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In this paper, the wake characteristics of Zell 2000 wind turbine under different tip velocity ratios are studied by using the lattice Boltzmann method and large eddy simulation. The adaptive mesh refinement method is performed to capture the fine flow structure and wake characteristics development. In this paper, we mainly focus on the effect of the tip speed ratio on the flow structure and unsteady characteristics of wind turbine wake. The three‐dimensional flow vorticity structures, the section vorticity diagram, the pressure fluctuation of wake and the lift coefficient of wind turbine wake are utilized to explore the effect of the tip speed ratio on the unsteady physics mechanism of wind turbine wake. With the increase of the tip speed ratio, the distance between two adjacent vortex rings along the axial direction gradually decreases, as the position of the broken vortex circles gradually approaches the center of the blade, separated vortexes are rapidly generated, and the coherent structure appears closer to the wind turbine. A relationship is established between the tip speed ratios and the positions of the broken vortex circles. It is further found that the dominant frequency amplitude gradually increases with the increase of tip speed ratio and the pressure amplitude spectra of vortex increases with the decrease of the distance between the wake and the center of the blade axis. The above series of studies can provide significant physical insight into deep understanding the influence of the tip speed ratio on the wake characteristics of wind turbines. [ABSTRACT FROM AUTHOR]

Due to the difficulties of system modeling, nonlinearity effects, uncertainties, and the availability of Wind Turbines (WTs) SCADA system data, data‐driven Fault Detection and Isolation (FDI) methods for WTs have received increasing attention. In this paper, using the wind turbine SCADA data, an effective FDI scheme is proposed using the K‐Nearest Neighbors (KNN) classifier. The operational data set is labeled by the status and warning data sets, and the labeled operational data set, after eliminating invalid data, feature selection, and standardization, is used for training and validation of the FDI model. Data imbalance, which is common in real data sets, does not affect the performance of the proposed method, hence there is no need for data balancing methods in this algorithm and the performance is not deteriorated by occurring false alarms. Therefore, the proposed method has provided impressive performance in FDI compared with previous research on this data set. Also, many of the fault classes addressed in this paper were not considered in previous works on this data set. [ABSTRACT FROM AUTHOR]

The research addresses the pressing need to understand wind conditions for optimal wind energy utilization, a crucial aspect of achieving global energy sustainability. While previous studies have focused on assessing wind energy potential and terrain roughness, gaps persist in understanding how wind and spatial conditions affect blade geometry. Therefore, this study aims to investigate the impact of local wind conditions on wind turbine blade geometry, integrating nonuniform wind inflow due to wind shear into the theoretical model. Through the paper, it explores blade design for varying inflow conditions and analyses the relationship between terrain type and blade designs. A novel approach to wind turbine blade design, utilizing a theoretical model that combines the local momentum theorem with insights from propeller vortex theory was adopted. Through the analysis, the authors demonstrate that variations in terrain significantly influence blade geometry, underscoring the necessity for tailored turbine designs based on local conditions. The findings reveal discrepancies in blade geometry across different turbine capacities and terrain types, offering novel insights into wind turbine performance optimization. By tailoring blade geometry to specific locations enhances resource utilization, offering insights for energy policymakers. The research provides validated methods for optimizing turbine geometric parameters, opening avenues for increased local investment in wind energy and promoting distributed energy development. Illustrated with wind plants examples, the paper concludes by outlining future research directions in this field. [ABSTRACT FROM AUTHOR]

Microgrids with large‐scale photovoltaic systems constitute a large part of distributed renewable generation in many grids around the world. Managing the performance of such microgrids and especially their interaction with the main power grid is a challenging task, because it requires the control of renewable resources. This paper presents a comprehensive overview of DC‐DC converter structures used in microgrids and presents a new classification for converters. This paper also provides an overview of the control techniques of DC‐DC converters in DC microgrids and the advantages and disadvantages of the control methods are discussed. In connection with the increasing penetration of distributed generation sources (DGS) and renewable sources in power systems and their power management has been raised as a major concern and methods for power management have been investigated in this paper. Finally, a DC microgrid system, which includes a solar system, wind turbine, and battery, is simulated in MATLAB/Simulink software and its performance is analyzed. [ABSTRACT FROM AUTHOR]

The wind power generation system of a 5 MW horizontal axis wind turbine has a high wind energy conversion efficiency. The proportion of installed capacity in practical production applications is increasing year on year, so that the stability of its operation becomes a central factor in determining the productivity of the wind farm in question. This paper takes a 5 MW wind turbine as the research object and proposes a parameter‐adaptive robust model predictive control method to achieve self‐optimization of controller parameters through a Bayesian optimization approach. A robust model predictive control strategy, aiming to reduce the power fluctuation while maximizing the power output, is developed in this paper to enhance the dynamic economic performance under uncertain wind speed variation. A Bayesian algorithm is used in this paper to optimize the parameters of the controller. Moreover, wind speeds are simulated using TurbSim for different turbulence intensities of 5%, 10%, and 15% turbulence. Finally, the robust model predictive control toolbox in MATLAB is designed and simulated. The results show that the operational instability of the wind energy system is overcome. Meanwhile, the robustness of the wind energy system operation is improved compared to the traditional model predictive control approach. [ABSTRACT FROM AUTHOR]

This paper reviews state‐of‐the‐art numerical tools for the operation and maintenance (O&M) of offshore wind farms, focusing on decision support models for maintenance scheduling and the consideration of human and environmental uncertainty. In this review, various factors that can influence the successful conduct of maintenance operations will be examined and special attention will be paid to the most significant ones. Data‐driven technologies for improved offshore asset management are also examined and the most used data‐driven methods for modeling and optimizing turbine operation and maintenance are presented. A focus will be placed on the choice of maintenance strategy, which is the basis for the planning of operations and thus the optimization problem discussed. As offshore maintenance is a complex operation whose efficiency and safety depend on human and environmental factors, special attention will be paid to the planning strategy that minimizes the risks involved while maximizing efficiency by considering these factors. The choice of planning technique for turbine maintenance and better consideration of uncertainties are crucial areas of improvement as they can lead to better overall efficiency, higher profit margins, better safety, and improved sustainability of offshore wind farms. The paper covers the application of digital technologies for offshore wind O&M planning and the associated challenges. The paper also highlights the various environmental and human factors to be considered for the operation and maintenance of wind turbines. [ABSTRACT FROM AUTHOR]

Wind power generation does not emit greenhouse gases or pollutants, but there are some carbon emissions from the manufacturing, transportation, operation, and waste disposal of wind turbines. Directly driven permanent magnet and doubly fed asynchronous wind turbines currently have the largest market share in China, but few Chinese studies have compared their differences in carbon reduction potential. This paper uses life cycle assessment (LCA) to quantitatively analyze the full life cycle carbon emissions of the two wind turbines to determine which type of wind turbine has greater carbon reduction potential, obtaining the following results. (1) The full life cycle greenhouse gas emissions of 2.5 MW directly driven permanent magnet and doubly fed asynchronous wind turbines are 8.48 and 10.43 g CO2/kWh, respectively. The direct‐driven permanent magnet wind turbine is superior in terms of carbon reduction. (2) The stage with the greatest impact and the greatest difference between the two wind turbines in the full life cycle is the production stage, during which the carbon emissions of the directly driven permanent magnet and doubly fed asynchronous wind turbines are 1.045 × 106 and 1.210 × 106 kg, respectively. (3) According to sensitivity analysis, proper waste disposal and transportation can reduce carbon emissions from wind turbines. These research findings can be used to help achieve carbon peaking and neutrality goals, as well as the technological development of wind power enterprises. [ABSTRACT FROM AUTHOR]

As a key part of the wind turbines (WTs), the blade has a direct influence on the efficiency of WT. Because the defect detection technology of WT blade is not widely used, and the robustness of traditional detection methods is poor, this paper proposes a multi‐feature fusion residual network combined with transfer learning. In this paper, the WT blade image dataset is enhanced and constructed to train the convolutional network. Two residual structures of multi‐feature fusion (two feature fusion and three feature fusion) are proposed and compared. At the same time, transfer learning is used to improve training process and accelerate convergence. Compared with several convolutional neural networks based on indices include training loss and testing accuracy, f1‐score and confusion matrix, the method proposed greatly reduces the time while achieving accurate detection. [ABSTRACT FROM AUTHOR]

The importance of flexible alternating current transmission system (FACTS) devices is increasing day by day as they provide compensation for power systems. The installation of such devices solves many issues regarding the stability and power transferability of power systems. However, for optimizing the performance of FACTS devices, the location and sizes of FACTS devices must be selected very carefully. On the other side, the utilization of optimization techniques is increasing daily, as it is capable of solving nonlinear, random problems by its stochastic nature. As a result, it became a reliable tactic for solving stochastic problems almost in every aspect of our lives. Therefore, it is adopted for solving power system problems, like, FACTS devices allocation, distributed generators allocation, determining component's parameters, and more. In this paper, the optimal size and location of several FACTS devices are selected to achieve the following single objective functions separately: fuel cost minimization and power losses minimization, then combining the mentioned objective function into one multiobjective function for gross cost minimization. The power system optimized is the Institute of Electrical and Electronics Engineers 30‐bus standard system, and the FACTS devices installed are static VAR compensator, thyristor control series compensator, and thyristor‐controlled phase shifter, while the optimization is performed using different conventional and recent optimization algorithms, including a newly developed algorithm, typically Leader Walrus Optimization Algorithm "LWaOA," making a comparison among such algorithms to investigate the algorithm that possesses the best performance. The results of the optimization processes show the superiority of LWaOA. [ABSTRACT FROM AUTHOR]

Wind turbine bearings play a crucial role in ensuring the safe and efficient operation of wind turbines. Accurate estimation of the remaining useful life (RUL) of bearings can significantly reduce operating and maintenance costs. In this paper, we propose three advanced data‐driven models to predict the RUL of high‐speed shaft bearings in wind turbines. These models combine the sparrow search algorithm (SSA) with three different regression models, namely support vector machine, random forest (RF) regression and Gaussian process regression. The models are based on features extracted from the vibration signal analysis, and the features are selected based on their monotonicity to evaluate bearing degradation. To optimize the performance of the regression models, all model parameters are tuned using the SSA algorithm. The proposed models are validated using vibration data collected from a real 2 MW commercial wind turbine. Our results demonstrate that the proposed models are effective in predicting the RUL of wind turbine bearings, and the SSA algorithm improves the accuracy of the predictions. [ABSTRACT FROM AUTHOR]

The flexible system of wind turbines refers to the components such as blades, towers, and rotor shafts that are subjected to external forces such as wind loads, inertial forces, and gravity during operation, resulting in deformation and vibration. This paper proposes that dynamic response analysis of the flexible system, through the response data, put forward improvement measures to improve the stability. The flexible dynamic response of wind turbine was analyzed. The fluid dynamics and structural dynamics of wind turbine are analyzed by the finite element method, and the flow chart is combined to get the wind turbine velocity, pressure, shear stress, and vorticity distribution nephogram. The results provide a reference value for monitoring structural state dynamics parameters of large wind turbines. Wind power generation technology is relatively mature, and its proportion in the field of power generation is gradually increasing. Wind energy is inexhaustible and can occupy a place in the development and utilization of new energy for a long time. This study provides an important reference for determining the dynamic parameters of wind turbine operation and improves the stability and reliability of wind turbine operation. The results provide a reference value for monitoring structural state dynamics parameters of large wind turbines. [ABSTRACT FROM AUTHOR]

Due to the random fluctuation of wind energy, the working conditions of the drivetrain have complex time‐varying characteristics. To deeply understand time‐varying dynamic behavior, a dynamic behavior method for normal and faulty drivetrain under multiple operating status is proposed. First, the working condition is divided into multiple working conditions by combining the control principle of the wind turbine with the K‐means clustering algorithm. Second, the torsional dynamics model of the drivetrain is established, solved, and verified. Finally, the dynamic behavior of normal and faulty drivetrain under multiple working conditions is analyzed. The dynamic behavior of drivetrain in normal and faulty states under different working conditions can be determined by the method proposed in this paper, which is affected by the system itself, external load, and the control strategy of the wind turbine. At the same time, the fault‐sensitive working condition range and the fault feature index under each working condition are determined. This research can provide an important theoretical basis for variable condition fault diagnosis. [ABSTRACT FROM AUTHOR]

Wind energy maintenance and operation costs can total millions of dollars each year in an average industrial‐size wind park. Therefore, moving from preventive and corrective maintenance to predictive maintenance is imperative in the wind energy sector. This paper contributes to this challenge by providing a main bearing early damage detection technique that exclusively uses standard supervisory control and data acquisition (SCADA) data (10‐min average) and a convolutional autoencoder with the following contributions. (i) Entirely semisupervised (not requiring the labeling of data through work order logs and avoiding the problem of data imbalance between classes) based only on healthy data, thus expanding its range of application (even when the failure of interest has never occurred in the park before). (ii) Validated using real‐world SCADA data and shown to be resistant to seasonality, and operational and environmental conditions. (iii) Reliable predictions with minimum false alarms thanks to specially designed fault prognosis indicators based on the image mean square error metric. (vi) The early warning is achieved months in advance, thus providing adequate time for plant operators to plan properly. (v) The main use of exogenous variables in the model (variables that are not affected by other variables, e.g., wind speed, wind turbulence, and ambient temperature) guarantees the detection of damage directly related only to the low‐speed shaft temperature (the only nonexogenous variable used by the stated model). (vi) Finally, the proposed strategy is validated in a wind park made up of 12 wind turbines. [ABSTRACT FROM AUTHOR]

Due to the inherent intermittency in wind power production, reliable short‐term wind power production forecasting has become essential for the efficient grid and market integration of wind energy. The current wind power production forecasting schemes are predominantly developed for wind farms. With the rapid growth in the microgrid sector and the increasing number of wind turbines integrated with these local grids, power production forecasting schemes are becoming essential for distributed wind energy systems as well. This paper proposes a power production forecasting scheme developed explicitly for distributed wind energy projects. The proposed system integrates two submodels based on support vector regression: one for downscaling the wind speed predictions to the hub coordinates of the turbine and the other for predicting the site‐specific performance of the turbine under this wind condition. The forecasting horizons considered are the hour ahead (t + 1) and the day ahead (t + 36), which align with the Nord pool's energy market requirements. For the day‐ahead scheme, a multistep recursive approach is adopted. The accuracy and adaptability of the proposed forecasting scheme are demonstrated in the case of a distributed wind turbine. [ABSTRACT FROM AUTHOR]

The coaxial magnetic gear is a new type of noncontact transmission mechanism, which avoids the inherent defects of the mechanical gearbox due to gear meshing transmission. To study a coaxial magnetic gear (CMG) with high torque density and low torque ripple to replace the mechanical gear in the semidirect drive wind turbine, a novel umbrella shaping inner rotor CMG (US‐CMG) with an auxiliary modulation ring is proposed in this paper. The novel structure adopts a CMG with a dual modulated ring to increase the electromagnetic torque. In addition, the magnetic block of the poled inner rotor is divided into three pieces magnetized by Halbach arrays, and the middle piece is umbrella‐shaped to reduce torque ripple. Based on the topology of the US‐CMG and the principle of the magnetic field modulation, the operation mechanism of the US‐CMG is analyzed. A contrastive analysis among the US‐CMG, Halbach magnetized dual‐modulator CMG (HM‐CMG), and surface‐mounted CMG (SM‐CMG) is established to identify the performance of the US‐CMG. Compared with the SM‐CMG, the results show that the electromagnetic torque of the US‐CMG has increased by 65.46%, and the torque ripple of the US‐CMG is 57.6% of that of the SM‐CMG. [ABSTRACT FROM AUTHOR]

Ice accretion on wind turbine blades is one of the major faults affecting the operational safety and power generation efficiency of wind turbines. Current icing detection methods are based on either meteorological observing system or extra condition monitoring system. Compared with current methods, icing detection using the intrinsic supervisory control and data acquisition (SCADA) data of wind turbines has plenty of potential advantages, such as low cost, high stability, and early icing detection ability. However, there have not been deep investigations in this field at present. In this paper, a novel intelligent wind turbine blade icing detection method based on the wind turbine SCADA data is proposed. This method consists of three processes: SCADA data preprocessing, automatic feature extraction, and ensemble icing detection model construction. Specifically, deep autoencoders network is employed to learn multilevel fault features from the complex SCADA data adaptively. And the ensemble technique is utilized to make full use of all the extracted features from different hidden layers of the deep autoencoders network to build the ensemble icing detection model. The effectiveness of the proposed method is validated using the data collected from actual wind farms. The experimental results reveal that the proposed method is able to not only adaptively extract valuable fault features from the complex SCADA data, but also obtains higher detection accuracy and generalization capability compared with conventional machine learning models and individual deep learning model. [ABSTRACT FROM AUTHOR]

This paper explores the wake of a wind turbine yaw and its influence on the output power of the downstream wind turbine. A wind turbine model (hub height and diameter of 1.2 m and 1.0 m, respectively) was used in the experiments, and the yaw angle of the upstream wind turbine was successively varied through values of 0°, 15°, and 30°. The wind speed and the intensity of turbulence in the wake region were measured using a hot‐wire anemometer. The measuring positions were changed to 3, 5, and 7 rotor diameters behind the upstream wind turbine. In addition, the output power of the downstream wind turbine model was measured using the rotational speed torque meter. The results show that, in the case of large‐angle yaw of the upstream wind turbine, the recovery of the wind speed in the wake area was greatly improved, and up to 30% recovery was achieved. The power output of the downstream wind turbine was also significantly increased. Under the yaw conditions of 15° and 30° for the upstream wind turbine, the average output power of the downstream wind turbine increased by 17.6% and 21.6%, respectively. [ABSTRACT FROM AUTHOR]

The most common approach to solving blade element momentum (BEM) equations is through fixed point method. The fixed point method can provide reliable solutions with high precision, yet the robustness of the method has been challenged when infrequent failures of converging to a physical solution are found for some design space. Though the lack of robustness is alleviated by applying two improved algorithms, their shortcomings should not be of an understatement. Ning's method can result in a converged yet nonphysical solution, and Sun's method decreases the computational efficiency remarkably. To overcome these setbacks, a new algorithm has been proposed in this paper. A clear classification of a wind turbine operating states has been given first to correct the thrust relation for a > 1, followed by discussions of three failure cases encountered during solving BEM equations. Then, the new algorithm with three major modifications has been introduced and explained. The test of Section 4 reveals that the decreasing rf technique has a positive effect on improving the robustness. Besides, the first two tests in Section 5 prove that the new thrust equation can greatly enhance the robustness, and Aitken's squared process can significantly strengthen the efficiency. The results show that all three modifications contribute to offering a new FPA with high robustness and satisfactory computing efficiency, which serves as the best option for solving the BEM equations. [ABSTRACT FROM AUTHOR]

To consider the failure correlation among key subsystems, based on the reliability allocation method of the series system, a wind turbine reliability allocation calculation method based on the vine copula correlation model is proposed. The reliability modeling method of the series system is adopted to model the reliability of each subsystem, combined with the copula function to model the correlation between the variables, and then, the multidimensional variable problem is converted into the two‐dimensional variable problem through the vine copula function. The reliability calculation model of the copula function model, under the influence of factors such as the complexity, importance, and failure hazard of each subsystem, allocates the reliability of the key component systems of the wind turbine generator set. Finally, through three allocation methods based on the independent, multivariate copula function and vine copula function and other distribution methods, the calculation and analysis of the wind turbine generator set are performed. The results show that the allocated control system has the lowest reliability, and the tower has the highest reliability; compared with the assumption of the independent allocation method, the allocation result of this paper's method is lower; compared with the allocation method of the multidimensional copula function, the allocated failure rate is increased by more than 20%. This paper verifies that the proposed method is not only effective and reasonable but also more consistent with the actual situation. [ABSTRACT FROM AUTHOR]

The need for energy independence of small remoted islands, promote the exploration of renewable energy sources. Greek islands have rich wind potential and subsequently, the attention is being drawn to the use of wind turbines to meet their energy needs. However, the optimal location of wind turbines installation is the basis for the exploitation of high wind potential in combination with the current legislation framework. This paper presents a combined use of Multi‐Criteria Decision‐Making (MCDM) and Geographic Information Systems (GIS) for the evaluation of eligible sites for wind turbine installation in North Aegean island, in Greece. Criteria weights are estimated for three scenarios using the Analytic Hierarchy Process (AHP) for the illustration of the impact on the final design. The minimum distance from the main settlement of the island constitutes an extra constraint for the investigation of the capability of installing wind farms with the lowest possible energy losses due to power transmission via cables. In all scenarios, the prohibited zone corresponds to 27.9% of the total area of the island. Also, sites with high wind potential can be combined with lower altitudes and can give even more suitable locations for installation, reducing, thus, the installation cost. Economic‐oriented scenario appears to combine both low cost and larger surface area for installation. A sensitivity analysis is performed, for determining the impact of the criteria on the suitability of a site. It seems that new suitability maps are affected greater by the type of the chosen criterion rather than the weight. [ABSTRACT FROM AUTHOR]

In traditional opportunistic group replacement maintenance (OGRM) strategy for wind turbines, the reliability thresholds of different subassemblies are set the same and its two‐step optimization for maintenance cost is diseconomy. In this paper, an improved OGRM strategy for wind turbines is proposed. A uniform formulation on maintenance cost concerning different maintenance activities is presented. On the basis of this, an integrated objective function minimizing the maintenance cost is established, and the reliability thresholds of each subassembly of wind turbines are individually severed as the optimization variables of the objective function. The coordinate rotation method is utilized for solving this optimization problem. A maintenance case based on the proposed strategy from a wind farm in North China is implemented, which verifies its superior performance than the traditional opportunistic group maintenance strategy. [ABSTRACT FROM AUTHOR]

In this paper, a novel fuzzy Proportion Integration Differentiation (PID) controller is proposed to extract the optimal energy from wind for a hydraulic offshore wind turbine. Firstly, in order to calculate the theoretical power, an adaptive neuro‐fuzzy inference system (ANFIS) is designed for wind speed evaluation, in which the input parameters include rotor speed, rotor torque, and pitch angle, and the output parameter is the estimated wind speed. Then, the optimal power coefficient is achieved by controlling the hydraulic pump torque to tracking the optimal rotor speed, and a fuzzy PID is designed to adjust the pump displacement and to obtain the optimal hydraulic pump torque. Furthermore, aiming to meet the various output power demand, a hydraulic accumulator is utilized for storing the excess energy, and a PID controller is designed for distributing the hydraulic energy between the accumulator and the Pelton turbine. Finally, the performance of proposed controls system are confirmed by two case simulations on a 5 MW hydraulic offshore wind turbine. The simulation results show that, compared with the conventional control methods (fuzzy logical and PID controller) used in electro‐hydraulic servo system, the fuzzy PID controller can provide the better tracking effect with a faster speed and a minimal steady‐state oscillation. Moreover, with the use of a hydraulic accumulator, all the power fluctuations from the wind speed disturbances are damped out and the output power become controllable. [ABSTRACT FROM AUTHOR]

New and comprehensive time‐accurate, experimental data from an H‐Darrieus wind turbine are presented to further develop our understanding of the performance of these turbines with a particular focus on self‐starting. The impact of turbine solidity, blade profile, surface roughness, pitch angle, and aspect ratio on the turbine's performance is investigated, parameters that are thought to be critical for small‐scale VAWT operation, particularly when operating in the built environment. It is demonstrated clearly that high turbine solidity (σ≥0.81) is beneficial for turbine self‐starting and that the selection of a thick, symmetrical aerofoil set at a low, negative pitch angle (β≥-2∘) is better than a cambered foil. Increased blade surface roughness is also shown to improve a turbine's self‐starting capability at low tip speed ratios and with high turbine solidity and the associated flow physics are discussed. Finally, it was confirmed that blade span has a significant impact on turbine starting. This paper contributes to the understanding of the turbine characteristics during the starting period and provides clear guidance and validation cases for future design and research in order to promote and justify the wider application of this wind turbine configuration. [ABSTRACT FROM AUTHOR]

Similarities and differences between the features related to the productivity of onshore and offshore wind energy are developed with the aid of information theory techniques complemented by normal statistics. The data comes from the 13‐year period between 2010 and 2022 for the registered turbines in Germany (practically all). The information content of the generated power is dynamically measured by the mutability of the files storing the information. Monthly statistics show that in spite of the Summer months being relatively unproductive, the corresponding mutability shows the possibility of making use of short periods of intermediate productivity in the case of offshore plants. Favorable conditions for wind energy generation in Wintertime are reached for both onshore and offshore production, although the latter is favored. More homogeneity and stability in the data are still necessary to generalize algorithms, protocols, and criteria. This general study shows the success of the information theory techniques in describing wind ramps. Applications to specific zones could improve the efficiency and capacity factor of particular wind turbines depending on their exposure to wind streams or the blocking of nearby mountains and forestry. The information theory techniques presented here allow for a different and novel viewpoint to detect favorable and unfavorable wind energy periods. [ABSTRACT FROM AUTHOR]

The attractive specifications of vertical axis wind turbine (VAWT) are its operating with low noise and wind in various directions. To achieve a higher performance of these turbines, modeling of a curved‐blade VAWT by the modified double multiple streamtube (DMST) method and optimizing this wind turbine are performed. The power coefficient and the total normal force acting on blades were selected as two objective functions. Minimizing the normal force acting on turbine blades as the second objective function was not observed in the open literature. The amount of normal force is crucial in the structural design of VAWT because it generates both the bending moment on blades and forces on supporting arms of VAWT. Another innovation of this study is to consider the shape coefficient parameter that determines the shape of the rotor geometry as a design variable in optimization procedure. A 3 m height curved‐blade VAWT type was optimized by this multi‐objective optimization technique. The optimum values of the design variables obtained by the Genetic Algorithm were selecting an elliptical rotor geometry, pitch angle −1.4°, diameter/height 1.3, blade aspect ratio 19.8, tip speed ratio 4.3 and selecting three blades which could provide a power coefficient of 0.49 and a normal force of 158.7 N. [ABSTRACT FROM AUTHOR]

An ensemble K‐nearest neighbor model based on the double‐layer sampling method was proposed for the condition monitoring of wind turbine (WT) gearboxes. The distance function was improved to affinity distance, which helped overcome the limitation of obtaining local optimums. The feature priority was calculated based on the regularized mutual information, and a double‐layer sampling method combining data and feature sampling was designed. On the basis of the statistical process control technology, the warning threshold was designed and the real‐time residual was analyzed. The condition of the gearbox was monitored by combining the failure rate. Experimental results with real supervisory control and data acquisition (SCADA) data demonstrated that the proposed method improved the estimation accuracy of the model and could realize early fault warnings of a WT gearbox approximately 20 days earlier than the SCADA system. [ABSTRACT FROM AUTHOR]

The drivetrain has an important impact on the stability and reliability of wind turbines operating in complex conditions, which belongs to the electromechanical coupling system. This work studies the two domains of research on drivetrain vibration analysis and control method in detail. The electromechanically coupled torsional vibration model is first established by considering the air gap magnetic field energy of the PMSG. Preliminary analysis in terms of Hamiltonian energy indicates that there is sufficient energy to sustain the exciting oscillation behaviors. Moreover, to reduce the vibration amplitude caused by wind speed or torsional stiffness, a novel adaptive fixed‐time control method is proposed to solve the issue of wind turbine drivetrain chaotic oscillation with uncertainties and disturbances. Furthermore, the proposed method not only can prevent the jumping and bifurcation of torsional vibration from happening, but also there are the advantages of the control scheme with high accuracy, fast convergence rate and strong robustness. For the fair comparison, three different control methods have been used to demonstrate the superiority of the control scheme by the simulation results. The research results can provide a theoretical basis for the parameter design and control of wind turbine drivetrain. [ABSTRACT FROM AUTHOR]

Nanqi Yuan, Wenli Yang, Byeong Kang, Shuxiang Xu, Xiaolin Wang, Chengjiang Li. Manifold learning‐based fuzzy k‐principal curve similarity evaluation for wind turbine condition monitoring (https://doi.org/10.1002/ese3.247). The above article published online on 17 October 2018 in Wiley Online Library (wileyonlinelibrary.com) has been retracted by agreement between all authors, the journal Editor‐in‐Chief, Professor Yun Hang Hu, and John Wiley and Sons, Ltd. The retraction has been agreed due to author Nanqi Yuan's admittance of submitting the article without the knowledge and agreement of the co‐authors and due to evidence indicating the peer review of the paper was compromised. [ABSTRACT FROM AUTHOR]

Green renewable energy is produced by floating offshore wind turbines (FOWT), a crucial component of the modern offshore wind energy industry. It is a safety concern to accurately evaluate excessive weights while the FOWT operates in adverse weather conditions. Under certain water conditions, dangerous structural bending moments may result in operational concerns. Using commercial FAST software, the study's hydrodynamic ambient wave loads were calculated and converted into FOWT structural loads. This article suggests a Monte Carlo‐based engineering technique that, depending on simulations or observations, is computationally effective for predicting extreme statistics of either the load or the response process. The innovative deconvolution technique has been thoroughly explained. The suggested approach effectively uses the entire set of data to produce a clear but accurate estimate for severe response values and fatigue life. In this study, estimated extreme values obtained using a novel deconvolution approach were compared to identical values produced using the modified Weibull technique. It is expected that the enhanced new de‐convolution methodology may offer a dependable and correct forecast of severe structural loads based on the overall performance of the advised de‐convolution approach due to environmental wave loading. [ABSTRACT FROM AUTHOR]

To solve the problem of large output power fluctuations in wind turbines and improve grid adaptability, a hydraulic energy storage system is introduced in traditional hydraulic wind turbines. Based on the working principle of energy storage hydraulic wind turbines, an energy storage hydraulic wind turbine state space model is established, and the feedback linearization method is introduced to solve the multiplication nonlinear problem in the modeling process. The output power is taken as the control output, and the torque compensation controller is established with the feedback linearization method. The displacement of the variable displacement pump motor is controlled to realize hydraulic energy storage system energy charging and discharging, and the wind turbine output power smoothing control is realized with the fluctuating wind speed. The power smoothing control strategy is verified with the 24 kW energy storage hydraulic wind turbines semi‐physical simulation experimental platform. The proposed control strategy lays the groundwork for the wide application of the energy storage hydraulic wind turbines. [ABSTRACT FROM AUTHOR]

The development of wireless sensor network based on breeze power generation is of great significance to promote the development of the intelligent mine. According to the underground environment of coalmine, a small horizontal axis wind turbine with mine air duct was designed. The aerodynamic performance of the wind turbine was analyzed by constructing the three‐dimensional solid model of the wind turbine. The results show that under the condition of underground low wind speed. Outer basin: the air flow entering the air duct is accelerated and rectified, and the speed increases near the impeller. The pressure value at the front end of the blade is the largest and the pressure value at the rear end of the blade is the smallest. A certain range of negative pressure zone is formed at the rear end of the impeller, which is conducive to the operation of the wind turbine. After passing through the impeller, the speed of the air flow decreases and is the lowest near the impeller rotating shaft. Inner basin: the air flow speed gradually increases along the radial direction of the impeller center. The pressure at the front end of the blade is greater than the atmospheric pressure, which is conducive to facilitating the operation of the wind turbine. When the wind speed reaches the rated wind speed of the wind turbine, the power, torque, and output power of the wind turbine increase, which can provide effective power generation. Compared with other airfoils, NACA5505 airfoil has higher lift drag ratio and thrust coefficient. Compared with the wind turbine without air duct, the wind turbine with air duct has higher wind energy conversion and output power under the condition of low Reynolds number, which verifies the feasibility of the wind turbine with air duct. [ABSTRACT FROM AUTHOR]

With millions of years of evolution, owls have developed many excellent characteristics in terms of their flight. The speed of an owl in flight is similar to the relative speed of the blade of a small wind turbine with respect to air. Therefore, the owl wing airfoil is selected as the design airfoil of the wind turbine blade to reduce the flow separation under low Reynolds number. In this study, we analyze an owl wing‐section airfoil and the non‐smooth leading‐edge shape of an owl's wing, and implement an orthogonal optimum design to optimize the wavelength and amplitude of the non‐smooth leading edge. We extract the cross‐sectional features of the airfoil and the non‐smooth leading‐edge shape of the wing. Based on the orthogonal optimum design results, we determine the optimal combination of the wavelength, amplitude, and airfoil, and then design a horizontal‐axis wind turbine blade through bionic coupling. The flow field at different tip speed ratios (TSRs) is simulated using the k‐ωSST turbulence model at the rated wind speed. The results show that the power coefficient (CP) of the bionic wind turbine at a high tip speed ratio is 17.7% higher than that of the standard type. Furthermore, we analyze the operation of the turbine at TSRs of 2 and 5. At a high TSR, the leading edge bulge of the bionic wind turbine blade can change the flow direction distribution of the airflow on the blade surface, make the airflow to adhere to the suction surface, and then reduce the stall area on the suction surface of the blade. Thus, the wind turbine produces higher torque, thereby generating higher power. [ABSTRACT FROM AUTHOR]

The expenses of a wind turbine modeling in particular a wind farm modeling either numerically or experimentally cause to take the advantages of actuator disks (AD). Porous disks (PD) are used to simulate actuator disks in experiments especially for wake studies. A rotor of a wind turbine replaced by a PD can be modeled efficiently with less cost and process time. For a proper PD selection, some semi‐empirical equations are suggested for a range of solidity of 0.2‐0.6. These equations came from a number of tests. The PDs with different dimensions and solidity values were considered for the tests and models. The results of 2D and 3D numerical simulations and the experimental results show that at the solidity of 0.5, the coefficient of performance of a PD reaches approximately 16/27 which is equal to the Betz's limit; the relation between the coefficient of power and the solidity is fitted by a parabolic equation. The average error of the proposed equations for the power coefficient is reported 1.59%. Finally, based on the current results, some semi‐empirical equations are suggested to help the initial PD selection, and then, the cost of further studies may decrease. [ABSTRACT FROM AUTHOR]

In this study, authors present a new hybrid nuclear small modular reactor system assisted with wind energy for net zero emissions trigeneration system. Small modular reactors bring multiple advantages including (a) improved thermal efficiency, (b) better building efficiency due to modularity, and (c) less operation and maintenance costs compared to standard nuclear power generation. Furthermore, the greenhouse gas emissions from small modular reactors are lower than regular counterparts. This study hybridizes small modular reactors with wind turbines for producing three useful commodities, namely electricity, hydrogen, and hot water. A two‐step high‐temperature thermochemical cycle (based on hydrogen chloride gas) is used for hydrogen production, and its performance in terms of energy and exergy efficiencies is evaluated. Additionally, the exergy and energy analyses (by writing balance equations for each component of the system) are carried out to determine the thermodynamic feasibility of the proposed system. In order to observe the effects of various parameters such as the temperature of the thermochemical cycle steps, inlet gas turbine temperature, the pressure ratio of the gas turbine, actual wind speed, and current density on the system performance, a detailed parametric study is conducted. The results of this study show that the overall system can achieve an energy efficiency of about 57.5% and exergy efficiency of about 38.1%. [ABSTRACT FROM AUTHOR]

With the development of urbanization and the application of renewable energy, wind turbine is becoming an important approach for wind energy reservation and utilization. This study provides a numerical investigation on understanding the surface pressure distribution, flow characteristics and dynamic responses of a parked straight‐bladed vertical axis wind turbine (VAWT), which is helpful for its design. Together with the two‐way coupling method between simulation platforms such as STAR‐CCM+ and ABAQUS, the SST k‐ω turbulence model is used to obtain the surface pressure and surrounding flow of the VAWT, and the finite element method is used to obtain the dynamic responses of its structural components. The results show that the contours of the pressure distribution on the windward surface of the VAWT are similar even under a few different conditions, and the deformation of the VAWT can lead to changes in surface pressure; the turbulent flow characteristics and the wake effect become more obvious as the wind velocity increases; the blades and support arms of the VAWT need to be reinforced during the design, and the effect of the parked condition on the dynamic responses of the VAWT can be neglected. The two‐way coupling method as well as the numerical simulation results is expected to provide references for the design of VAWTs subjected to coming wind action. [ABSTRACT FROM AUTHOR]

The structure of an improved wind turbine gearbox is presented for meeting the operation of the optimized wind turbine power‐wind speed curve (P‐v curve). When the wind speed is lower than the cut‐in wind speed, the operation mode of the wind turbine is changed by the extra power, which is supplied by the motor excited source to keep the wind turbine running. Moreover, the transmission principle of the improved wind turbine gearbox is discussed. Various motor power impacts on the transmission characteristic for the improved transmission structure are investigated and results are compared with the professional software. Results indicate that as the motor power increases, the transverse vibration of sun gears and meshing forces of the low‐speed and medium‐speed planetary stages decreases. The transverse vibration for the pinion gear of the high‐speed stage enhances with the increase of the motor power. Load‐sharing coefficients of the planetary gear stages are augmented with the enlargement of the motor power. It is found that meshing forces of the torque‐implement parallel stage are increased with augmentation of the motor power. [ABSTRACT FROM AUTHOR]

This study investigates power generation capacity of stator‐augmented PowerWindow, a linear cascade wind turbine, when installed in through‐building openings of a tall building. By employing a new approach, referred to as equivalent momentum sink method, the flow characteristics of the ducted flow, such as its pressure, velocity, and turbulence intensity are predicted when subjected to different wind directions in presence of a wind turbine. This study shows that a properly designed layout maintains the velocity in the through‐building openings for a wide range of wind directions and enhances the power generation by 50%‐80% in comparison with the free‐stream wind turbine installed at the same elevation. This study also compares the power generation of stator‐augmented PowerWindow with a conventional horizontal axis wind turbine, Ampair 300, installed in the same through‐building openings. The results show that the power generation of the ducted stator‐augmented PowerWindow is close to that of the ducted Ampair 300 in certain wind directions. However, it can also effectively generate power at those wind directions that the ducted Ampair 300 is unable to operate. The analysis shows that this advantage significantly increases the annual power generation probability of the building‐integrated stator‐augmented PowerWindow. As a case study, it is shown that by embedding four through‐building openings integrated with stator‐augmented PowerWindow in a tall building in Sydney area, a portion (0.55‐8.07 KW) of the electricity consumption of the building facilities can be supplied 72% of times. Employing a new approach, referred to as equivalent momentum sink (EMS) method, this study predicts the flow characteristics of the ducted flow in presence of a wind turbine when the duct is subjected to different wind speeds and directions. Using the EMS method, power generation capacity of a linear cascade wind turbine (LCWT), referred to as stator‐augmented PowerWindow is estimated when installed in a through‐building opening and the building is subjected to a realistic wind condition. It is shown that the LCWT can generate 0.55 to 8.07 KW power 72% of the times throughout the year. [ABSTRACT FROM AUTHOR]