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Full- and reduced-order observers have been used in many engineering applications, particularly for energy systems. Applications of observers to energy systems are twofold: (1) the use of observed variables of dynamic systems for the purpose of feedback control and (2) the use of observers in their own right to observe (estimate) state variables of particular energy processes and systems. In addition to the classical Luenberger-type observers, we will review some papers on functional, fractional, and disturbance observers, as well as sliding-mode observers used for energy systems. Observers have been applied to energy systems in both continuous and discrete time domains and in both deterministic and stochastic problem formulations to observe (estimate) state variables over either finite or infinite time (steady-state) intervals. This overview paper will provide a detailed overview of observers used for linear and linearized mathematical models of energy systems and review the most important and most recent papers on the use of observers for nonlinear lumped (concentrated)-parameter systems. The emphasis will be on applications of observers to renewable energy systems, such as fuel cells, batteries, solar cells, and wind turbines. In addition, we will present recent research results on the use of observers for distributed-parameter systems and comment on their actual and potential applications in energy processes and systems. Due to the large number of papers that have been published on this topic, we will concentrate our attention mostly on papers published in high-quality journals in recent years, mostly in the past decade. [ABSTRACT FROM AUTHOR]

To improve the power generation efficiency of offshore wind turbines and address the problem of high fire monitoring and warning costs, we propose a data-driven fire warning method based on transfer learning for wind turbines in this paper. This paper processes wind turbine operation data in a SCADA system. It uses an extreme gradient-boosting tree (XGBoost) algorithm to build an offshore wind turbine unit fire warning model with a multiparameter prediction function. This paper selects some parameters from the dataset as input variables for the model, with average cabin temperature, average outdoor temperature, average cabin humidity, and average atmospheric humidity as output variables. This paper analyzes the distribution information of input and output variables and their correlation, analyzes the predicted difference, and then provides an early warning for wind turbine fires. This paper uses this fire warning model to transfer learning to different models of offshore wind turbines in the same wind farm to achieve fire warning. The experimental results show that the prediction performance of the multiparameter is accurate, with an average MAPE of 0.016 and an average RMSE of 0.795. It is better than the average MAPE (0.051) and the average RMSE (2.020) of the prediction performance of a backpropagation (BP) neural network, as well as the average MAPE (0.030) and the average RMSE (1.301) of the prediction performance of random forest. The transfer learning model has good prediction performance, with an average MAPE of 0.022 and an average RMSE of 1.469. [ABSTRACT FROM AUTHOR]

At present, most studies use the direct method to analyze the oscillation problem of modern power systems. However, these studies often only simplify the external characteristics of the wind turbine and lack an in-depth understanding of its internal refined energy structure. In this paper, based on the direct-drive permanent magnetic synchronous generator's detailed model (D-PMSG), combined with the dynamic energy of its port, layers of analysis are performed on the wind turbine's internal connections, and a detailed model of the energy structure is created. Then, the interaction mechanism of each control link in the wind turbine is analyzed by combining the energy function of the wind turbine with the improved perturbation method. Finally, this paper constructs a sub-synchronous oscillation (SSO) scenario of weak damping and a forcing type and proves the accuracy and effectiveness of the traceability method based on the refined energy of D-PMSG. This traceability method based on refined energy is expected to provide a new solution to the stability problem caused by the integration of new energy. [ABSTRACT FROM AUTHOR]

Mathematical models of energy systems have been mostly represented by either linear or nonlinear ordinary differential equations. This is consistent with lumped-parameter dynamic system modeling, where dynamics of system state variables can be fully described only in the time domain. However, when dynamic processes of energy systems display both temporal and spatial evolutions (as is the case of distributed-parameter systems), the use of partial differential equations is necessary. Distributed-parameter systems, being described by partial differential equations, are mathematically (and computationally) much more difficult for modeling, analysis, simulation, and control. Despite these difficulties in recent years, quite a significant number of papers that use partial differential equations to model and control energy processes and systems have appeared in journal and conference publications and in some books. As a matter of fact, distributed-parameter systems are a modern trend in the areas of control systems engineering and some energy systems. In this overview, we will limit our attention mostly to renewable energy systems, particularly to partial differential equation modeling, simulation, analysis, and control papers published on fuel cells, wind turbines, solar energy, batteries, and wave energy. In addition, we will indicate the state of some papers published on tidal energy systems that can be modelled, analyzed, simulated, and controlled using either lumped or distributed-parameter models. This paper will first of all provide a review of several important research topics and results obtained for several classes of renewable energy systems using partial differential equations. Due to a substantial number of papers published on these topics in the past decade, the time has come for an overview paper that will help researchers in these areas to develop a systematic approach to modeling, analysis, simulation, and control of energy processes and systems whose time–space evolutions are described by partial differential equations. The presented overview was written after the authors surveyed more than five hundred publications available in well-known databases such as IEEE, ASME, Wiley, Google, Scopus, and Web of Science. To the authors' best knowledge, no such overview on PDEs for energy systems is available in the scientific and engineering literature. Throughout the paper, the authors emphasize novelties, originalities, and new ideas, and identify open problems for future research. To achieve this goal, the authors reviewed more than five hundred journal articles and conference papers. [ABSTRACT FROM AUTHOR]

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

With the continuous improvement of wind power penetration in the power system, the volatility and unpredictability of wind power generation have increased the burden of system frequency regulation. With its flexible control mode and fast power adjustment speed, energy storage has obvious advantages in participating in power grid frequency regulation. Therefore, this paper studies the control strategy of wind energy storage combined with primary frequency regulation and proposes a control method of wind energy storage combined with primary frequency regulation based on the operation state of wind turbines. This paper analyzes the reserve power and rotor reserve kinetic energy of wind turbines operating in different wind speed ranges, introduces the fan frequency regulation operation state coefficient to quantify the real-time frequency regulation ability of the unit, regards the fan and energy storage system as independent frequency regulation sources, and designs the control strategy of joint frequency regulation of wind and storage in different wind speed ranges. The energy storage system is employed to participate in frequency control in the low-wind-speed range, thereby addressing the "blind spot" issue of wind turbine unit frequency control alone. In the medium- and high-wind-speed ranges, the real-time complementary output of wind and energy storage power is achieved by assigning weights based on the frequency control operation status coefficient. Finally, the effectiveness of the joint frequency modulation control strategy of wind storage in low-, medium-, and high-wind-speed regions is verified in the simulation model. [ABSTRACT FROM AUTHOR]

With the trend towards larger and lighter designs of wind turbines, blades are progressively being developed to have longer and more flexible configurations. Under standstill conditions, the separated flow induced by a wide range of incident flow angles can cause complex aerodynamic elastic phenomena on blades. However, classical momentum blade element theory methods show limited applicability at high angles of attack, leading to significant inaccuracies in wind turbine performance prediction. In this paper, the geometrically accurate beam theory and high-fidelity CFD method are combined to establish a bidirectional fluid–structure coupling model, which can be used for the prediction of the aeroelastic response of wind turbine blades and the analysis of fluid–structure coupling. Aeroelastic calculations are carried out for a single blade under different working conditions to analyze the influence of turbulence, gravity and other parameters on the aeroelastic response of the blade. The results show that the dominant frequency of the vibration deformation response in the edgewise direction is always the same as the first-order edgewise frequency of the blade when the incoming flow condition is changed. The loading of gravity will make the aeroelastic destabilization of the blade more significant, which indicates that the influence of gravity should be taken into account in the design of the aeroelasticity of the wind turbine. Increasing the turbulence intensity will change the dominant frequency of the vibration response in the edgewise direction, and at the same time, it will be beneficial to the stabilization of the aeroelasticity response. [ABSTRACT FROM AUTHOR]

Wind energy, as a key link in renewable energy, has seen its penetration in the power grid increase in recent years. In this context, accurate and reliable short-term wind power prediction is particularly important for the real-time scheduling and operation of power systems. However, many deep learning-based methods rely on the relationship between wind speed and wind power to build a prediction model. These methods tend to consider only the temporal features and ignore the spatial and frequency domain features of the wind power variables, resulting in poor prediction accuracy. In addition to this, existing power forecasts for wind farms are often based on the wind farm level, without considering the impact of individual turbines on the wind power forecast. Therefore, this paper proposes a wind power prediction model based on multi-feature domain learning (MFDnet). Firstly, the model captures the similarity between turbines using the latitude, longitude and wind speed of the turbines, and constructs a turbine group with similar features as input based on the nearest neighbor algorithm. On this basis, the Seq2Seq framework is utilized to achieve weighted fusion with temporal and spatial features in multi-feature domains through high-frequency feature extraction by DWT. Finally, the validity of the model is verified with data from a wind farm in the U.S. The results show that the overall performance of the model outperforms other wind farm power prediction algorithms, and reduces MAE by 25.5% and RMSE by 20.6% compared to the baseline persistence model in predicting the next hour of wind power. [ABSTRACT FROM AUTHOR]

This paper investigates the use of fractional-slot concentrated windings (FSCWs) in large-scale (MW level) offshore wind generators. It focuses specifically on a power rating of 3 MW and uses an existing direct-drive synchronous PM machine (DD-SPM) with 480s/160p and dual three-phase integer-slot winding (ISW) as a baseline. A multiple of the common 12s/10p FSCW machine is used that matches the electrical frequency of the ISW machine, yielding a 192s/160p dual three-phase machine. The hybrid star–delta connection has grown increasingly popular owing to its unique harmonic cancellation properties, which can help reduce rotor and PM eddy current losses in FSCW machines. In this paper, two dual three-phase star–delta-wound machines are scaled to 3 MW and included in the investigation. Specifically, a 384s/160p dual three-phase and dual star–delta winding machine, which is a multiplication of the 24s/10p machine, and a 192s/176p dual three-phase and dual star–delta winding machine, which is a multiplication of the 24s/22p machine, are used. These machines are investigated using finite element analysis (FEA) and compared on the basis of their air-gap flux density harmonics, open-circuit electro-motive force (EMF), torque performance, and losses and power. It is found that the proposed 384s/160p dual star–delta winding machine has the best electromagnetic performance of all, with a stator power that is 1.2% greater than that of the baseline ISW machine. However, this machine has a coil pitch of 2 and so loses the manufacturing and fault-tolerant advantage of having concentrated windings. If concentrated windings are desired, then the proposed 192s/176p dual star–delta winding machine is the best choice, with the stator power only 2.6% less than that of the baseline ISW machine, but unfortunately still has significant rotor and PM eddy current losses. [ABSTRACT FROM AUTHOR]

The development and utilization of clean energy is becoming more extensive, and wind power generation is one of the key points of this. Occasionally, wind turbines are faced with various extreme environmental impacts such as icing, lightning strikes and so on. In particular, the icing of wind turbines increases icing–wind loads, and results in a reduced power output. And blades broken down lead to large-area shutdown accidents caused by high-speed rotating, which seriously affects the reliability and equipment safety of wind power generation. Relevant institutions and researchers at home and abroad have carried out a lot of research on this. This paper summarizes the formation and influencing factors of wind turbine icing, the influence of icing on wind power generation, and defense technologies. First, it introduces the formation conditions and mechanisms of icing in wind farm regions and the relationship between meteorological and climatic characteristics and icing, and analyzes the key influence factors on icing. Then, the impact of icing on wind turbines is explained from the aspects of mechanical operation, the power curve, jeopardies and economic benefits. And then the monitoring and safety status of wind turbines icing is analyzed, which involves collecting the relevant research on anti-de-icing in wind power generation, introducing various anti/de-icing technologies, and analyzing the principle of icing defense. Finally, this paper summarizes wind turbine icing and its defense technologies, and puts forward the future research direction based on the existing problems of wind power generation icing. [ABSTRACT FROM AUTHOR]

Many countries have a variety of offshore and onshore wind turbines that face extreme aging challenges. Issues with harmful vibrations that must be minimized are addressed in this paper. A new method of wind turbine tower vibration control using piezoelectricity and shunt circuits is proposed in this paper. The passive vibration control method is shown to improve the tower's structural performance under various environmental loads, like wind and seismic excitations. To examine the effectiveness of the suggested shunted piezoelectric system, a simple surrogate finite element model of a wind turbine tower is considered, and various investigations at the second eigenfrequency are carried out. An alternative way of modeling the studied structure is considered and the results demonstrate better performance. The advantages of setting up structural damping systems for decreasing tower vibrational loads and boosting their structural stability and resilience against extreme events are highlighted throughout this work. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive review of advanced control methods specifically designed for floating offshore wind turbines (FOWTs) above the rated wind speed. Focusing on primary control objectives, including power regulation at rated values, platform pitch mitigation, and structural load reduction, this paper begins by outlining the requirements and challenges inherent in FOWT control systems. It delves into the fundamental aspects of the FOWT system control framework, thereby highlighting challenges, control objectives, and conventional methods derived from bottom-fixed wind turbines. Our review then categorizes advanced control methods above the rated wind speed into three distinct approaches: model-based control, data-driven model-based control, and data-driven model-free control. Each approach is examined in terms of its specific strengths and weaknesses in practical application. The insights provided in this review contribute to a deeper understanding of the dynamic landscape of control strategies for FOWTs, thus offering guidance for researchers and practitioners in the field. [ABSTRACT FROM AUTHOR]

This paper studies the THD and AC losses on the DC cables of offshore wind farm-based multi-terminal HVDC systems when they extract and deliver power from and to more than one connection point. In the paper, the study of a full system PLECS + Simulink model with two branches, including a wind resource, a wind turbine, a Permanent Magnet Synchronous Generator (PMSG), a Pulse Width Modulation (PWM) rectifier, a Single Active Bridge (SAB) DC–DC converter, an Input Parallel Output Series (IPOS) DC–DC converter, HVDC cables, and a simplified onshore system, is presented. It focuses on the investigation of the output ripple content of multiple DC–DC converters on DC cables under different wind conditions with different voltage and power ratings. The importance of the study is providing a general understanding of the operation of the innovative offshore wind farm-based DC system, as well as the interaction between different DC–DC converters and their influence on cable ripple content under different situations. [ABSTRACT FROM AUTHOR]

Wind turbine energy generators operate in a variety of environments and often under harsh operational conditions, which can result in the mechanical failure of wind turbines. In order to ensure the efficient operation of wind turbines, the detection of any abnormality in the mechanics is particularly important. In this paper, a method for detecting abnormalities in the bearings of wind turbine energy generators, based on the cascade deep learning model, is proposed. First, data on the mechanics of wind turbine generators were collected, and the correlation between the data was studied in order to select the parameters related to the bearing temperature. Then, the logical relationship between the observation parameters and the target parameters was established based on a one-dimensional convolutional neural network (CNN) and a long short-term memory (LSTM) network, and the difference between the predicted temperature and the actual temperature was assessed using the root mean square error evaluation model. Finally, a numerical example was used to verify the operational data from a wind farm unit in northwest China. The results show that the CNN-LSTM model proposed in this paper can detect abnormalities earlier in the state of the main bearing than the LSTM model, and the CNN-LSTM model can detect abnormalities in the main bearing that the LSTM network cannot find. [ABSTRACT FROM AUTHOR]

In South Korea, Renewable Energy Sources (RES) have been increasing with the application of energy policies, such as Feed in Tariff (FIT) and the Renewable Portfolio Standard (RPS). However, a rapid increase in RES supply leads to an uncertain power supply due to the intermittent output of RES. A representative example is the curtailment of Wind Turbines (WT), which frequently occurs in Jeju Island, South Korea. The proportion of RES power on Jeju Island is 67%, and there are cases where WT is curtailed among the operable sections to maintain the balance of power supply and demand. This paper applies Power-to-Gas (P2G) technology to hydrogenate, store, and utilize unused power to solve this problem. In this paper, Aewol-eup in Jeju Island is selected as a target site for case study. An Integrated Energy System (IES) for various energy operations is designed to control RES output. This paper proposes the optimal facility configuration and finally drives the optimal design and operation solution of IES by analyzing the objective functions and focusing on the Independent Power Producer (IPP) perspective. [ABSTRACT FROM AUTHOR]

Traditional machine learning prediction methods usually only predict input parameters through a single model, so the problem of low prediction accuracy is common. Different predictive models extract different information for input, and combining different predictive models can make as much use as possible of all the information contained in the inputs. Therefore, this paper improves the existing oil temperature prediction method of offshore wind turbine gearboxes, and for the actual prediction effect of Supervisory Control And Data Acquisition (SCADA) data in this paper, Bayesian-optimized Light Gradient Boosting Machine (LightGBM) and eXtreme Gradient Boosting(XGBoost) machine learning models are selected to be combined. A method based on the Induced Ordered Weighted Average (IOWA) operator combination prediction model is thus proposed, with simulation results showing that the proposed model improves the accuracy of gearbox condition monitoring. The innovation of this article lies in considering the various negative impacts faced by actual offshore wind turbines and adopting a combination prediction model to improve the accuracy of gearbox condition monitoring. [ABSTRACT FROM AUTHOR]

As the penetration of renewable energy sources into a power system increases, the significance of precise short-term forecasts for wind power generation becomes paramount. However, the erratic and non-periodic nature of wind poses challenges in accurately predicting the output. This paper presents a comprehensive investigation into forecasting wind power generation for the following day, using three machine learning models: long short-term memory (LSTM), convolutional neural network-bidirectional LSTM (CNN-biLSTM), and light gradient boosting machine (LGBM). In addition, this paper proposes a method to improve the prediction performance of LGBM by separating data according to the distribution of features, and training and testing each separated dataset with a distinct model. This study includes a comparative analysis of the performance of the proposed models in predicting wind turbine output, offering valuable insights into their respective efficiencies. The results of this investigation were analyzed for two geographically distinct wind farms (Korea and the UK). The findings of this study are expected to facilitate the selection of efficient prediction models within the forecast accuracy auxiliary service market and assist grid operators in ensuring reliable power supply for the grid. [ABSTRACT FROM AUTHOR]

Offshore wind energy is considered as one of the most promising resources of clean and renewable energy to replace fossil fuels. Additionally, its cost is expected to be lower than onshore wind energy as the technology matures. Offshore wind turbines (OWTs) normally operate in harsh ocean environments, which could impact their structural integrity. Scour erosion around foundations of OWTs can substantially change the overall stiffness of these structures and shorten their lifetime. Currently, there are a limited number of studies on countermeasures and their engineering requirements for decreasing the scouring effect; this is due to their different hydraulic circumstances, such as their stability, reliability, and resistance capacity. To this end, advancements in scour protection measures in the offshore energy sector are evaluated in this paper through a thorough and critical review following the PRISMA systematic literature mapping approach. This includes 68 papers on scour protection and over 30 scour protection designs for various types of wind turbine foundations. Here, we aim to provide an overview of the latest scouring protection measures and their comprehensive assessment, as well as their prospects and future challenges. The findings of this study will provide key insights into scour protection measures for OWTs and will subsequently contribute to the future growth of the offshore renewable energy sector. [ABSTRACT FROM AUTHOR]

The design optimization of a direct-drive permanent magnet synchronous generator (DDPMSG) is of great significance for wind turbines because of its unique advantages. This paper proposes a two-stage model to realize multi-objective design optimization for a 6 MW DDPMSG. In the first stage, a surrogate optimized response surface model based on an improved sparrow search algorithm (ISSA) was established for modeling the cogging torque and generator efficiency. In the second-stage model, a multi-objective optimization model is proposed to optimize the cogging torque and generator efficiency of the DDPMSG. Finally, the proposed two-stage model was used for a 6 MW DDPMSG design optimization, and the simulation results demonstrated the superiority and rationality of the proposed model. In the first-stage model, the proposed surrogate model based on the ISSA had a better modeling accuracy and lower errors. Compared with traditional response surface models and correlation analysis models, the proposed optimized surrogate model reduced errors in the cogging torque by 34.63% and 42.97%, respectively, while the errors in the efficiency models were reduced by 12.92% and 60.78%, respectively, which indicates the superiority of the first-stage model. In the second stage, compared with the single-objective optimization model, the multi-objective optimization model achieved a trade-off optimization between the cogging torque and the efficiency. Compared with the cogging torque optimization model, the proposed model optimized the efficiency by 101.41%. Compared with the efficiency optimization model, the proposed model reduced the cogging torque by 16.67%. These results verified the superiority and rationality of the second-stage model. [ABSTRACT FROM AUTHOR]

Small wind turbines operating in low wind speed regions have not had any significant success. In addition, small wind speed regions occupy a large area of the world, so they represent a potential area for installing small wind turbines in the future. In this paper, a method to design an efficient airfoil for small wind turbines in low wind speed conditions using XFLR5 and CFD simulations is implemented. Because the impact of the airflow on the blade surface under low Re number conditions can change suddenly for small geometries, designing the airfoil shape to optimize the aerodynamic performance is essential. The tuning of the key geometric parameters using inversion techniques for better aerodynamic performance is presented in this study. A two-dimensional model was used to consider the airflow on the airfoil surface with differences in the angle of attack. The original S1010 airfoil was used to design a new airfoil for increasing the aerodynamic efficiency by using V6.57 XFLR5 software. Subsequently, the new VAST-EPU-S1010 airfoil model was adjusted to the maximum thickness and the maximum thickness position. It was simulated in low wind speed conditions of 4–6 m/s by a computational fluid dynamics simulation. The lift coefficient, drag coefficient, and CL/CD coefficient ratio were evaluated under the effect of the angle of attack and the maximum thickness by using the k-ε model. The simulation results show that the VAST-EPU-S1010 airfoil achieved the greatest aerodynamic efficiency at an angle of attack of 3°, a maximum thickness of 8%, and a maximum thickness position of 20.32%. The maximum value of CL/CD of the new airfoil at 6 m/s was higher than at 4 m/s by about 6.25%. [ABSTRACT FROM AUTHOR]

To ensure the stable operation of a wind turbine generator system when the wind speed exceeds the rated value and address the issue of excessive rotor speed during high wind speeds, this paper proposes a novel variable pitch controller strategy based on a back-propagation neural network and optimal control theory to solve this problem. Firstly, a mathematical model for the wind turbine is established and linearized. Then, each optimal sub-controller is designed for different wind speed conditions by optimal theory. Subsequently, a back-propagation neural network is utilized to learn the variation pattern of controller parameters with respect to wind speed. Finally, real-time changes in wind speed are applied to evaluate and adjust controller parameters using the trained back-propagation neural network. The model is simulated in MATLAB 2019b, real-time data are observed, and the control effect is compared with that of a Takagi–Sugeno optimal controller, firefly algorithm optimal controller and fuzzy controller. The simulation results show that the rotor speed overshoot of the optimal controller under the step wind speed is the smallest, only 0.05 rad/s. Under other wind speed conditions, the rotor speed range fluctuates around 4.35 rad/s, and the fluctuation size is less than 0.2 rad/s, which is much smaller than the fluctuation range of other controllers. It can be seen that the back-propagation optimal controller can ensure the stability of the rotor speed above the rated wind speed. At the same time, it has better control accuracy compared to other controllers. [ABSTRACT FROM AUTHOR]

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

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

The wind energy sector is experiencing rapid growth, marked by the expansion of wind farms and the development of large-scale turbines. However, conventional manual methods for wind turbine operations and maintenance are struggling to keep pace with this development, encountering challenges related to quality, efficiency, and safety. In response, unmanned aerial vehicles (UAVs) have emerged as a promising technology offering capabilities to effectively and economically perform these tasks. This paper provides a review of state-of-the-art research and applications of UAVs in wind turbine blade damage detection, operations, and maintenance. It encompasses various topics, such as optical and thermal UAV image-based inspections, integration with robots or embedded systems for damage detection, and the design of autonomous UAV flight planning. By synthesizing existing knowledge and identifying key areas for future research, this review aims to contribute insights for advancing the digitalization and intelligence of wind energy operations. [ABSTRACT FROM AUTHOR]

Microhydraulic turbines offer a promising solution for decentralized energy production, suitable for both grid-connected and standalone applications, due to their compactness and high efficiency. This paper introduces a control approach for such systems employing microhydraulic turbines as distributed generators (DGs), utilizing six-phase induction generators for electricity production. This study emphasizes control strategies for both grid-connected and standalone modes utilizing proportional-integral (PI) controllers. An integrated energy storage system based on Li-Ion battery technology is also implemented to store the excess energy and compensate for production deficits to meet demand. The results obtained using MATLAB/Simulink demonstrate efficient and reliable power management among production sources, the grid and the local load, highlighting the unique contribution of employing a six-phase induction generator with the energy storage system. [ABSTRACT FROM AUTHOR]

This paper is the second in a two-part series presenting preliminary results on a passively controlled wind turbine rotor system using a flexible curved blade concept. Building on the initial findings, this segment explores the application of glass-fiber-reinforced polymer (GFRP) composites with strategically oriented layers to enhance blade flexibility and aerodynamic performance and ensure operational safety. Previously, we demonstrated that flexible blades fabricated from isotropic materials with an NACA4415 airfoil profile could self-regulate rotor RPM and power output in response to aerodynamic loads, offering a glimpse of controlled operational behavior, in contrast to straight blades of similar material geometry and aerodynamic characteristics. However, they did not fully meet the design objectives, particularly in achieving nominal power at the intended wind speeds and in safely halting operation at high wind speeds. The current study employs a GFRP blade with a simpler, flat geometry due to manufacturing constraints, diverging from traditional airfoil contours to focus on material behavior under aerodynamic loads. Despite these changes, the blade exhibited all desired operational characteristics: quick startup, stable power output across operational wind speeds, and effective shutdown mechanisms at high speeds. This success illustrates the potential of passively controlled blades designed with appropriately oriented composite layers. Challenges with load application methods—that were identified in the first installment—were addressed by adopting a generator connected to a rheostat, offering improved control over load variations compared to the mechanical brakes used previously. This advancement enabled more consistent data collection, particularly at lower Tip–Speed Ratio (TSR) values, although real-time control for maximum power point tracking was still out of reach. These findings not only confirm the effectiveness of the flexible blade concept but also highlight the need for further refinement in blade design and testing methodology to optimize performance and ease of manufacturing. Future work will continue to refine these designs and explore their scalability and economic viability for broader applications in wind energy technology and in particular to those of small Wind Energy Converter Systems (WECSs). [ABSTRACT FROM AUTHOR]

Currently, most studies on the optimization of wind farm layouts on flat terrain employ a discrete grid-based arrangement method and result in irregular layouts that may damage the visual appeal of wind farms. To meet the practical requirements of wind farms, a two-step optimization method called "grid–coordinate" based on a genetic algorithm is proposed in this paper. The core idea is to initially determine the number of wind turbines and their initial positions using a grid-based approach, followed by a fine-tuning of the wind farm layout by moving the turbines in a row/column manner. This two-step process not only achieves an aesthetically pleasing arrangement but also maximizes power generation. This algorithm is conducted to optimize a 2 km × 2 km wind farm under three classic wind conditions, one improved wind condition, and a real wind condition employing both the Jensen and Gaussian wake models. To validate the effectiveness of the proposed method, the optimization of configurations based on different wake models was conducted, yielding results including the efficiency, total power output, number of wind turbines, and unit cost of electricity generation. These results were compared and analyzed against the classical literature. The findings indicate that the unit cost of electricity generation using the two-step optimization approach with the Gaussian wake model is higher than that of the initial grid optimization method. Additionally, varying the number of wind turbines can lead to instances of high power generation coupled with low efficiency. This phenomenon should be carefully considered in the wind farm layout optimization process. [ABSTRACT FROM AUTHOR]

This paper explores an interesting approach to wind energy technology, focusing on a novel type of drag-driven vertical-axis wind turbines (VAWTs). Studied geometries employ rotor-shaped cross-sections, presenting a distinctive approach to harnessing wind energy efficiently. The rotor-shaped cross-section geometries are examined for their aerodynamic efficiency, showcasing the meticulous engineering behind this innovation. The drag-driven turbine shapes are analyzed for their ability to maximize energy extraction in a variety of wind conditions. A significant aspect of these turbines is their adaptability for diverse applications. This article discusses the feasibility and advantages of utilizing these VAWTs in fence configurations, offering an innovative integration of renewable energy generation with physical infrastructure. The scalability of the turbines is highlighted, enabling their deployment as a fence around residential properties or as separators between highway lanes and as energy-generating structures atop buildings. The scientific findings presented in this article contribute valuable insights into the technological advancements of rotor-shaped VAWTs and their potential impact on decentralized wind energy generation. The scalable and versatile nature of these turbines opens up new possibilities for sustainable energy solutions in both urban and residential settings, marking a significant step forward in the field of renewable energy research and technology. In particular, it was shown that among the proposed rotor geometries, the five-blade rotor was characterized by the highest efficiency and, working in a palisade configuration with a spacing of 10 mm to 20 mm, produced higher average values of the torque coefficient than the corresponding Savonius turbine. [ABSTRACT FROM AUTHOR]

The effective exploitation of renewable energy sources is one of the most effective solutions to counter the energy, environmental and economic problems associated with the use of fossil fuels. Small-scale wind turbines (converting wind energy into electric energy with a power output lower than 50 kW) have received tremendous attention over the past few decades thanks to their reduced environmental impact, high efficiency, low maintenance cost, high reliability, wide wind operation range, self-starting capability at low wind speed, limited installation space, reduced dependence on grid-connected power and long transmission lines, low capital costs, as well as the possibility to be installed in some urban settings. However, there are significant challenges and drawbacks associated with this technology from many different perspectives, including the significant discrepancy between theoretical performance data provided by the manufacturers and real field operation, that need to be investigated in greater depth in order to enable a more widespread deployment of small-scale wind turbines. In this review, a complete and updated list of more than 200 commercially available small-scale horizontal and vertical wind turbine models is provided and analysed, detailing the corresponding characteristics in terms of the number and material of blades, start-up wind speed, cut-in wind speed, cut-out wind speed, survival wind speed, maximum power, noise level, rotor diameter, turbine length, tower height, and specific capital cost. In addition, several scientific papers focusing on the experimental assessment of field performance of commercially available small-scale horizontal and vertical wind turbines have been reviewed and the corresponding measured data have been compared with the rated performance derived from the manufacturers' datasheets in order to underline the discrepancies. This review represents an opportunity for the scientific community to have a clear and up-to-date picture of small-scale horizontal as well as vertical wind turbines on the market today, with a precise summary of their geometric, performance, and economic characteristics, which can enable a more accurate and informed choice of the wind turbine to be used depending on the application. It also describes the differences between theoretical and in-situ performance, emphasizing the need for further experimental research and highlighting the direction in which future studies should be directed for more efficient design and use of building-integrated small-scale wind turbines. [ABSTRACT FROM AUTHOR]

This paper proposes the development of an active control system to control the power output of a low-power horizontal-axis wind turbine (HAWT) when operating at wind speeds above the rated wind speed. The system is composed of an active articulated vane (AAV) in charge of the orientation of the wind turbine, which is driven by an electric actuator that changes the angle of the AAV to maintain a constant power output. Compared with the passive power regulation systems most often used in low-power HAWTs, active systems allow for better control and, therefore, greater stability of the delivered power, which reduces the structural stresses and allows for controlled braking in any wind condition or during system failures. The control system was designed and simulated using MATLAB R2022b software, and then built and evaluated under laboratory conditions. For the control design, the transfer function (TF) between the pulse width modulation (PWM) and the AAV angle (θ) was determined via laboratory tests using MATLAB's PIDTurner tool. For the simulation, the relationship between the power output and the AAV angle was determined using the vector decomposition of the wind speed and wind rotor area. Wind speed step and ramp response tests were performed for proportional–integral–derivative (PID) control. The results obtained demonstrate the technical feasibility of this type of control, obtaining settling times (ts) of 6.7 s in the step response and 2.8 s in the ramp response. [ABSTRACT FROM AUTHOR]

In accordance with South Korea's recent 2030 Carbon Neutral Plan, an 8GW offshore wind farm is planned for construction in the South-west Sea. Therefore, it is expected that large-scale wind turbines will be installed, and these turbines must operate stably, even when there are instantaneous voltage fluctuations in the power system. The grid code is described for the low-voltage-ride-through (LVRT) and high-voltage-ride-through (HVRT) functions, and a test facility that can perform both LVRT and HVRT tests is essential. In the case of LVRT/HVRT test facilities developed by the existing RLC (impedance component) method, it may be difficult to test large-scale wind turbines due to problems such as power quality, frequent failures and narrow short-circuit capacity ranges. Therefore, to solve such problems, this paper proposes an LVRT/HVRT test facility of the autotransformer type, which is capable of outputting the desired voltage range by changing the wiring method and tap position. Specifically, in order to implement the test facility of the autotransformer type, which is able to output the desired voltage range by changing the wiring method and tap according to the LVRT/HVRT test status, this paper presents an impedance determination algorithm (two-step layer impedance determination algorithm) of auto-transformer based on the fault-current analysis and operation strategy at a real LVRT/HVRT testing evaluation facility. [ABSTRACT FROM AUTHOR]

To face the growing electricity demand, several countries have adopted the solution of clean energy and use renewable energy sources (e.g., wind and solar) to reinforce the stability of the power network, especially during peak demand periods. Forecasting wind power generation is one of the important tasks for the network regulator. This paper deals with the probabilistic forecasting of hourly wind speed time series. In this approach, instead of evaluating a single-point forecast, an intraday interval prediction is provided, which allows modeling the probability distribution of the wind speed process at any specific hour. Practically, the quantification of uncertainty might be of particular interest for risk management purposes associated with wind power generation. The definition of interval prediction is based on the notion of conditional quantiles. In this paper, we introduce a new statistical approach, which deals with the nonstationarity behavior of the wind speed process, to define the conditional quantile predictor. The proposed approach was applied and evaluated on hourly wind speed processes. The suggested methodology provides accurate single-point forecasts using the conditional median as the predictor. Furthermore, the obtained hourly interval predictions are small and well adapted to the shape of the daily wind speed curves, which confirms the efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

In order to improve the reliability and life of the wind turbine, this paper takes the rolling bearing in the experimental platform of the wind turbine as the research object. In order to obtain the intrinsic mode function (IMF) of each fault type, the original signals of different fault states of the rolling bearing on the experimental platform are decomposed by using the overall average empirical mode decomposition method (EEMD) and the wavelet packet decomposition method (WPD), respectively. Then the energy ratio of the IMF component of the different types of faults to the total energy value is calculated and the eigenvectors of different types of faults are constructed. The extreme learning machine (ELM) and probabilistic neural network (PNN) are used to learn fault types and eigenvector samples to identify the faults of the rolling bearing. It is found that the bearing fault characteristics obtained by the WPD method are more obvious, and the results obtained by the same recognition method are ideal; and the PNN method is obviously superior to the extreme learning machine method in bearing fault recognition rate. [ABSTRACT FROM AUTHOR]

The stability of offshore floating wind turbine foundation platforms is a fundamental requirement for the efficiency and safety of wind power generation systems. This paper proposes a novel small-diameter float-type semi-submersible platform to improve system stability. To evaluate the superior motion stability of the proposed floating platform, a comprehensive frequency–domain response analysis and experimental study were conducted in comparison with the OC4-DeepCwind platform developed by the National Renewable Energy Laboratory (NREL). The respective comparison of the frequency–domain response analysis and the experimental results demonstrated that the proposed floating wind turbine platform shows better hydrodynamic characteristics and resonance avoidance capability. This not only reduces the Response Amplitude Operators (RAOs), but also enhances the system stability, namely, effectively avoiding the regions of concentrated wave loading and low-frequency ranges. Furthermore, the proposed small-diameter semi-submersible platform has the potential to reduce manufacturing costs, providing valuable insights for the manufacturing of offshore floating wind turbine systems. [ABSTRACT FROM AUTHOR]

Aerodynamic noise emitted by small wind turbines is a concern due to their proximity to urban environments. Broadband airfoil self-noise has been found to be the major source, and several studies have discussed techniques to reduce airfoil leading-edge and trailing-edge noises. Reduction mechanisms inspired by owl wings and their airfoil sections were found to be most effective. However, their effect/s on the tip vortex noise remain underexplored. Therefore, this paper investigates the effects of implementing an owl airfoil design on the tip vortex noise generated by the National Renewable Energy Laboratory (NREL) Phase VI wind turbine to gain an understanding of the relationship, if any, between airfoil design and the tip vortex noise mechanism. Numerical prediction of aeroacoustics is employed using the Ansys Fluent Broadband Noise Sources function for airfoil self-noise radiation. Detailed comparisons and evaluations of the generated acoustic power levels (APLs) for two distinguished inlet velocities were made with no loss in torque. Although the owl airfoil design increased the maximum generated APL by the baseline model from 105 dB to 110 dB at the lower inlet velocity, it significantly reduced the surface area generating the noise, and reduced the maximum APL generated by the baseline model by 4 dB as the inlet velocity increased. The ability of the owl airfoil to mitigate the velocity effects along the span of the blade was found to be its main noise reduction mechanism. [ABSTRACT FROM AUTHOR]

In large-scale wind farms, the voltage fluctuations caused by the uncertainty of wind speed at the turbine terminals pose a pressing challenge. This article presents a localized voltage control strategy tailored toward rapid adjustments in turbine terminal voltage in wind turbine generators. Based on relative voltage observation, this strategy achieves voltage coordination between the high and low ends of the transformer in wind turbine generators. Firstly, the overall structure of the wind farm and the characteristics of terminal voltage are analyzed. Secondly, the principles and feasibility of the relative voltage control strategy are examined. Finally, the effectiveness of the proposed control strategy is validated through simulation results from a specific wind farm. The results demonstrate its capability to achieve a fast and stable voltage dynamic response within the wind farm based on local information, thus mitigating the risk of voltage out of limit. [ABSTRACT FROM AUTHOR]

Simplified methods are often used for load estimations during the initial design of the foundations of offshore wind turbines (OWTs). However, the reliability of simplified methods for designing different OWTs needs to be studied. This paper provides a comparative study to evaluate the reliability of simplified approaches. The foundation loads are calculated for OWTs at the mudline level using a simplified approach and OpenFAST simulations and compared. Three OWTs, NREL 5 MW, DTU 10 MW, and IEA 15 MW, are used as reference models. An Extreme Turbulence Model wind load at a rated wind speed, combined with a 50-year Extreme Wave Height (EWH) and Extreme Operating Gust (EOG) wind load and a 1-year maximum wave height are used as the load combinations in this study. In addition, the extreme loads are calculated using both approaches for various metocean data from five different wind farms. Further, the pile penetration lengths calculated using the mudline loads via two methods are compared. The results show that the simplified method provides conservative results for the estimated loads compared to the OpenFAST results, where the extent of conservativism is studied. For example, the bending moment and shear force at the mudline using the simplified approach are 23% to 69% and 32% to 53% higher compared to the OpenFAST results, respectively. In addition, the results show that the simplified approach can be effectively used during the initial phases of monopile foundation design by using factors such as 1.5 and 2 for the shear force and bending moment, respectively. [ABSTRACT FROM AUTHOR]

Icing on the blades of wind turbines during winter seasons causes a reduction in power and revenue losses. The prediction of icing before it occurs has the potential to enable mitigating actions to reduce ice accumulation. This paper presents a framework for the prediction of icing on wind turbines based on Supervisory Control and Data Acquisition (SCADA) data without requiring the installation of any additional icing sensors on the turbines. A Temporal Convolutional Network is considered as the model to predict icing from the SCADA data time series. All aspects of the icing prediction framework are described, including the necessary data preprocessing, the labeling of SCADA data for icing conditions, the selection of informative icing features or variables in SCADA data, and the design of a Temporal Convolutional Network as the prediction model. Two performance metrics to evaluate the prediction outcome are presented. Using SCADA data from an actual wind turbine, the model achieves an average prediction accuracy of 77.6 % for future times of up to 48 h. [ABSTRACT FROM AUTHOR]

In recent trends, a rising demand for renewable energy has driven wind turbines to larger proportions, where lighter blade designs are often adopted to reduce the costs associated with logistics and production. This causes modern utility-scale wind turbine blades to be inherently more flexible, and their amplified aeroelastic sensitivity results in complex multi-physics reactions to variant atmospheric conditions, including dynamic patterns of aerodynamic loading at the rotor and vortex structure evolutions within the wake. In this paper, we analyze the influence of inflow variance for wind turbines with large, flexible rotors through simulations of the National Rotor Testbed (NRT) turbine, located at Sandia National Labs' Scaled Wind Farm Technology (SWiFT) facility in Lubbock, Texas. The Common Ordinary Differential Equation Framework (CODEF) modeling suite is used to simulate wind turbine aeroelastic oscillatory behavior and wind farm vortex wake interactions for a range of flexible NRT blade variations, operating in differing conditions of variant atmospheric flow. CODEF solutions of turbine operation in Steady-In-The-Average (SITA) wind conditions are compared to SITA wind conditions featuring a controlled gust-like pulse overimposed, to isolate the effects of typical wind fluctuations. Finally, simulations of realistic time-varying wind conditions from SWiFT meteorological tower measurements are compared to the solutions of SITA wind conditions. These increasingly complex atmospheric inflow variations are tested to show the differing effects evoked by various patterns of spatiotemporal atmospheric flow fluctuations. An analysis is presented for solutions of wind turbine aeroelastic response and vortex wake evolution, to elucidate the consequences of variant inflow, which pertain to wind turbine dynamics at an individual and farm-collective scale. The comparisons of simulated farm flow for SITA and measured fluctuating wind conditions show that certain regions of the wake contain up to a 12% difference in normalized axial velocity, due to the introduction of wind fluctuations. The findings of this study prove valuable for practical applications in wind farm control and optimization strategies, with particular significance for modern utility-scale wind power plants operating in variant atmospheric conditions. [ABSTRACT FROM AUTHOR]

With the large-scale use of wind turbines, the ability of the power system to resist frequency fluctuations has been greatly weakened, making the contradiction between frequency regulation supply and demand in the power system increasingly prominent. In order to ensure the frequency security of the power system, this paper conducts research on the primary frequency regulation (PR) backup configuration problem of a power system containing a high proportion of wind power. First, according to the dynamic properties of the speed regulator, a system frequency response model considering the limiting link is established. And the system frequency response model is transformed into a time-domain analytical function of PR reserve capacity and frequency maximum value. On this basis, a PR backup configuration model of a power system containing a high proportion of wind power is constructed with the optimization goal of minimizing system operating costs and taking into account the limiting link constraints. It is proposed to use the L-shape algorithm to decompose the model into main problems and sub-problems, which effectively reduces the solution complexity of the model. Finally, the correctness and effectiveness of this model are verified based on the improved IEEE 39-bus system. [ABSTRACT FROM AUTHOR]

In recent years, morphing wings have become not only a concept, but an aerodynamic solution for the aviation industry to take a step forward toward future technologies. However, continuously morphing airfoils became an interesting answer to provide green energy solutions. In this paper, the authors conducted experimental research on a continuously camber-morphing airfoil using the Particle Image Velocimetry (PIV) and Computational Fluid Dynamics (CFD) methods. The main objective of this work was to research a variety of morphing airfoils with different camber deflections. An average velocity distribution and turbulence distribution were compared and are discussed. The two-dimensional PIV results were compared to the CFD simulations to validate the numerical method's accuracy and obtain the aerodynamic coefficient's trends. A further comparison revealed that morphing airfoils have better aerodynamic performance than conventional airfoils for very low camber deflections and create substantial amounts of drag for significant camber deflections. [ABSTRACT FROM AUTHOR]

The complex environmental impact makes it difficult to predict wind speed with high precision for multiple wind turbines. Most existing research methods model the temporal dependence of wind speeds, ignoring the spatial correlation between wind turbines. In this paper, we propose a multi-wind turbine wind speed prediction model based on Weighted Diffusion Graph Convolution and Gated Attention Network (WDGCGAN). To address the strong nonlinear correlation problem among multiple wind turbines, we use the maximal information coefficient (MIC) method to calculate the correlation weights between wind turbines and construct a weighted graph for multiple wind turbines. Next, by applying Diffusion Graph Convolution (DGC) transformation to the weight matrix of the weighted graph, we obtain the spatial graph diffusion matrix of the wind farm to aggregate the high-order neighborhood information of the graph nodes. Finally, by combining the DGC with the gated attention recurrent unit (GAU), we establish a spatio-temporal model for multi-turbine wind speed prediction. Experiments on the wind farm data in Massachusetts show that the proposed method can effectively aggregate the spatio-temporal information of wind turbine nodes and improve the prediction accuracy of multiple wind speeds. In the 1h prediction task, the average RMSE of the proposed model is 28% and 33.1% lower than that of the Long Short-Term Memory Network (LSTM) and Convolutional Neural Network (CNN), respectively. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to estimating short-term production of wind farms, which are made up of numerous turbine generators. It harnesses the power of big data through a blend of data-driven and model-based methods. Specifically, it combines an Artificial Neural Network (ANN) for immediate future predictions of wind turbine power output with a stochastic model for dependability, using Hybrid Reliability Block Diagrams. A thorough state-of-the-art review has been conducted in order to demonstrate the applicability of an ANN for non-linear stochastic problems of energy or power forecast estimation. The study leverages an innovative cluster analysis to group wind turbines and reduce the computational effort of the ANN, with a dependability model that improves the accuracy of the data-driven output estimation. Therefore, the main novelty is the employment of a hybrid model that combines an ANN with a dependability stochastic model that accounts for the realistic operational scenarios of wind turbines, including their susceptibility to random shutdowns This approach marks a significant advancement in the field, introducing a methodology which can aid the design and the power production forecast. The research has been applied to a case study of a 24 MW wind farm located in the south of Italy, characterized by 28 turbines. The findings demonstrate that the integrated model significantly enhances short-term wind-energy production estimation, achieving a 480% improvement in accuracy over the solo-clustering approach. [ABSTRACT FROM AUTHOR]

With the continuous warming of the global climate, expanding the use of renewable energy has become one of the main social responsibilities. However, as the number of installed wind turbines and their physical dimensions continue to increase, the issue of generated noise has become increasingly significant in influencing the acceptance and endorsement of wind power projects by neighboring communities. In this paper, we investigated the noise generated by two wind turbine units with rated powers of 1.5 MW and 4.5 MW and analyzed the variations in low-frequency noise during their operation and shutdown periods. This research shows that the power of a single unit has a significant impact on the low-frequency noise emitted into the environment. Compared with 1.5 MW wind turbines, 4.5 MW wind turbines generate more low-frequency noise when operating at the same wind speed. Further analysis of the narrowband frequency spectra and one-third octave spectra of the measured noise indicates that the low-frequency noise from the 4.5 MW wind turbine increases significantly in the range of approximately 80 Hz to 300 Hz, with more pronounced variations below 250 Hz corresponding to changes in wind speed. However, the overall variations in low-frequency noise with wind speed are not as notable as those observed for the 1.5 MW wind turbine. Due to the relatively weak attenuation of low-frequency noise in the atmosphere, the higher low-frequency content of large wind turbines may cause more distress to residents near wind farms. The result of this study emphasizes that in the planning and design of wind power projects, in addition to considering the efficiency of single-unit power generation and the contribution of renewable energy, it is also necessary to pay full attention to noise emission issues to ensure that the project is widely supported and accepted in the community. [ABSTRACT FROM AUTHOR]

Scouring around the fixed foundations of offshore wind turbines (OWTs) remains a pressing concern within the offshore wind industry, significantly jeopardizing OWT safety. Despite previous efforts, the current understanding of this phenomenon remains incomplete, as various factors related to offshore wind farms and OWT operations can exert intricate influences. To further explore this matter, this paper undertakes a new study to further understand the interplay between monopile vibrations, tidal currents, and sea waves, elucidating their combined impact on scour surrounding an OWT's foundation. It was found that, besides pile vibration amplitude and frequency, the pile vibration direction can also notably influence the scour around the monopile. Specifically, the most pronounced scour mostly transpires when the monopile undergoes vibrations along the 135° direction relative to the inflow direction. The mildest scour mostly occurs when the pile vibrates along the 90° direction. Additionally, this study reveals that different types of waves, indicated by the ratio of water depth, H, to wavelength, L, also engender varying scour around the monopile. The influence of waves on scour increases with the decrease in the H/L ratio, implying that OWT foundations are more threatened by shallow water waves than by deep water waves. [ABSTRACT FROM AUTHOR]

Blade icing seriously affects wind turbines' aerodynamic performance and output power. Timely and accurately predicting blade icing status is crucial to improving the economy and safety of wind farms. However, existing blade icing prediction methods cannot effectively solve the problems of unbalanced icing/non-icing data and low prediction accuracy. In order to solve the above problems, this paper proposes a wind turbine blade icing prediction method based on the focal loss function and CNN-Attention-GRU. First, the recursive feature elimination method combined with the physical mechanism of icing is used to extract features highly correlated with blade icing, and a new feature subset is formed through a sliding window algorithm. Then, the focal loss function is utilized to assign more weight to the ice samples with a lower proportion, addressing the significant class imbalance between the ice and non-ice categories. Finally, based on the CNN-Attention-GRU algorithm, a blade icing prediction model is established using continuous 24-h historical data as the input and the icing status of the next 24 h as the output. The model is compared with advanced neural network models. The results show that the proposed method improves the prediction accuracy and F1 score by an average of 6.41% and 4.27%, respectively, demonstrating the accuracy and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper publishes the experimental performance results of a novel small dual−stage wind turbine at different speeds under wind tunnel testing. The results confirm its superiority at low wind speeds <7 m/s, which is consistent with natural wind conditions in most countries around the world. At lower wind speeds, the efficiency of the small, dual−stage wind turbine is higher than that of a conventional single−stage turbine. At wind speeds of <6 m/s, the novel dual−stage wind turbine always produces approximately 2.5 times more power than a single−stage wind turbine; the power coefficient is greater than 0.4. Thanks to this feature, the novel small, dual−stage wind turbine is effective in practical applications. For windy areas in Vietnam, the power generated over the same amount of time by the novel dual−stage wind turbine can be more than double that of a single−stage turbine. Next, a standard CFD model is established and validated with the above experimental measurements. This method ensures the objectivity and authenticity of the computational model. This standard CFD model is applicable to a horizontal double−stage wind turbine with any configuration and blade profile. The URANS simulation method, which uses sliding meshes and the k−omega SST turbulence model, was implemented with rotational domains of the minimum thicknesses of rotational domains (in this paper, 0.09Rfront and 0.06Rrear, accordingly). This method predicts higher mechanical work for the turbine than the actual value by an interval of <10% in the same speed range as in the experiment. This is an acceptable deviation. [ABSTRACT FROM AUTHOR]

Aiming to resolve the problem where the reliability of gearbox bearings of wind turbines is easily affected by random impact, this paper puts forward a reliability evaluation method for the competitive failure of gearbox bearings of wind turbines considering the fault incubation point. Firstly, we use Weibull distribution to simulate the fault latent process of wind turbine gearbox bearings. Secondly, a natural degradation model of gearbox bearing is established based on the Wiener process. Then, we model the random impact arrival frequency and impact intensity through a homogeneous Poisson process and normal distribution, respectively. Finally, based on considering the fault incubation point, the natural degradation of the bearing, the instantaneous degradation caused by impact, and the decline in the impact resistance of the bearing, a reliability evaluation model of gearbox bearings of wind turbines is established. A high-speed bearing of a gearbox from a wind farm in northern China is selected for simulation analysis. The results show that the proposed method can better describe the reliability decline process of the gearbox bearings of wind turbines, which has a specific guiding significance for the maintenance of wind turbines. [ABSTRACT FROM AUTHOR]

Predicting the output power of wind generators is essential to improve grid flexibility, which is vulnerable to power supply variability and uncertainty. Digital twins can help predict the output of a wind turbine using a variety of environmental data generated by real-world systems. This paper dealt with the development of a physics-based output prediction model (P-bOPM) for a 10 MW floating offshore wind turbine (FOWT) for a digital twin. The wind power generator dealt with in this paper was modeled considering the NREL 5 MW standard wind turbine with a semi-submersible structure. A P-bOPM of a 10 MW FOWT for a digital twin was designed and simulated using ANSYS Twin Builder. By connecting the P-bOPM developed for the digital twin implementation with an external sensor through TCP/IP communication, it was possible to calculate the output of the wind turbine using real-time field data. As a result of evaluating the P-bOPM for various marine environments, it showed good accuracy. The digital twin equipped with the P-bOPM, which accurately reflects the variability of the offshore wind farm and can predict the output in real time, will be a great help in improving the flexibility of the power system in the future. [ABSTRACT FROM AUTHOR]

Wind farm (WF) equivalence is an effective method to achieve accurate and efficient simulation of large-scale WF. Existing equivalent models are generally suitable for one certain or very few scenarios, and have difficulty reflecting the multiple aspects of dynamic processes of WF. Aiming at these problems, this paper proposes an equivalent model of WF based on multivariate multi-scale entropy (MMSE) and multi-view clustering. Firstly, the influence of the factors on the dynamic process of the wind turbine (WT) is discussed, including control mode, wind speed and its wake effect, resistance of crowbar resistor and so on. The relationship between these factors and the dynamic equivalence of WF is analyzed. Secondly, an overview of MMSE is given, and the applicability of MMSE on WF equivalence is analyzed. On this basis, this paper proposes the extraction process of a WT clustering indicator using MMSE. Then, the multi-view fuzzy C means (MV-FCM) algorithm is used for the clustering of WTs, and the equivalent model of WF is obtained after calculating the equivalent parameters. Finally, the IEEE14 power system including WF is simulated. The results show that the equivalent model could be applied to dynamic process simulation in various fault scenarios of power systems, and the error is small when the cluster number is 4. Compared with the detailed model, the simulation time of the WF equivalent model proposed in this paper is shortened by 86%, and the simulation accuracy is improved by about 44% compared with the comparative model. [ABSTRACT FROM AUTHOR]