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This paper presents a comprehensive review of the most recent papers and research trends in the fields of wind energy and artificial intelligence. Our study aims to guide future research by identifying the potential application and research areas of artificial intelligence and machine learning techniques in the wind energy sector and the knowledge gaps in this field. Artificial intelligence techniques offer significant benefits and advantages in many sub‐areas, such as increasing the efficiency of wind energy facilities, estimating energy production, optimizing operation and maintenance, providing security and control, data analysis, and management. Our research focuses on studies indexed in the Web of Science library on wind energy between 2000 and 2023 using sub‐branches of artificial intelligence techniques such as artificial neural networks, other machine learning methods, data mining, fuzzy logic, meta‐heuristics, and statistical methods. In this way, current methods and techniques in the literature are examined to produce more efficient, sustainable, and reliable wind energy, and the findings are discussed for future studies. This comprehensive evaluation is designed to be helpful to academics and specialists interested in acquiring a current and broad perspective on the types of uses of artificial intelligence in wind energy and seeking what research subjects are needed in this field. [ABSTRACT FROM AUTHOR]

The wind turbine fault feature has strong uncertainty due to various factors such as wind speed and equipment parameters. Based on the analysis of the vibration characteristics of wind turbines with unknown prior conditions, this paper conducts research on the enhancement method of fault features. First, analyzing the change in fault features caused by the change in operating conditions to lay the foundation for subsequent research; Second, for the uncertainty of wind turbine fault feature frequency, searching for fault features by constructing the energy in each spectral interval, then use signal noise optimal resonance to transfer noise energy to low frequency fault features to achieve the purpose of adaptive optimal resonance enhancement of fault feature; Finally, validation of the proposed method by simulation experiments and fault signals of actual wind turbine rolling bearings. The case shows that the proposed algorithm can effectively realize the uncertain fault feature of wind turbines search and enhancement. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

The implementation of renewable energy strategies has been on the rise due to recent global initiatives on sustainable development. In this work, meteorological data obtained from geographically separated stations in Nigeria is utilized to assess the economic benefits of off‐grid renewable energy projects specifically WT/battery, PV/battery, and PV/WT/battery systems for upcoming projects in six different sites, with anticipated start dates in 2023, 2026, and 2029. By utilizing future forecasts of capital costs, this analysis aims to facilitate the decision‐making process regarding the optimal timing and placement of these installations. The WT/battery systems have a levelised cost of electricity (LCOE) that is 20–35 times more in comparison to both PV/WT/battery and PV/battery systems. In 2023, the LCOE for photovoltaic (PV)/battery systems varies from $0.16/kWh in Yola to $0.169/kWh in Port Harcourt. On the other hand, PV/WT/battery systems have the lowest LCOE, ranging from $0.261/kWh in Jos to $0.319/kWh in Port Harcourt. The analysis additionally demonstrated that in the event of project delays, PV/battery systems are projected to exhibit the lowest LCOE across all locations by 2029. This is attributed to the anticipated accelerated reduction in PV and battery expenses compared to the decline in costs associated with WTs. In the specified year, Yola exhibited the most favorable LCOE at $0.0748 per kilowatt‐hour, while Port Harcourt recorded the highest LCOE at $0.113 per kilowatt‐hour. It has been discovered that solar energy exhibits more cost‐effectiveness compared to wind energy, mostly attributable to its superior ability to match generation with load on an hourly and monthly basis, particularly in the presence of heavy loads. This study provides a comprehensive geographical overview that will assist policymakers in the strategic selection of cities in Nigeria for the deployment of off‐grid renewable energy (RE) systems. [ABSTRACT FROM AUTHOR]

The power performance and the wind velocity field of an onshore wind farm are predicted with machine learning models and the pseudo‐2D RANS model, then assessed against SCADA data. The wind farm under investigation is one of the sites involved with the American WAKE experimeNt (AWAKEN). The performed simulations enable predictions of the power capture at the farm and turbine levels while providing insights into the effects on power capture associated with wake interactions that operating upstream turbines induce, as well as the variability caused by atmospheric stability. The machine learning models show improved accuracy compared to the pseudo‐2D RANS model in the predictions of turbine power capture and farm power capture with roughly half the normalized error. The machine learning models also entail lower computational costs upon training. Further, the machine learning models provide predictions of the wind turbulence intensity at the turbine level for different wind and atmospheric conditions with very good accuracy, which is difficult to achieve through RANS modeling. Additionally, farm‐to‐farm interactions are noted, with adverse impacts on power predictions from both models. [ABSTRACT FROM AUTHOR]

This manuscript proposed a hybrid system for enhancing the control strategy of a unified power quality conditioner connected to a wind energy conversion system. The proposed method is combined with honey badger algorithm (HBA) and reptile search algorithm (RSA). Therefore, it is known as the Enhanced HBA (EnHBA) technique. The key aim of the EnHBA technique is to alleviating the PQ issue exhibited in the WECS. The EnHBA approach is utilized in the non‐linear load condition to find the best solutions from the available searching space in light of the goal function and generates the output. During the load variation conditions the EnHBA technique manages the loss of power, total harmonic distortion (THD), and voltage instability issue respectively. In this way, the PQ system performances improved and moreover, the various qualities decreased with the support of the EnHBA method. The EnHBA techniques' performance is validated through the MATLAB platform and the implementation is calculated with the existing techniques. The method EnHBA technique displays better outcomes in all approaches like, particle swarm optimization, whale optimization algorithm and singular spectrum analysis (SSA). The proposed method's voltage sag and swell are lowered to 14% and 15%, respectively. The THD of the En HBA method is 10%, which is lesser than other existing PSO, WOA, and SSA methods. [ABSTRACT FROM AUTHOR]

Summary: More and more prefabricated steel–concrete hybrid wind turbine towers have been built because of their better lateral stiffness than those of the full steel towers, in which epoxy resin joints are commonly adopted at the horizontal joint between two ring units for improving the erection speed. In fact, epoxy resin joints are designed in the same way as dry joints due to the very thin thickness of epoxy resin layer, in which epoxy resin only acts as a leveling blanket and sealer for jointing and compensates for the unevenness of the contact surface between two ring units. The current design method for the resistance to torsional moment at the horizontal joint is not reasonable because of the unreasonable assumption of Saint‐Venant's torsional theory. The integral expressions of the ultimate torsional moment at the horizontal joint with and without shear force are derived, respectively. The solution of the integral expressions for the ultimate torsional moment is realized by Python programming. The refined finite element analyses of two cases are compared with the existing small‐scale tests with segmental aluminum tubes, which verifies the calculation accuracy of the proposed integral method. In the modified integral model of the ultimate torsional moment, a correction term of the resistance to torsional moment and a more suitable distribution of shear stress under the action of horizontal shear force are proposed to obtain a more accurate ultimate torsional moment. Finally, 36 sets of cases with typical dimensions and axial forces in practical engineering are analyzed by the proposed integral model in the absence of horizontal shear force. One six‐parameter model for calculating the ultimate torsional moment is fitted by the least square method. A discount factor is proposed to consider the influence of the horizontal shear force on the ultimate torsional moment. [ABSTRACT FROM AUTHOR]

Translation abstract Application areas of calculation models for the torsional load‐bearing capacity of segment joints – Wind turbines with annular cross‐sectionWind turbines play a decisive role in the reorientation of our energy supply. The torsional load‐bearing behaviour of the towers must be analysed in detail and suitable calculation models developed and established to ensure the safe and resource‐saving construction of these plants. In recent years, two important models have been developed to determine the torsional resistance of the horizontal segment joints of wind towers. However, it has not yet been possible to specify their areas of application. Using new test data and analyses, it has now been possible to determine the mechanical validity limits of the models. With the parameter space presented in this paper, the areas of application of the most important calculation models for the torsional load‐bearing capacity of dry joints with annular cross‐sections can be defined and thus an assignment can also be made for wind turbine towers commonly used in practice. [ABSTRACT FROM AUTHOR]

Providing sustainable electricity access to remote areas is critical for economic development and environmental preservation. This study investigates the performance of single‐source and hybrid renewable energy systems for the town of Zahedan, Iran, which has significant solar and wind energy potential. Using TRNSYS software, eight configurations were simulated and analyzed, comprising two single‐source (photovoltaic [PV] and wind turbine [WT]) and six hybrid systems incorporating combinations of PV panels, WTs, alkaline fuel cells, and diesel generators. The analysis revealed that hybrid systems, particularly those combining PV and WT, outperformed single‐source configurations. For instance, a hybrid system with 800 kW of PV and a 50 kW WT reduced diesel consumption by 35% and CO2 emissions by 45% compared to a system relying solely on a diesel generator. Conversely, the configuration involving WTs, fuel cells, and diesel generators showed high energy dumping (1,821,776 kWh) and considerable diesel usage, underscoring the challenges of maintaining energy balance without solar integration. Overall, hybrid renewable systems generally provide enhanced reliability and environmental benefits, although their performance heavily depends on the specific energy source mix. This study offers insights into optimizing renewable energy systems for remote locations, highlighting the necessity of a balanced solar‐wind combination to achieve optimal sustainability and cost‐effectiveness. The findings are applicable to regions with similar climatic conditions and contribute to global sustainable energy solutions, providing crucial information for policymakers and investors focused on supporting sustainable energy projects in isolated areas. [ABSTRACT FROM AUTHOR]

Summary: The prestressed anchor bolt system is a reasonable connection mode between the upper steel tower and the bottom concrete foundation for multi‐megawatt wind turbines. This prestressed anchor bolt connection with forged flanges has a similar form to the prestressed high‐strength bolt connection with forged flanges between steel tubes for the tower. However, their mechanical performances have a great difference because of the influence of the stiffness of the anchorage zone in the concrete foundation. Based on the Petersen's method, the engineering calculation method of the prestressed anchor bolt system for wind turbine foundation is derived. The tensile force of the anchor bolt, the anchorage stiffness and clamping force of the base are all deduced according to the theories of mechanics of materials. The numerical models of the segment foundation with the unfavorable anchor bolt and the overall foundation with all anchor bolts are developed for researching the influence of the spatial effect of adjacent segments on the restraint stiffness of the concrete foundation. The numerical analyses of four engineering cases designed by engineering calculation method are carried out for verifying the effectiveness of engineering calculation method. The analysis results show the spatial effect of adjacent segments can be neglected and the Petersen's method can be directly applied for the design of the prestressed anchor bolt system for wind turbine foundation in engineering practice. The engineering calculation method meets the accuracy requirements in engineering practice and can be used to design the prestressed anchor bolt system of wind turbine foundation. [ABSTRACT FROM AUTHOR]

As the global momentum for wind power generation accelerates, the industry faces substantial challenges due to premature failures in wind turbine components. These failures, particularly in critical elements like the high‐speed shaft bearing, lead to significant operational losses, including unplanned downtime and elevated maintenance costs. To mitigate these issues, it's crucial to have precise predictions of the remaining useful life (RUL) of these components, enabling timely interventions and more efficient maintenance schedules. This article proposes advanced, data‐driven approaches for estimating the RUL of wind turbine high‐speed shaft bearings, utilizing cutting‐edge techniques such as long short‐term memory (LSTM), bidirectional LSTM (BiLSTM), gated recurrent units (GRU), and random forest (RF) algorithms. Our analysis leverages vibration data from a 2 MW wind turbine equipped with a 20‐tooth pinion gear, providing a thorough validation and comparison of these methodologies against traditional models. Our results reveal that the LSTM and BiLSTM models excel in both accuracy and computational efficiency for predicting RUL and enhancing system prognosis, surpassing the performance of conventional RF and GRU methods. This research underscores the potential of our innovative data‐driven strategies to develop effective RUL estimation algorithms, significantly advancing wind turbine proactive operation and maintenance operations. [ABSTRACT FROM AUTHOR]

The upward discharge is a significant factor threatening the operation of wind farms. This paper investigates the inception and development of upward leaders of wind turbines' tip, where blade rotation prevents charge accumulation, using charge density models from laboratory discharges and artificially triggered lightning. The results indicate that larger return stroke currents create higher spatial potentials, and slower downward leader speeds allow for longer development time, favoring the development of upward leaders. As wind turbine sizes increase, the rotation of the blade tips resembles artificially triggered lightning, and the long air gap discharge model may underestimate the development of upward leaders. Calculations from the artificially triggered lightning model reveal improved upward leader formation when further laterally away from the downward leader within a certain distance, elucidating why multiple upward leaders are frequently observed around turbines. The striking distance and attractive radius are largely determined by the development of upward leaders. The absence of corona charge shielding at blade tips means upward leader inception is independent of downward leader velocity, emphasizing downward leaders' significant influence on turbines. Thus, the formula for estimating a wind turbine's lightning incidence requires integrating the return stroke current and velocity of the downward leader. [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]

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]

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]

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]

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]

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]

Neglecting the velocity reduction in the induction zone of wind turbines can lead to overestimates of power production predictions, and, thus, of the annual energy production for a wind farm. An experimental study on the induction zone associated with wind turbine operations is performed in the boundary‐layer test section of the BLAST wind tunnel at UT Dallas using stereo particle image velocimetry. This experiment provides a detailed quantification of the wind speed decrease associated with the induction zone for two different incoming flows, namely, uniform flow and boundary layer flow. Operations of wind turbines in different regions of the power curve are modeled in the wind tunnel environment with two porous disks with a solidity of 50.4% and 32.3%, which correspond to thrust coefficients of 0.71 and 0.63, respectively. The porous disks are designed to approximate the wake velocity profiles previously measured for utility‐scale wind turbines through scanning wind LiDARs. The results show that the streamwise velocity at one rotor diameter upwind of both disks decreases 1% more for the boundary layer flow than for the uniform flow. Further, the effect of shear in front of the disk with a higher thrust coefficient can be observed until 1.75 rotor diameter upwind of the disk, whereas for the disk with a lower thrust coefficient, the effect of shear becomes negligible at 1.25 rotor diameter upwind. It is found that at one rotor diameter upwind, for both incoming flows, the disk having a higher thrust coefficient has 2% more velocity reduction than the lower‐thrust‐coefficient disk. The results suggest that the variability in wind shear and rotor thrust coefficient, which is encountered during typical operations of wind turbines, should be considered for the development of improved models for predictions of the rotor induction zone, the respective cumulative effects in the presence of multiple turbines, namely, wind farm blockage, and more accurate predictions of wind farm power capture. [ABSTRACT FROM AUTHOR]

Summary: An adaptive optimal controller is proposed in this paper to maximize the captured power of a variable speed wind power system. The proposed controller is a combination of optimal and adaptive control components. The Adaptive Dynamic Programming technique is used to design the optimal control component to overcome the nonlinear problem of system dynamics and ensure stability. While the neural network is used to approximate unknown disturbances and system uncertainties. After that, the adaptive control component fully compensates for the effects of these unknown elements. Neither optimal nor adaptive control components necessitate prior knowledge of system dynamics. Furthermore, the approximation network updates only the weight matrix norm rather than the weight matrix of the neural network in each interval time, which significantly reduces computation. The stability analysis of the closed‐loop system is obtained using Lyapunov stability theory. The correctness and robustness of the control scheme are validated in two different scenarios using MATLAB/Simulink. The presented robust adaptive optimal controller is also compared to other existing controllers to demonstrate its benefits. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This study addresses the pressing issue of quality electricity access in remote regions, with a specific focus on Tumbatu Island in Tanzania. Most studies on quality improvement concentrate on low‐voltage distribution lines and leave high‐voltage (HV) transmission lines behind. It is essential to address quality issues in HV lines due to their critical role in electricity transmission and distribution infrastructure. The objective of the study is to improve the voltage profile on the island's HV transmission line by identifying the optimal hybrid energy system comprising solar PV, wind turbine, and battery technologies. The study begins by presenting the total power demand and consumption on Tumbatu Island, which are important factors in designing an efficient energy system. The findings reveal a power demand of 7173 kW and a consumption of 28540 kWh/day, with an average scaled value of 1507.9 and 6000 kWh/day. To achieve the desired voltage profile improvement, the research incorporates HOMER Pro software for simulating and analyzing various hybrid system configurations. Essential input parameters, such as costs, resources, technology components, and load, are considered in the simulation process. The software generates a ranked list of options based on the system's net present cost (NPC). The simulation results demonstrate that the integration of solar PV, wind turbine, and HV lines provides a substantial improvement in the voltage profile, increasing it from 29.6 to 31.23 kV during peak demand periods. This solution proves to be the most economically viable, with the lowest NPC of $4,003,851 and a relatively short payback period of 3.79 years. In addition, a sensitivity analysis is performed to identify the most influential parameter in the system's performance. Wind speed is found to have the greatest impact, emphasizing its significance in the design and operation of the hybrid energy system. The implementation of this optimal hybrid renewable energy system on Tumbatu Island will not only improve the voltage profile and meet the island's energy needs but also shall contribute to global efforts in reducing pollution and the cost of electricity to the Tumbatu Island population. Moreover, it addresses the current and future demands for clean energy, underlining its importance in achieving sustainable and accessible electricity in sub‐Saharan Africa and beyond. [ABSTRACT FROM AUTHOR]

The use of a turbo‐sail airfoil to improve the power generation of a Darrieus wind turbine was examined. The turbo‐sail airfoil comprises a symmetric blade from which a high‐speed tangential jet is produced to suppress flow separation. For a steady angle of attack, we experimentally confirmed an anti‐stall function of the airfoil at an angle of attack up to and exceeding 30°. Considering the case of the rotation of a single blade of a Darrieus wind turbine, the flow field was replaced with a non‐separated stream around the airfoil particularly even at a tip‐speed ratio lower than 3. The pressure field and local lift‐to‐drag ratio measured by particle image velocimetry quantitatively supported the expected anti‐stall function. Improvements in the torque and power coefficient were estimated from measurements considering the external power consumed in supplying the jet. [ABSTRACT FROM AUTHOR]

This manuscript proposes a novel technique for the precise model of electric vehicles (EVs) in the reliability and adequacy model of smart grids (SG). The proposed method combines forensic‐based investigation (FBI) and Archimedes optimization algorithm (AOA), named the FBIAOA technique. The objective of the proposed method is to rise the profit of fast charging stations and lessen the rising energy demand on the grid that is made up of storage systems and renewable energy generation (wind and PV). The demand for EVs and renewable generation is calculated using the FBI algorithm method. The growth of the proposed method is to examine the reliability of SG depending on the aggregation of the state matrices of EV stochastic parameters. The proposed method can help accelerate the reliability calculations by determining the desired count of EV states. The proposed strategy is run in MATLAB and is evaluated in its performance with existing methods. The proposed method gives a lower cost than the existing genetic algorithm, cuttlefish algorithm, and tunicate swarm algorithm methods. [ABSTRACT FROM AUTHOR]

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]

Summary: Over the past few decades, global demand for renewable energy has been rising steadily. To meet this demand, there has been an exponential growth in size of wind turbines (WTs) to capture more energy from wind. Consequent increase in weight and flexibility of WT components has led to increased structural loading, affecting reliability of these wind energy conversion systems. Spatio‐temporal variation of rotor effective wind field acts as a disturbance to a WT system, hence, necessitating controllers that can cancel this disturbance. Additionally, assumptions made in extracting linear models for controller design lead to modeling errors resulting from changing operating conditions. Previous attempts have proposed robust controllers incorporating wind disturbance models. However, these controllers have been evaluated on smaller WTs, which experience lower structural loading than larger ones. Additionally, a majority these controllers are based on collective pitch control (CPC), hence do not address loading in the blades. To address these challenges, this contribution proposes an independent pitch‐based robust disturbance accommodating controller (IPC‐RDAC) for reducing structural loads and regulating generator speed in utility‐scale WTs. The proposed controller is designed using μ‐synthesis approach and is evaluated on the 5 MW National Renewable Energy Laboratory (NREL) reference WT. Its performance is evaluated against a gain‐scheduled proportional integral (GSPI)‐based reference open‐source controller (ROSCO) and a CPC‐based RDAC (CPC‐RDAC) controller, developed previously by the authors. Simulation results for various wind conditions show that the proposed controller offers improved performance in blade and tower load mitigation, as well a generator speed regulation. [ABSTRACT FROM AUTHOR]

The aim of this study is to explore the potential and economic benefits of utilising Supervisory Control and Data Acquisition (SCADA) data to improve wind turbine operation and maintenance activities. The review identifies a gap in the current understanding of how to effectively use SCADA data in wind turbine applications. It emphasises the need for pre‐processing SCADA data to ensure data integrity by addressing outliers and employing interpolation techniques. Additionally, it highlights the challenges associated with early fault detection methods using SCADA data, including the development of physical models, data‐driven machine learning models, and statistical regression models. The review also recognises the limitations caused by the lack of public data from wind turbine developers and the imbalance between normal operation data samples and abnormal data samples, negatively impacting model accuracy. The key findings of the review demonstrate that SCADA data‐driven techniques can lead to significant improvements in wind turbine operations and maintenance. The application of data‐driven technologies based on SCADA data has proven effective in reducing operation and maintenance costs and enhancing wind power generation. Moreover, the development of robust decision support systems using SCADA data minimises the need for frequent maintenance interventions in offshore wind farms. To bridge the gap and further enhance wind turbine applications using SCADA data, several recommendations are provided. These include encouraging greater openness in sharing SCADA data to improve the robustness and accuracy of AI models, adopting transfer learning techniques to overcome the scarcity of quality datasets, establishing unified standards and taxonomies, and providing specialised resources such as software applications with interactive graphical user interfaces for easier storage, annotation, and analysis of SCADA data. The authors' review paper identifies a gap in the current understanding of how to effectively utilise SCADA data in wind turbine applications. It emphasises the importance of pre‐processing SCADA data to ensure data integrity by addressing outliers and employing interpolation techniques. Furthermore, the authors highlight the challenges associated with early fault detection methods using SCADA data, including the development of physical models, data‐driven machine learning models, and statistical regression models. Our review paper identifies a gap in the current understanding of how to effectively utilize SCADA data in wind turbine applications. It emphasizes the importance of pre‐processing SCADA data to ensure data integrity by addressing outliers and employing interpolation techniques. Furthermore, we highlight the challenges associated with early fault detection methods using SCADA data, including the development of physical models, data‐driven machine learning models, and statistical regression models. [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]

This paper addresses the problem of controlling and optimizing a hybrid renewable energy system, specifically consisting of a photovoltaic (PV) generator and a wind energy conversion system comprising a wind turbine and a permanent magnet synchronous machine. The primary control objectives involve maximizing power extraction from both the PV and wind turbine while ensuring efficient injection of the extracted energy into the electrical network and maintaining a suitable power factor. This study presents two key innovations that distinguish it from existing research. Firstly, it introduces a new hybrid PV/aerogenerator energy source that eliminates the need for a DC/DC converter in the PV structure. Secondly, unlike previous works that rely on various measurements such as wind speed, temperature, and irradiance, our approach only requires measurements of electrical variables. This advancement significantly reduces the number of sensors required and enhances the overall reliability of the system. [ABSTRACT FROM AUTHOR]

Predictive high‐fidelity modeling of wind turbines with computational fluid dynamics, wherein turbine geometry is resolved in an atmospheric boundary layer, is important to understanding complex flow accounting for design strategies and operational phenomena such as blade erosion, pitch‐control, stall/vortex‐induced vibrations, and aftermarket add‐ons. The biggest challenge with high‐fidelity modeling is the realization of numerical algorithms that can capture the relevant physics in detail through effective use of high‐performance computing. For modern supercomputers, that means relying on GPUs for acceleration. In this paper, we present ExaWind, a GPU‐enabled open‐source incompressible‐flow hybrid‐computational fluid dynamics framework, comprising the near‐body unstructured grid solver Nalu‐Wind, and the off‐body block‐structured‐grid solver AMR‐Wind, which are coupled using the Topology Independent Overset Grid Assembler. Turbine simulations employ either a pure Reynolds‐averaged Navier–Stokes turbulence model or hybrid turbulence modeling wherein Reynolds‐averaged Navier–Stokes is used for near‐body flow and large eddy simulation is used for off‐body flow. Being two‐way coupled through overset grids, the two solvers enable simulation of flows across a huge range of length scales, for example, 10 orders of magnitude going from O(μm) boundary layers along the blades to O(10 km) across a wind farm. In this paper, we describe the numerical algorithms for geometry‐resolved turbine simulations in atmospheric boundary layers using ExaWind. We present verification studies using canonical flow problems. Validation studies are presented using megawatt‐scale turbines established in literature. Additionally presented are demonstration simulations of a small wind farm under atmospheric inflow with different stability states. [ABSTRACT FROM AUTHOR]

This paper studies the rating lives of wind turbine main bearings, as determined by the IEC 61400‐1 and ISO 281 standards. A critical review of relevant bearing life theory and turbine design requirements is provided, including discussion on possible shortcomings such as the existence (or not) of the bearing fatigue load limit and the validity of assuming linear damage accumulation. A detailed exploratory case study is then undertaken to determine rating lives for two models of main bearing in a 1.5 MW wind turbine. Rating life assessment is carried out under different conditions, including various combinations of main bearing temperature, wind field characteristics, lubricant viscosity, and contamination levels. Rating lives are found to be sufficiently above the desired 20‐year design life for both bearing models under expected operating conditions. For the larger bearing, operational loads are shown to be below or close to the bearing fatigue load limit a vast majority of the time. Key sensitivities for rating life values are temperature and contamination. Overall, the results of this study suggest that an ISO 281 rating life assessment does not account for reported rates of main bearing failures in 1 to 3 MW wind turbines. It is recommended that a similar analysis be undertaken for ISO/TS 16281 rating lives, along with further efforts to identify principal root causes of main bearing failures in future work, possibly leading to a new application standard specific to this component. It is also recommended that the impacts of partial wake impingement on main bearing rating lives are investigated. [ABSTRACT FROM AUTHOR]

Automated curtailment of wind turbines can reduce fatality rates of wildlife but the resulting increased number of curtailments can reduce power generation. Tailoring curtailment criteria for each individual turbine could reduce unnecessary curtailment, yet it is unknown whether the risk to wildlife varies among turbines. We demonstrate turbine‐specific variation in the speed, altitude, approach angle and distance metrics associated with entry by eagles into rotor‐swept zones. Our results thus illustrate the potential value of turbine‐specific curtailment criteria to reduce fatality rates of wildlife at wind energy facilities. [ABSTRACT FROM AUTHOR]

One major criterion in the selection of wind farm location is the cost of energy (COE). COE is the cost of producing 1 kWh electric energy on an annual basis. Mathematical model of COE includes site‐specific constants (such as reference height, mean wind speed, shape factors, wind shear coefficient, average temperature, and turbine altitude) and wind turbine parameters (such as maximum power coefficient, total loss of energy, cut‐in/cut‐off wind speed, rated wind speed, rated power, and the fix charge rate). In this work, we evaluate the COE of an onshore wind farm located at Dumat Al‐Jandal (Saudi Arabia) according to the hub height and rotor size. The 99 Vestas turbines can be mounted at a hub height ranging from 105 to 166 m with available rotor diameters of 105, 112, 117, 126, 136, 150, 155, or 163 m. Particle swarm optimization with a normal distribution is used to optimize the COE. Results show that COE is varying around the average value of $0.029335/kWh by ±$0.00021/kWh. The minimum COE was achieved with a rotor diameter of 150 m at hub height of 105 m. COE increases with the increase of hub height. At 105 m‐hub height, COE is almost the same, with a variation of 0.03% (It ranges between $0.029125/kWh and $0.029133/kWh). COE is more sensitive to rotor size than hub height. This investigation revealed that the COE estimation is in a range of 39%–48% greater than that announced COE by the developing project consortium. [ABSTRACT FROM AUTHOR]

In this article, a method based on sliding mode control, named adaptive slope sliding mode control is proposed to increase the response speed of the system outputs. In this method, the difference between the surface and the state is maintained at its minimum value by constantly changing the slope of the surface, making the states converge faster. Compared to fixed slope methods, it can be said that by changing the slope of the system, not only the response speed of the system is increased, but also the output accuracy increases as well. To evaluate the effects of adding a variable slope to sliding mode controllers, the proposed controller is applied to the grid synchronization of a wind turbine system. [ABSTRACT FROM AUTHOR]

A tropical cyclone (TC) is extreme weather severely threatening coastal wind turbines. However, the non‐extreme wind speed period (nEWSP) before and after it can also bring considerable economic benefits to wind farms. Aiming at the working conditions of nEWSP‐TC, this paper analyzes the characteristics of the wind field based on historical actual TC data and constructs a pseudo‐Monte Carlo experiment, uses the experimental results to construct a knowledge base and an inference engine, and forms an expert system to guide the yaw control of wind turbines in nEWSP‐TC. The simulation results show that the yaw error has different effects on the fatigue load of the wind turbine under different working conditions of nEWSP‐TC, and the proposed improved yaw strategy can reduce the fatigue load of the wind turbine under the premise of limited power loss and improve safe operation capability of wind turbines under nEWSP‐TC conditions. [ABSTRACT FROM AUTHOR]

1)We recently demonstrated efficacy of automated curtailment of wind turbines in reducing fatalities of eagles at a study site in Wyoming, USA. Huso and Dalthorp criticize our work, asserting that there are several 'major errors' that render our previous work as providing 'no meaningful support' for automated curtailment. As we show here, our data do indeed provide support for the efficacy of automated curtailment.2)The purported major errors they identify include: having only 1 year of post‐treatment data, having only one control and one treatment site, using the control site for inference, choosing an arbitrary demarcation date between the 'before' and 'after' periods, analysing the number, rather than the rate, of fatalities, and not including data they consider pertinent. These claims are unsupported because all result from misreading our study, ignoring realities of causal inference in field ecology, or are matters of preference.3)Most importantly, we did not fail to include pertinent data and we provided sufficient criteria in the manuscript to explain inclusion of carcasses in our study. As stated in our previous study, the eagle carcasses we did not include were found either outside of search areas or incidentally, and thus did not meet criteria for inclusion. Further, Huso and Dalthorp present a standard for spatial and temporal replication that is inconsistent with their own recent work and many other studies in this field. Finally, their reanalysis of our data ignores the control site while including inappropriate data and thus is not suitable for inference.4)We appreciate the unusually thorough critique of our work provided by Huso and Dalthorp. Despite the major errors in their argument, this discussion provides a platform for further evaluation of our original work.5)Synthesis and applications. We show that incorporating control site data and using our a priori demarcation of before and after periods into Huso and Dalthorp's reanalysis returns meaningful support for automated curtailment. We also direct the reader to an updated analysis that uses components of the approach Huso and Dalthorp suggest, along with additional data and a number of other analytical improvements, to validate and strengthen our original conclusions. [ABSTRACT FROM AUTHOR]

To reduce bird collisions on wind turbines, Automatic Detection Systems have been developed to locate approaching birds and trigger turbines to slowdown to 2–3 rotations per minute (rpm). However, it is unknown whether birds can detect this reduced speed and avoid the turbine. We conducted an operant conditioning experiment on domestic doves (Streptopelia roseogrisea) and Harris's hawks (Parabuteo unicinctus) to assess their ability to discriminate between stationary and rotating miniature wind turbines, depending on the rotation speed and the contrast between the white blades and the background (only for doves for the latter). At high contrast, regardless of the speed tested, hawks were able to differentiate between the rotating and stationary turbines, while doves were not able to discriminate the slow‐rotating turbine (3 rpm) from the stationary one. The discrimination threshold increased to 8 rpm for the doves when the contrast was reduced. Our results suggest that the residual wind turbine speed of 2–3 rpm may not be detected by all bird species under all environmental conditions. Increasing the contrast between wind turbines and their environment may improve the detection of low‐speed rotation by some birds, otherwise, complete turbine shutdown should be recommended. [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]

Networked microgrids emerged from the growing deployment of microgrids in the distribution network. The coordinated operation of networked microgrids offers technical and economical benefits to microgrid owners, customers, grid/utility, and other stakeholders. The optimal capacity sizing of renewable energy sources and battery energy storage systems allows microgrids to minimize costs and maximize reliability. A multi‐objective optimization problem is developed for optimal sizing in a networked microgrid consisting of four different microgrids. The annual energy costs and loss of power supply probability index are taken as objectives. Peer‐to‐peer and peer‐to‐grid energy trading approaches are employed. The peer‐to‐peer energy trading among microgrids employs the proposed "proportional trading method" via a networked microgrid manager or aggregator. The multi‐objective optimization problem formulated is solved using Multi‐Objective Particle Swarm Optimization. The individual objective optimization results for annual energy cost and loss of power supply probability are also analyzed. The proposed method decreases the interaction between the grid and the MGs, and the usage of renewable energy sources is enhanced. The capacity of battery energy storage systems is lowered by 96%, 53.2%, 48.86%, 21% for respective microgrids in networked microgrids. The results of proportional peer‐to‐peer energy trading‐based multi‐objective optimization show that trading energy among microgrids minimizes annual energy cost by 0.75% while maintaining system reliability. [ABSTRACT FROM AUTHOR]

With the increasing demand for wind energy, it is important to be able to understand and predict the available wind resources. To that end, the present wind tunnel study addresses the flow in the induction and entrance region of wind farms through particle image velocimetry, with focus on differences between actuator disks and two‐bladed rotating wind turbine models. Both staggered and aligned farm layouts are examined for three different incoming wind directions. For each layout, 69 disks or turbines are used, and the field of view ranges from 12 rotor diameters upstream of the farms to 8 diameters downstream of the first row. The results show that the induction, or blockage effect, is higher for the disks, even though the thrust (or drag) coefficient is the same. In contrast, the wake is stronger downstream of the turbines. The orientation and layout of the farm do not have a major impact on the results. Modal decomposition of the flow shows that the flow structure similarity between the disk and turbines improves downstream of the second row of wake generating objects, indicating that the substitution of wind turbines by actuator disks is more appropriate for wind farms than for the investigation of single wakes. [ABSTRACT FROM AUTHOR]

The rotor‐circuit (RC) of the doubly fed induction generator‐based wind turbine (DFIG‐WT) consists of various equipment in which faulty conditions must be immediately identified, because this faulty status leads to the loss‐of‐excitation (LOE) phenomenon and may cause some serious damages. In this regard, this paper develops a novel solution to protect the DFIG‐WT during such a condition. For this purpose, a comprehensive investigation on behavior of the DFIG‐WT during the conditions of the RC failures has been carried out. The results show that, the output active power of the machine (Pdfig) can be considered as a suitable initial indicator for detecting the RC failures. However, to reliable detection of the partial‐LOE (PLOE), complete‐LOE (CLOE) and discrimination from the other power system disturbance (PSD), a directional over‐current (DOC) and an under voltage (UV) relays are suggested to be installed in the RC, at both ends of the back‐to‐back converters. Consequently, by detecting the Pdfig decrement, the provided DOC and UV relays in the RC will be triggered to classify the occurred events. The outlined protection scheme is exhaustively investigated on a typical power system which the obtained simulation results show the high reliability in discriminating the RC failures and PSD events with an acceptable detection time. [ABSTRACT FROM AUTHOR]

As a well‐established renewable energy resource, wind energy is being widely used in distribution networks. However, flicker emission produced by wind turbines (WTs) is one of the main disadvantages which may cause serious problems during the system operation. Power quality‐based optimal allocation (siting and sizing) of WTs is an appropriate way to mitigate the flicker emission. Since the planning issue is a high dimensional and complex optimization problem and optimization methods suffer from premature convergence, search space reduction may be an effective approach to optimally solve the allocation problem. Here, to determine the importance of the network buses, two indices, namely, power loss index (PLI) and flicker emission index (FEI) are introduced to determine the sensitivity of the network losses and flicker emission to allocate WT on each bus. Then, based on the proposed indices, a number of important buses are identified and the allocation problem is solved with respect to these buses. Over the case study, it can be drawn that optimal allocation of WTs considerably affects the flicker emission. Moreover, search space reduction is an efficient way to optimally solve the allocation problem. [ABSTRACT FROM AUTHOR]

This article presents the development of the Control‐oriented, Reconfigurable, and Acausal Floating Turbine Simulator (CRAFTS). CRAFTS has a modular, hierarchical model architecture that enables rapid and accurate simulation of wind turbines. The architecture facilitates the incorporation of model variants, and its system reconfiguration features help simulate multiple design variants. CRAFTS also supports the integration of models developed on a causality‐free platform (e.g., Modelica®) with existing causal models. This article focuses on the validation of a land‐based wind turbine models against OpenFAST, an industry‐standard platform, for several test cases. Closed‐loop scenarios are also tested using the standard ROSCO controller and compared against OpenFAST. In addition, a nonlinear controller developed in our prior work is also evaluated. The test cases demonstrate the user‐friendly and computationally efficient capabilities of CRAFTS to facilitate control co‐design, assist in incorporating multiphysics models, be adaptable to design variants, and allow for rapid simulations to validate models and evaluate controllers. [ABSTRACT FROM AUTHOR]

Modern utility‐scale wind turbines are equipped with a Supervisory Control And Data Acquisition (SCADA) system gathering vast amounts of operational data that can be used for analysis to improve operation and maintenance of turbines. We analyze high‐frequency SCADA‐data from the Thanet offshore wind farm in the United Kindom and evaluate Pearson correlation matrices for a variety of observables with a moving time window. This renders possible a quantitative assessment of nonstationarity in mutual dependencies of different types of data. We show that a clustering algorithm applied to the correlation matrices reveals distinct correlation structures for different states. Looking first at only one and then at multiple turbines, the main dependence of these states is shown to be on wind speed. This is in accordance with known turbine control systems, which change the behavior of the turbine depending on the available wind speed. We model the boundary wind speeds separating the states based on the clustering solution. Our analysis shows that for high‐frequency data, the control mechanisms of a turbine lead to detectable nonstationarity in the correlation matrix. The presented methodology allows accounting for this with an automated preprocessing by sorting new data based on wind speed and comparing it to the respective operational state, thereby taking the nonstationarity into account for an analysis. [ABSTRACT FROM AUTHOR]

Bearing failure in wind turbine gearboxes is one of the significant sources of downtime. While it is well‐known that bearing failures cause the largest downtime, the failure cause(s) is often elusive. The bearings are designed to satisfy their rolling contact fatigue (RCF) life. However, they often undergo sudden and rapid failure within a few years of operation. It is well‐known that these premature failures are attributed to surface damages such as white surface flaking (WSF), white etching cracks (WECs) and axial cracks. In that regard, transient torque reversals (TTRs) in the drivetrain have emerged as one of the primary triggers of surface damage, as explained in this paper. The risk associated with TTRs motivates the need to mitigate TTRs arising in the drivetrain due to various transient events. This paper investigates three TTR mitigation methods. First, two existing devices, namely, the torsional tuned mass damper and the asymmetric torque limiter, are studied to demonstrate their TTR mitigation capabilities. Then, a novel idea of open‐loop high‐speed shaft mechanical brake control is proposed. The results presented here show that while the torsional tuned mass damper and the asymmetric torque limiter can improve the torsional vibration characteristics of the drivetrain, they cannot mitigate TTRs in terms of eliminating the bearing slip risk associated with TTRs. However, the novel approach proposed here can mitigate TTRs both in terms of improving the torque characteristic in the high‐speed shaft and reducing the risk of bearing slip by actuating the high‐speed shaft brake at the onset of the transient event. Furthermore, the control method is capable of mitigating TTRs with the mechanical limitations of a pneumatic actuator in terms of bandwidth and initial dead time applied to it. This novel approach allows the wind turbines to protect the gearbox bearings from TTRs using the existing hardware on the turbine. [ABSTRACT FROM AUTHOR]

The uncertainty of wind speed has a great impact on the reliability of wind turbines. However, previous studies usually used average wind speed or rated wind speed as inputs, ignoring the impact of time and space distribution on wind speed. Based on the hourly ground observation data of meteorological stations, four observation stations are taken as the research object of the uncertainty of wind speed spatial distribution. The four observation stations are divided into single day and one week, and seven data analysis indicators and normal distribution models are used day and night to analyze the uncertainty of wind speed time distribution. The results show that both spatial distribution and temporal distribution are important factors that cause the uncertainty of wind speed. Due to different geographical locations of four stations, their wind speed time domain diagrams are different, and the maximum wind speed, fluctuation frequency and fluctuation amplitude of wind speed at each observation station are different; The wind speed distribution of a single day is quite different from that of a week. The data of a single day and a week are analyzed by day and night, which is quite different from the overall data analysis. The necessity of wind speed uncertainty research of wind turbine considering the temporal and spatial distribution is verified, and it is concluded that normal distribution is not suitable for fitting the distribution model of four stations under study, which lays a foundation for determining the wind speed distribution model, analyzing the power characteristics of wind turbine, evaluating the generation capacity of wind turbine, and analyzing the reliability of wind turbine. [ABSTRACT FROM AUTHOR]

Recently, due to the increase of the frequency of high impact low probability (HILP) events (manmade and natural events), assessment and enhancement of resilience has become very important in the operation and planning procedures of future power systems. In this regard, several researches have been conducted on operation and planning strategies of the power system to improve resilience. Here, assessment and enhancement approaches of power system resilience by focusing on the role of wind farms and wind turbines are reviewed. To do so, a comprehensive review of the related metrics, hazard types, modelling approaches, assessment methods, and enhancement solutions is presented. Then, the assessment and enhancement methods of power system resilience are categorized in three phases before event (preventive proactive actions), during event (corrective active actions) and after event (restorative reactive actions) from the point of view of operation and planning actions. Finally, the current challenges, research gaps and future trends are discussed. [ABSTRACT FROM AUTHOR]

The adverse effect of transient torque reversals (TTRs) on wind turbine gearboxes can be severe due to their magnitude and rapid occurrence compared with other equipment. The primary damage is caused to the bearings as the bearing loaded zone rapidly changes its direction. Other components are also affected by TTRs (such as gear tooth); however, its impact on bearings is the largest. While the occurrence and severity of TTRs are acknowledged in the industry, there is a lack of academic literature on their initiation, propagation and the associated risk of damage. Furthermore, in the wide range of operation modes of a wind turbine, it is not known which modes can lead to TTRs. Further, the interdependence of TTRs on environmental loading like the wind is also not reported. This paper aims to address these unknowns by expanding on the understanding of TTRs using a high‐fidelity numerical model of an indirect drive wind turbine with a doubly fed induction generator (DFIG). To this end, a multibody model of the drivetrain is developed in SIMPACK. The model of the drivetrain is explicitly coupled to state‐of‐the‐art wind turbine simulator OpenFAST and a grid‐connected DFIG developed in MATLAB®'s Simulink® allowing a coupled analysis of the electromechanical system. A metric termed slip risk duration is proposed in this paper to quantify the risk associated with the TTRs. The paper first investigates a wide range of IEC design load cases to uncover which load cases can lead to TTRs. It was found that emergency stops and symmetric grid voltage drops can lead to TTRs. Next, the dependence of the TTRs on inflow wind parameters is investigated using a sensitivity analysis. It was found that the instantaneous wind speed at the onset of the grid fault or emergency shutdown was the most influential factor in the slip risk duration. The investigation enables the designer to predict the occurrence of TTRs and quantify the associated risk of damage. The paper concludes with recommendations for utility‐scale wind turbines and directions for future research. [ABSTRACT FROM AUTHOR]

An operating 2‐MW wind turbine has been scanned and analysed using 2D computational fluid dynamics (CFD) and blade element momentum (BEM) analysis. The current work illustrates using full‐scale 3D CFD simulations the differences between 2D and 3D simulations and its impact on the local aerofoil vortex shedding frequency. The outcome shows that despite a pressure redistribution and lift change introduced by the blade span and rotation, the vortex shedding frequency remains similar between 2D and 3D thereby validating the novel fatigue calculation method previously proposed. [ABSTRACT FROM AUTHOR]

The main bearing supports the rotation of the main shaft of a wind turbine. It bears heavy dead weights as well as variable speed dynamic loading during operations; thus, it is a vulnerable part in a wind turbine drive train. Because of the low speed and time‐varying operations of the main bearing, vibrations generated by bearing faults are often weak in response amplitudes, low in frequency range, and smeared in damage feature energy. As a result, the applicability of the conventional acceleration envelope analysis (AEA) technique, a traditionally effective technology for bearing fault diagnosis, is limited in such cases. In order to resolve this, a modified AEA method specially designed for bearings with low and variable speed operation is proposed in this paper. First, the structural response is decomposed by means of variational mode decomposition (VMD) for the low frequency components to form a series of band‐limited intrinsic mode functions (BLIMFs). Next, weighting factors are determined for the BLIMFs by defined energy ratios. Finally, a new envelope is reconstructed by weighting the envelopes of each BLIMF for bearing fault diagnosis. The effectiveness and practicality of the proposed method for the diagnosis of main bearing faults in wind turbines is verified through the analysis of measured data from a wind turbine in the field. The proposed method provides an effective way for bearing fault diagnosis at low and variable rotational speeds. [ABSTRACT FROM AUTHOR]

This article presents a data‐driven control algorithm for a variable‐speed wind turbine in the partial load region. The controller is designed and optimized online to capture maximum energy from the wind. The online optimization is based on the simultaneous perturbation stochastic approximation method (SPSA) that is data‐driven, has a low computational load, and converges rapidly. In the control structure, the generator torque control is activated, and the aerodynamical models and the wind speed value are not required. A suitable cost function is defined, and a time window optimization problem is solved via the SPSA algorithm. The efficiency of the proposed method is evaluated on a 4.8 MW wind turbine benchmark. [ABSTRACT FROM AUTHOR]