loss and enhance voltage stability. The MRA method generates the control signal of the converter, while the QNN method predicts the control signal based on the MRA output. The effectiveness of the approach is revealed through simulations on standard IEEE 33 bus and 69 bus systems. Implementation in MATLAB shows superior performance compared to existing methods, with lower power loss values. There has been a sustained rise in the system voltage profile (In the WT and PV situations, 0.950. and 93 p.u), as well as a considerable reduction in the active power (AP) losses (to 132.39 kW with PV and 81.23 kW with WT from 362.86 kW). With PV, the entire yearly economic loss is lowered from $158932.68 to just $57996.939, and with WT, it is decreased to $56805.479. With three PVs, the yearly economic loss and active power losses are decreased to 30419.871 $ and 69.449, and 4.27 kW and 1875.930 $, respectively. [ABSTRACT FROM AUTHOR]

To reduce the significant economic losses caused by the fault deterioration of wind turbine generators, it is urgent to detect and diagnose the early faults of generators. The existing condition monitoring and fault diagnosis (CMFD) methods have disadvantages of less considering data temporal characteristic, acquiring early faults with difficulty, and having lower diagnostic accuracy. To address those limitations, a novel LSDAE-stacking CMFD method of generators was proposed. Specifically, a multivariate spatiotemporal condition monitoring model (LSDAE) was established by combining the LSTM and SDAE networks, which can detect generator early anomalies through real-time monitoring the reconstruction residual. Then, based on the stacking ensemble algorithm, a multi-classification fault diagnosis model (Stacking) was constructed to identify early fault types, which can integrate advantages of different base-classifiers to achieve a better diagnostic accuracy. Case studies on three actual generator failures were employed to validate the effectiveness and accuracy of the proposed LSDAE-stacking method. The results illustrated that, compared with conventional SDAE model, the proposed LSDAE model had higher reconstruction precision and superior early-fault-warning capacities. And compared with traditional algorithms such as SVM, RF, AdaBoost, GBDT and XGBoost, the constructed Stacking model can effectively identify the fault types of generators and had higher diagnostic accuracy. [ABSTRACT FROM AUTHOR]

The wind power industry is rapidly developing under the '3060' dual-carbon strategy. Preventive maintenance has become crucial in improving the operational reliability of wind turbines. However, operational management of wind turbines in complex environments still lacks sufficient understanding of the degradation state and reliable maintenance strategies. This paper examines the relationship between the degradation process of wind turbine key components and a multi-stage preventive maintenance strategy. The objective is to minimize expected costs. Based on Markov degraded state transfer, a multi-stage preventive maintenance cost model is constructed. This paper describes a model that utilizes the equipment decline law and Markov chain to portray the degraded state and maintenance strategy. It also introduces reliability, failure rate, and service age factors to calculate multi-stage preventive maintenance and failure hours. Additionally, the model considers the influence of weather conditions on the maintenance cost of wind turbines throughout the maintenance cycle. Numerical simulation is used to solve and analyze the model. The results indicate that the minimum and replacement maintenance strategy accounts for over 80% of the total maintenance cost, while the preventive maintenance optimization strategy accounts for less than 20%. Therefore, wind power enterprises in low wind speed areas can use this strategy as a practical reference for decision-making to enhance the operational reliability of wind turbines. [ABSTRACT FROM AUTHOR]

In this paper, a new configuration of a horizontal axis wind turbine with three blades for domestic electricity production on farms was numerically simulated. Three configurations were tested numerically: a standard single-blade wind turbine (STWT), a single wind turbine (SWT), and a tandem three-blade wind turbine (TWT) for rural use. For the tandem wind turbine, the effects of pitch angle and the distance between the blades were also investigated. The Gambit and Fluent 19.2 codes were, respectively, used to generate different meshes and determine various parameters. The resolution of the averaged Navier–Stokes equations (RANS) using a finite volume method was conducted. The k–ε realizable two-equation model was chosen for turbulence calculation. The obtained results showed that the maximum power coefficient (Cp) was 0.444 for one of the tandem configurations (TWT), compared to the conventional blade (STWT). The maximum Cp obtained in this study is slightly greater than the experimental results found in the literature. It has also been observed that the flow on these new turbines exhibits a complex phenomenon on both surfaces. [ABSTRACT FROM AUTHOR]

This paper establishes a high-fidelity and efficient Computational Fluid Dynamics (CFD) numerical method (AL-LDS-nnew) for wind turbine wake by combining the actuator line (AL), the localized dynamic Smagorinsky (LDS) sub-grid scale (SGS), and the new generation nnew vortex identification method under the framework of large eddy simulation. The model advantages are encouraging: 1) In terms of turbine modeling, the AL model is adopted to replace the traditional threedimensional solid model, which avoids solving the boundary layer on the blade surface and improves computational efficiency; 2) In terms of wake simulation, the LDS SGS model is used to model turbulence, reducing vortex dissipation and further improving the refinement of turbine wake; 3) In terms of vortex identification, the new generation nnew vortex identification method avoids the difficult threshold selection in previous vortex identification and captures more refined vortex structures. The accuracy of the model is validated against published data of a NREL 5 MW wind turbine, and then extended to simulate the wake interference of tandem twin-rotor turbines by changing the pitch angle of the upstream wind turbine (WT1). The influence mechanisms between array wake interference and energy conversion efficiency under the pitch strategy are explored, demonstrating the AL-LDS-finew coupling method is computationally accurate and efficient for simulating the complex wake interference. From analyses, the pitch strategy can effectively suppress the wake effect of the upstream turbine (WT1) and increase the power output of the downstream turbine (WT2), thus improving the overall output power of the array farm. Compared with the non-pitch condition (0 pitch angle), a pitch angle of (2°) maximizes the global energy conversion efficiency of the twin-rotor array: power augmentation by 0.29%, and thrust reduction by 5%. This optimal state reduces the fatigue load of the turbine and is more conducive to long-term operation. The findings, whilst preliminary, encourage the use of turbine pitch strategies in the wind farm planning and operation. [ABSTRACT FROM AUTHOR]

The investigation of aerodynamics and the establishment of flow patterns around finite-length cylinders with various end shapes in a free, boundless air flow with longitudinal and transverse flow over a wide range of geometric and regime parameters is sketchy and does not have a wide range of geometric and regime parameters. This, in turn, affects the entire aerodynamics of the streamlined body. This paper considers the numerical simulation of a wind turbine made of combined blades. CFD (computational fluid dynamics) methods based on the realisable k-ε turbulence model were used in the study. The results on the influence of the position of the fixed blade on the angle of inclination are obtained (0°, 15°, 30°, 45°, and 60°). The authors found that the pressure of a fixed blade at an optimal angle increases the power coefficient Cp by 35–40%. The dependence of the Cp power coefficient on the rotational speed (speed coefficient) for a three-bladed wind turbine was also established, and it was determined that the maximum value of Cp = 0.28 at Z = 4.9. Based on the results obtained, it was determined that the wind turbine has a maximum power coefficient at an angle of inclination of 0 degrees. [ABSTRACT FROM AUTHOR]

This study aims to comprehensively investigate the dynamic characteristics of the tower of a scaled wind turbine model through wind tunnel tests. A model was scaled from the NREL 5 MW prototype wind turbine with a geometric scale ratio of 1/75, based on the similarity rules in thrust coefficient and dynamic characteristics. A series of wind tunnel tests were carried out on the scaled wind turbine model under different operating conditions and parked conditions with different yaw angles, and the modal parameters of the scaled model were identified by the stochastic subspace identification method and rotor stop tests. It was found that the vibration response of the tower in the fore–aft direction achieved its maximum value when the yaw angle was 90° with feathered blades, while the tower vibration response in the side–side direction was relatively severe with the yaw angle ranging from 10° to 50°. These observations are found to be well aligned with the aerodynamic characteristics of the airfoil. Moreover, the experimental results indicate that the scaled wind turbine model can reflect the vibration responses of its full-scale counterpart in the fore–aft direction. The natural frequencies and mode shapes of the scaled model can be accurately identified by different methods, but the identified damping ratios are relatively scattered. [ABSTRACT FROM AUTHOR]

The monitoring of wind turbine (WT) systems allows operators to maximize their performance, consequently minimizing untimely shutdowns and related hazard situations while maximizing their efficiency. Indeed, the rational monitoring of WT ensures the identification of the main sources of risks at a proper time, such as internal or external failures, hence leading to an increase in their prevention by limiting the faults' occurrence regarding the different components of wind turbines, achieving production objectives. In this context, the present paper develops a practical monitoring approach using a numerical fault-detection process for the pitch system based on a benchmark wind turbine (WT) model with the main aim of improving safety and security performance. Therefore, the proposed fault-diagnosis procedure deals with eventual faults occurring in the actuators and sensors of the pitch system. In this proposed approach, a simple, logical process is used to generate the correct residuals as fault information based on the redundancy in the actuators and sensors of the pitch sub-systems. The obtained results demonstrate the effectiveness of this proposed process for ensuring the tasks of the fault diagnosis and condition monitoring of the WT systems, and it can be a promising approach for avoiding major damage in such systems, leading to their operational stability and improved reliability and availability. [ABSTRACT FROM AUTHOR]

Assessing the vibration signature produced by a rotating component of the wind turbine enables the identification of operational conditions and the detection of potential faults at an early stage. The main purpose is to enhance the sustainability of wind turbines while increasing the lifespan and uptime of their operational systems. This vibration analysis is based on the processing of the signal provided by sensors, which often incorporates piezoelectric transducers. This paper evaluates the consequences of employing piezoelectric sensors used for vibration measurement on electrical machines integrated into wind turbines by conducting a life cycle assessment (LCA). The widespread use of piezoelectric materials is due to their high sensitivity to vibrations, although their selection is also influenced by regulatory restrictions. This research focuses on the environmental impact of piezoelectric accelerometers used commonly in condition monitoring systems. The collected literature data on the manufacturing processes are inputted into the LCA model which is powered by the Ecoinvent 3 database. The impact assessment is carried out using the European ILCD 2011 Midpoint+ method by calculating the unique scores of the selected impact categories. The results are presented and discussed in terms of environmental indicators, as well as ecological recommendations on the design. [ABSTRACT FROM AUTHOR]

Wind turbine components' failure prognosis allows wind farm owners to apply predictive maintenance techniques to their fleets. Determining the health status of a turbine's component typically requires verifying many variables that should be monitored simultaneously. The scope of this study is the selection of the more relevant variables and the generation of a health status indicator (Failure Index) to be considered as a decision criterion in Operation and Maintenance activities. The proposed methodology is based on Gaussian Mixture Copula Models (GMCMs) combined with a smoothing method (Cubic spline smoothing) to define a component's health index based on the previous behavior and relationships between the considered variables. The GMCM allows for determining the component's status in a multivariate environment, providing the selected variables' joint probability and obtaining an easy-to-track univariate health status indicator. When the health of a component is degrading, anomalous behavior becomes apparent in certain Supervisory Control and Data Acquisition (SCADA) signals. By monitoring these SCADA signals using this indicator, the proposed anomaly detection method could capture the deviations from the healthy working state. The resulting indicator shows whether any failure is likely to occur in a wind turbine component and would aid in a preventive intervention scheduling. [ABSTRACT FROM AUTHOR]

Birds and bats are prone to collisions with wind turbines. To reduce the number of bat collisions, weather variables are commonly used to shut down wind turbines when a certain constellation of weather variables occurs. Such a general approach might also be interesting to mitigate raptor collisions. Studies on the relationship between flight behaviour and weather variables are needed. To investigate the flight behaviour of raptors within their breeding area in relation to local weather variables, we used high resolution data of flight tracks of Red Kites collected on a wind energy test site (Germany). Birds were tracked with a laser range finder (LRF) or with Global Positioning System (GPS) transmitters. Weather variables were continuously registered on site. We used generalised linear mixed models to analyse the influence of weather variables and of the measurement method on different flight parameters. Furthermore, we investigated the probability of flying within a virtual rotor height range defined by three hub heights (84, 94 and 140 m; diameter: 112 m). The median flight altitude measured by LRF (52.5 m, 95% CI: 44.9–61.0, N = 2511) was on average 25 m higher than the corrected one resulting from GPS (27.8 m, 95% CI: 24.7–31.2, N = 6792). Flight speed also differed between methods (GPS: 29.2 km/h, 95% CI: 28.2–30.3 km/h; LRF: 25.1 km/h, 95% CI: 24.0–26.3 km/h). The effects of the weather variables were weak. Birds tended to fly less and lower during wet (humid, rainy or foggy) than dry weather, and lower during strong than weak winds. Probabilities of flying within a height range of virtual rotors increased with decreasing hub height, and hence ground clearance. Synthesis and applications: Flight behaviour was highly variable. Flights occurred during all weather conditions at different altitudes throughout the day over the entire season. Further research into the relationship between flight behaviour, weather variables, collisions and other factors is needed as a basis for developing shutdown regimes generally suitable for raptors. The mean flight altitude and speed differed between the measurement methods. Any values resulting from studies should be interpreted in the context of the method. [ABSTRACT FROM AUTHOR]

With the development of wind energy, the unit capacity of wind turbine is becoming larger and the height of the support tower is getting higher. Limited by the transport conditions, the diameter of the tubular steel tower cannot further increase with the increase of the tower height, which results in the lower natural frequencies of the tower. The higher and more slender wind turbine towers are more sensitive to the external excitation and many tower collapses have occurred in recent years because of the interplay with the longer rotor blades. A 2 MW wind turbine system with a 120 m full steel tubular tower is taken as an example in this study to research the nonlinear coupling vibration mechanism under the normal operation case and the emergency shut-down case. An integrated model of large-scale wind turbine system including the tower, blades, hub, and nacelle is established by the open-source software FAST to study its nonlinear interaction vibration mechanism. Under the normal operation load case, the acceleration response of the steel wind turbine tower is greatly affected by the rotational frequency of the rotor. The maximum accelerations in the fore-aft and side-side directions both appear around the middle-upper height of the tower. Under the emergency shut-down load case, the maximum fore-aft acceleration response appears at the top of the tower, but the maximum side-side response still appears around the middle-upper height of the tower. The first-order vibration mode plays a dominant role in the top fore-aft acceleration and the second-order vibration mode has great contribution to the side-side acceleration under the emergency shut-down load case. The numerical results of this study can provide insight into the vibration control mechanism of the full steel tower. [ABSTRACT FROM AUTHOR]

Wind turbines installed in regions with high altitudes and low temperatures on winter season, ice can accumulate on the blade surfaces during operating condition. A small amount of ice accretion on the blade surface can result in several crucial issues, such as AEP (annual energy production) reduction due to degrade aerodynamic performance, and safety concerns related to ice falling down. Predicting and elucidating icing accretion phenomena under various weather conditions are significantly essential in the aspect of operating and maintenance. The primary objective of this study was to assess the impact of ice accretion on the aerodynamic characteristics of wind turbine blades CFD (computational fluid dynamics) analysis, including ice thickness and shape, under varying weather conditions. Therefore, this study focused on identifying the aerodynamic characteristics affected by ice accretion, including ice thickness and shape under various weather conditions through Fensap. Through this methodology, the presence of ice accumulation on the blade surfaces was investigated, and its adverse impact on aerodynamic performance has been assessed. [ABSTRACT FROM AUTHOR]

This paper takes the wind turbine of the new energy pumping unit as the study subject, which mainly analyzes realistic wind speed estimation on the windward side of the wind turbine and then proposes a wind turbine speed control method based on wind speed estimation. The support vector regression (SVR) model is built first in this study, and the parameters of the SVR model are optimized by the particle swarm algorithm. The SVR II wind speed prediction model is constructed and the working characteristic curve of the wind turbine is generated by gathering wind speed and output power data from the wind turbine at the assigned test location. The prediction model can compensate for the disturbance of system inertia and make the output wind speed more accurate. The effective wind speed acting on the turbine surface can be obtained by substituting the predicted wind speed into the optimal blade tip velocity ratio equation. Suppose the wind turbine is in maximum power tracking control. In that case, the optimal speed can be given directly by the model SCR III, and the speed can be tracked in real-time by the controller to accomplish the largest wind power capture. The optimal speed tracking control uses the calculated effective wind speed if the wind turbine is under constant power output control. In this paper, 14,000 sets of characteristic samples were extracted within the range of wind speed variation of 4–20 m/s, and model verification was carried out for models SVR-I, SVR-II and SVR-III. Finally, the predicted effective wind speed value and the actual value are within ±0.3 m/s of each other. In practical engineering applications, the forecasted effective wind speed value can entirely substitute the actual value. This work put forward a maximum power tracking control approach that can satisfy new energy pumping units' energy conservation needs. [ABSTRACT FROM AUTHOR]

To improve the reliability and lifespan of wind turbines, this paper takes the two-stage fixed shaft gearbox experimental platform of wind turbines as the research object. Based on Hertz contact theory, the oil film pressure and thickness in the contact area are solved by combining the equations of elastohydrodynamic lubrication and the Newton Raphson method; And the lubrication characteristics of the transmission system were analyzed to verify the correctness of the method; At the same time, in response to partial load phenomenon caused by system coupling deformation, genetic algorithm was selected to modify the gear teeth. The results show that the max unit load on the tooth face and the maximum stress of tooth root decreased by up to 26.48 % and up to 20.35 % respectively after modification which can improve the uneven distribution of oil film and the lubrication performance of the tooth surface. [ABSTRACT FROM AUTHOR]

A calculation method for optimizing the LCOE and the cost of subsequent PTX production from wind power is presented, based on Weibull parameters and cost functions. The PTX products methanol, ammonia and synthetic diesel are considered, as well as hydrogen, which is converted into an easily transportable form by hydrogenation of a liquid organic hydrogen carrier (LOHC). Haru‐Oni in Chile and Tarfaya in Morocco were chosen as examples of production sites with excellent wind conditions. These are compared with three domestic sites with very good to very moderate wind conditions in the order Dornum (North German coast), Senftenberg (Lausitz) and Ludwigshafen (Rhine Graben). [ABSTRACT FROM AUTHOR]

In this study, aluminum-based metal matrix composites (MMCs) were fabricated with varying compositions of SiC and TiO2 reinforcements using the stir casting method. Three different compositions were examined: 98% Al6061 alloy, 1% SiC, and 1% TiO2; 96% Al 6061 alloy, 2% SiC, and 2% TiO2; and 94% Al alloy, 3% SiC, and 3% TiO2. These MMCs were evaluated for their mechanical behavior and microstructure in terms of tensile, flexural, and impact strength. Scanning electron microscopy (SEM) analysis revealed a uniform distribution of reinforcements, while also indicating an uneven dispersion of SiC particles within the Al matrix. Tensile testing displayed an ultimate strength of 160.06 MPa, an elongation of 6.63%, and a hardness of 96.5 for the sample with the composition of 94% Al, 3% SiC, and 3% TiO2. For the composition of 96% Al alloy, 2% SiC, and 2% TiO2, the highest flexural strength of 6.57 kN, notable impact strength of 6 J, and a hardness value of 93.68 were achieved. Atomic force microscopy (AFM) was employed to analyze surface morphology. These results demonstrate the potential of these MMCs for practical applications due to their improved properties compared to commercially available composites. The study highlights the tailored mechanical attributes of metal matrix composites, showcasing their strength, stiffness, fatigue resistance, and corrosion resistance. Such systematic analyses aid in the development of more efficient and dependable structures for wind turbine applications. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive study on optimizing wind farm efficiency by controlling wake effects using the WFSim dynamic simulation model. Focusing on five key factors—yaw wind turbine position, yaw angle, wind farm spacing, longitudinal wind turbine spacing, and yaw rate—we qualitatively analyze their individual and combined impact on the wind farm's wake behavior and mechanical load. Through a quantitative approach using the orthogonal test method, we assess each factor's influence on the farm's overall power output. The findings prioritize the following factors in terms of their effect on power output: yaw wind turbine position, yaw angle, wind farm spacing, longitudinal spacing, and yaw rate. Most significantly, this study identifies optimal working conditions for maximizing the wind farm's average power output. These conditions include a wind turbine longitudinal spacing of 7.0D, a wind farm spacing of 15.0D, a yaw angle of 30°, and a yaw rate of 0.0122 rad/s, with the first and second rows of turbines in a yaw state. Under these optimized conditions, the wind farm's average power output is enhanced to 35.19 MW, marking an increase of 2.86 MW compared to the farm's original configuration. Additionally, this paper offers an analysis of wake deflection under these optimal conditions, providing valuable insights for the design and management of more efficient wind farms. [ABSTRACT FROM AUTHOR]

Wind energy, which generates zero emissions, is an environmentally friendly alternative to conventional electricity generation. For this reason, wind energy is a very popular topic, and there are many studies on this subject. Previous studies have often focused on onshore or offshore installations, lacking comprehensive comparisons and often not accounting for technological advancements and their impact on cost and efficiency. This study addresses these gaps by comparing onshore and offshore wind turbines worldwide in terms of installed capacity, levelized cost of electricity (LCOE), total installed cost (TIC), capacity factor (CF), turbine capacity, hub height, and rotor diameter. Results show that onshore wind power capacity constituted 98.49% in 2010, 97.23% in 2015, and 92.9% in 2022 of the world's total cumulative installed wind power capacity. Offshore wind capacity has increased yearly due to advantages like stronger, more stable winds and easier installation of large turbine components. LCOE for onshore wind farms decreased from 0.1021 USD/kWh in 2010 to 0.0331 USD/kWh in 2021, while offshore LCOE decreased from 0.1879 USD/kWh in 2010 to 0.0752 USD/kWh in 2021. By 2050, wind energy will contribute to 35% of the global electricity production. This study overcomes previous limitations by providing a comprehensive and updated comparison that incorporates recent technological advancements and market trends to better inform future energy policies and investments. [ABSTRACT FROM AUTHOR]