Advances in Wind Energy Technology

The population is often opposed to wind turbines being erected near their homes, mainly because the machines are noisy, especially at night. In an effort to establish a compromise between the needs of the people and the fulfilment of energy demands, wind turbines have the ability to switch between day and night operation by reducing the rotation speed during the night, resulting in a loss of generated power. The present study investigates simple models for noise emission, propagation, and prediction, with the objective of proposing a control system configuration that continuously adjusts the rotational speed as much as necessary until it matches sound level regulations while minimising power losses. Thus, several approaches are implemented and tested with a very large reference wind turbine. The simulation results of a reference wind turbine show that the approaches provide significant improvements in sound reduction as well as in power conversion. Full article

To protect submarine power cables from damage caused by anchoring and fishing, submarine power cables in shallow water areas are buried to a certain depth through a cable laying machine. However, limited attention has been paid to studying the stress behavior of submarine power cables while considering the effects of burial depth. In this research, static and dynamic analyses are carried out using three-dimensional numerical models performed by the OrcaFlex v11.0 to investigate the effects of burial depths on cable tension during the cable installation under various conditions. Numerical simulation results show that the peak tension of the submarine power cable increases linearly with the increase in burial depth. In addition, the burial depth can also change the tension state at the endpoint of the submarine power cable. The endpoint of the cable is in a compressed state when h < 2 m and the cable turns into a tensile state when h ≥ 2 m. Finally, genetic programming (GP) is used to analyze numerical simulation results to propose a prediction model that can be used to estimate the peak tension of the submarine power cable during cable installation under various burial depths in shallow sea areas. It should be noted that the proposed GP model is based on the analyses of numerical results; therefore, the GP model is open for further improvements as more experimental data become available. Full article

To compare and validate wind speed estimation algorithms applied to wind turbines, wind speed estimators were designed in this study, based on two methods presented in the literature, and their performance was validated using the NREL 5MW model. The first method for wind speed estimation involves a three-dimensional (3D) look-up table-based approach, constructed using drive train differential equations. The second method involves applying a continuous–discrete extended Kalman filter. To verify and compare the performance of the algorithms designed using these different methods, feed-forward control algorithms, available power estimation algorithms, and a linear quadratic regulator, based on fuzzy logic (LQRF) control algorithms, were selected and applied as verification means, using the estimated wind speed as the input. Based on the simulation results, the performance of the two methods was compared. The method using drive train differential equations demonstrated superior performance in terms of reductions in the standard deviations of rotor speed and electrical power, as well as in its prediction accuracy for the available power. Full article

In response to the problems that abnormal yaw position causes during the yawing process—on the one hand leading to the accumulation of yaw position errors, affecting the accuracy of yawing to the wind or safety due to excessive cable twisting, and on the other hand, with the phenomena of frequent position jumps or frequent short-term position maintenance generating certain yaw errors, affecting the stability of yaw control, thus resulting in a high occurrence frequency of yaw system failures and high operation and maintenance costs—a data-driven fault diagnosis method is proposed to give early warnings for abnormal conditions of the yaw position of the wind turbine unit. Firstly, for the massive data in the SCADA (Supervisory Control and Data Acquisition) system, the ReliefF feature algorithm based on standardized interaction gain (Standardized Interaction Gain and ReliefF, SIG–ReliefF) is used for accurately identifying and screening the characteristic parameters that have a greater impact on the yaw system failure of wind turbines. The advantage of this method lies in its ability to effectively consider the correlation between features and retain the relevant features and interaction features of yaw system failures to the greatest extent. Then, an Informer yaw position prediction model is established, combined with the two-stage attention mechanism (two-stage attention and Informer, TSA–Informer), and the distribution of residuals is statistically analyzed through the sliding window method to determine the fault threshold. Finally, the validity and accuracy of the proposed method are verified through examples, and through comparison with other algorithms, it is verified that it has better abnormal early warning performance. Relevant conclusions can provide a reference for the fault diagnosis of the actual yaw system. Full article

A 2-D Lattice Boltzmann Method, designed to ensure stability at high Reynolds numbers, is combined with an Actuator Line Model to compute the loads on a two-bladed vertical axis wind turbine. Tests on the kernel size at a high mesh resolution reveal that a size equal to half of the full chord length yields the most accurate results. The aerodynamic load solution is validated against a fully resolved Scale-Adaptive Simulation (SAS) output, demonstrating high correlation, and enabling an assessment of near wake and downstream effects. The model’s adaptability to various rotor operating conditions is confirmed through tests at high and low tip-speed ratios. Additionally, a Biot–Savart-based Vortex Model (VM) is employed for further comparison, showing good agreement with the Lattice Boltzmann output. The results indicate that the Highly Stable Lattice Boltzmann Method integrated with the Actuator Line Model enhances the accuracy of flow field resolution and effectively captures complex aerodynamic phenomena, making it a valuable tool for simulating vertical axis wind turbines. Full article

In cold climates, ice formation on wind turbines causes power reduction produced by a wind farm. This paper introduces a framework to predict icing at the farm level based on our recently developed Temporal Convolutional Network prediction model for a single turbine using SCADA data.First, a cross-validation study is carried out to evaluate the extent predictors trained on a single turbine of a wind farm can be used to predict icing on the other turbines of a wind farm. This fusion approach combines multiple turbines, thereby providing predictions at the wind farm level. This study shows that such a fusion approach improves prediction accuracy and decreases fluctuations across different prediction horizons when compared with single-turbine prediction. Two approaches are considered to conduct farm-level icing prediction: decision fusion and feature fusion. In decision fusion, icing prediction decisions from individual turbines are combined in a majority voting manner. In feature fusion, features of individual turbines are averaged first before conducting prediction. The results obtained indicate that both the decision fusion and feature fusion approaches generate farm-level icing prediction accuracies that are 7% higher with lower standard deviations or fluctuations across different prediction horizons when compared with predictions for a single turbine. Full article

The present research explores the optimization of maintenance strategies for floating offshore wind (FOW) farms using nested genetic algorithms. The primary goal is to provide insights on the decision-making processes required for both immediate and strategic maintenance planning, crucial for the viability and efficiency of FOW operations. A nested genetic algorithm was coupled with discrete-event simulations in order to simulate and optimize maintenance scenarios influenced by various operational and environmental parameters. The study revealed that short-term maintenance timing is significantly influenced by wind conditions, with higher electricity prices justifying on-site spare parts storage to mitigate operational disruptions, suggesting economic incentives for maintaining on-site inventory of spare parts. Long-term strategic findings emphasized the impact of planned intervals between inspections on financial outcomes, identifying optimal strategies that balance operational costs with energy production efficiency. Ultimately, this study highlights the importance of integrating sophisticated predictive models for failure detection with real-time operational data to enhance maintenance decision-making in the evolving landscape of offshore wind energy, where future farms are likely to operate farther from onshore facilities and under potentially highly varying market conditions in terms of electricity prices. Full article

The Archimedes spiral wind turbine (ASWT), as a novel type of horizontal-axis wind turbine, is well suited for remote islands. To explore the aerodynamic performance and coupling gain effect of ASWT array, a three-dimensional numerical simulation was carried out using the computational fluid dynamics (CFD) method. The influence of arrangement, relative spacing, and rotation configuration on the performance of a double-unit array and triangular array is studied. The results demonstrate that, in parallel arrangements, the double unit achieve higher performance than an isolated ASWT within a specific range of parallel spacing. However, the effect of performance improvement gradually diminishes as the parallel spacing increases. In tandem arrangements, the upstream unit performance remains largely unaffected when tandem spacing exceeds 5 D, while the downstream unit’s performance declines notably with reducing tandem spacing. The downstream unit in reverse rotation configuration accrues more energy than its counterpart operating at the co-rotation configuration. In triangular arrangements, the reverse rotation configuration can achieve better performance due to the meshing effect between the wake of the upstream ASWT and the downstream ASWT. This configuration allows the array system to maintain a higher maximum power output within a smaller spacing. The research results can provide a basis and reference for designing the layout scheme of a multi-unit ASWT power station. Full article

Ensuring the accuracy of wind power prediction is paramount for the reliable and stable operation of power systems. This study introduces a novel approach aimed at enhancing the precision of wind power prediction through the development of a multiscale hybrid model. This model integrates advanced methodologies including Improved Intrinsic Mode Function with Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), permutation entropy (PE), Least Squares Support Vector Regression (LSSVR), Regularized Extreme Learning Machine (RELM), multi-head attention (MHA), and Bidirectional Gated Recurrent Unit (BiGRU). Firstly, the ICEEMDAN technique is employed to decompose the non-stationary raw wind power data into multiple relatively stable sub-modes, while concurrently utilizing PE to assess the complexity of each sub-mode. Secondly, the dataset is reconstituted into three distinct components as follows: high-frequency, mid-frequency, and low-frequency, to alleviate data complexity. Following this, the LSSVR, RELM, and MHA-BiGRU models are individually applied to predict the high-, mid-, and low-frequency components, respectively. Thirdly, the parameters of the low-frequency prediction model are optimized utilizing the Dung Beetle Optimizer (DBO) algorithm. Ultimately, the predicted results of each component are aggregated to derive the final prediction. The empirical findings illustrate the exceptional predictive performance of the multiscale hybrid model incorporating LSSVR, RELM, and MHA-BiGRU. In comparison with other benchmark models, the proposed model exhibits a reduction in Root Mean Squared Error (RMSE) values of over 10%, conclusively affirming its superior predictive accuracy. Full article

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. Full article

The design and optimization of floating offshore wind turbines (FOWTs) pose significant challenges, stemming from the complex interplay among aerodynamics, hydrodynamics, structural dynamics, and control systems. In this context, this study introduces an innovative method for forecasting the dynamic behavior of FOWTs under various conditions by merging Convolutional Neural Network (CNN) with a Gated Recurrent Unit (GRU) network. This model outperforms traditional numerical models by delivering precise and efficient predictions of dynamic FOWT responses. It adeptly handles computational complexities and reduces processing duration, while maintaining flexibility and effectively managing nonlinear dynamics. The model’s prowess is showcased through an analysis of a spar-type FOWT in a multivariate parallel time series dataset using the CNN–GRU structure. The outcomes are notably promising, underscoring the model’s proficiency in accurately forecasting the performance of FOWTs. Full article

Wind systems are sustainable and economical options for producing electrical energy. These systems efficiently manage the power flow by maximizing wind power and consuming reactive power from the grid. In addition, wind systems must maintain operation despite utility grid electrical failure; hence, their control system must not collapse. This study proposes a fault-tolerant converter controller to ensure the efficient operation of wind system converters. The central concept behind this is that when there is an imbalance in the utility grid voltage due to a fault nearby or far away, positive and negative sequence voltages are created in the time domain. Then, two parallel controllers operate to allow the wind system to continue operating despite the failure. One controller utilizes positive sequence voltages as inputs to regulate the generator’s electromagnetic torque. This helps in maximizing the amount of wind energy. The second controller uses negative sequence voltages as inputs, which helps to cancel out the produced torque in the opposite direction, thereby preventing generator overload. Finally, the controllers proposed in this article are validated through simulations, and the results are presented. Full article

Wind prediction has consistently been in the spotlight as a crucial element in achieving efficient wind power generation and reducing operational costs. In recent years, with the rapid advancement of artificial intelligence (AI) technology, its application in the field of wind prediction has made significant strides. Focusing on the process of AI-based wind prediction modeling, this paper provides a comprehensive summary and discussion of key techniques and models in data preprocessing, feature extraction, relationship learning, and parameter optimization. Building upon this, three major challenges are identified in AI-based wind prediction: the uncertainty of wind data, the incompleteness of feature extraction, and the complexity of relationship learning. In response to these challenges, targeted suggestions are proposed for future research directions, aiming to promote the effective application of AI technology in the field of wind prediction and address the crucial issues therein. Full article

Individual pitch control (IPC) is a method to mitigate periodic blade loads in wind turbines, and it is typically implemented using the multi-blade coordinate (MBC) transform, which converts the blade load measurements from a rotating frame into the non-rotating tilt axis and yaw axis. Previous studies have shown that by including an additional tuning parameter in the MBC, the azimuth offset reduces the coupling between non-rotating axes, allowing for higher performance levels for diagonal controller structures. In these studies, the decentralized control of IPC was composed of two identical integral controllers. This work analyzes and compares the improvement that the azimuth offset can provide in different adaptive gain scheduling IPCs where the diagonal controllers can have integral or proportional action with different gains. They are applied to a 15 MW wind turbine simulated with OpenFAST v3.5 software. The controller parameter tuning is addressed as an optimization that reduces blade fatigue load based on the damage equivalent load (DEL) and is resolved through genetic algorithms. Simulations show that only using different controller gains in IPC does not provide significant improvements; however, including azimuth offset in the optimal IPC schemes with integral controllers allows for the greatest DEL reduction with a lower actuator effort. Full article

In this study, an $H_{\infty }$-fuzzy controller is proposed for application in wind turbines with uncertainties and nonlinearities. The performance of the proposed controller was validated via dynamic simulations using a commercial aero-elastic code and wind tunnel experiments employing a scaled wind turbine. The simulation and the experimental results were then compared with those of the conventional PI and LQR control algorithms presented in our previous study. In the simulation, the perturbation and the sensor noise were applied to reflect uncertainty and nonlinearity effects. In addition, in the wind tunnel experiment, a control system using a commercial Bachmann PLC was established with an accelerometer to estimate the fatigue load exerted by the rotor thrust. It was confirmed through experiments that the robustness and adaptation of the control system improved in the situation of pitch system failure. As a result of the experiment, the proposed $H_{\infty }$ controller was able to reduce the rotor speed fluctuation by 39.9%, the power fluctuation by 32.0%, and the fatigue load by 2.4% compared with the LQR fuzzy controller, which had better performance than the conventional PI controller. In addition, it was confirmed through experiments that the robustness and adaptation of the control system were well maintained. This was even true in the situation of one-blade pitch system failure. Full article

The wind turbine with a variable-pitch vertical axis is a novel type of small wind turbine with great development potential in the field of wind power generation. This study assessed the aerodynamic performance of a two-dimensional variable-pitch vertical-axis wind turbine (VAWT) under fluctuating wind conditions (sinusoidal-type fluctuations with an average velocity of 6 m/s) using the finite-volume method and the $RNGk-\epsilon$ turbulence model. The effects of the fluctuating inflow amplitude ($U_{amp}$), frequency ($f_c$), and mean tip speed ratio (${\lambda }_{mean}$) on the power coefficient of the wind turbine are analyzed. The results show that a maximum power coefficient of 0.33 is obtained when the inflow amplitude reaches 50% of the average velocity. The power coefficient initially increases and then decreases with the increase in the fluctuating inflow frequency, reaching a maximum value of 0.32 at $f_c=0.45\mathrm{H}\mathrm{z}$. Furthermore, the power coefficient reaches its maximum value of 0.372 at ${\lambda }_{mean}$= 0.5. Proper orthogonal decomposition (POD) is used to decompose and reconstruct the flow field under both fluctuating and uniform inflow conditions. A comparison of the POD analysis between the two conditions shows that the energy distribution is more dispersed under the fluctuating inflow condition and reconstructing the flow field under fluctuating inflow conditions requires more POD modes than that under uniform inflow conditions. Full article

This paper introduces a novel differentiator-based maximum power point tracking (MPPT) controller for a wind energy conversion system (WECS) equipped with a doubly fed induction generator (DFIG). Building upon our previous algorithms, the proposed controller reduces the need for detailed system information and displays enhanced robustness against parameter variations and disturbances. The innovation lies in the elimination of the need for explicit functional forms or specific parameter values in the system’s dynamics, relying solely on relative degrees and control directions. Utilizing a higher-order switching differentiator (HOSD), this paper outlines a method for overestimating the time derivatives of system outputs, thereby simplifying both the controller design and stability analysis. Compared to existing solutions, the proposed method requires minimal information, offers simpler control law structures, and follows a systematic design approach with fewer design constants. Simulation results demonstrate the efficacy of the proposed controller in both tracking maximum power and regulating reactive power to zero, suggesting a more efficient and simplified approach to MPPT control in WECS. Full article

The aim of this study is to develop a blade mass model that incorporates a low-induction rotor (LIR) and a low-specific power concept to reduce aerodynamic loads and lower the Levelized Cost of Energy (LCoE). This blade mass model replaces the traditional simple scaling rule and incorporates the concept of LCoE reduction, presenting not only the mass distribution in the blade span direction but also the stiffness distribution. In order to achieve the desired reduction in LCoE, we developed a mathematical model that expresses blade mass as a function of the axial induction factor, which influences the aerodynamic load on the blade. We used this model to determine geometries of various low-induction rotors for 20 MW class horizontal axis wind turbine, and to identify the axial induction factor that correlates with the lowest blade mass. The chord length and twist angle in the spanwise direction of the blade were determined using PROPID’s reverse design process, based on the specified axial induction factor. Since the low-induction concept is not aerodynamically optimal, a low-specific power design approach was also adopted. This involved increasing the blade length and shifting the power curve to the left. By doing so, the AEP is increased, directly contributing to a reduction in the LCoE. Mass per unit length of the blade was presented, reflecting the distribution of airfoil type, blade geometry, and shapes of internal structures such as spars and webs. Full article

Alarms generated by a wind turbine alarm system indicate the need for emergency action by operators to protect the turbine from running into risky conditions. However, it can be challenging for operators to identify the fault types that trigger alarms, particularly with few labeled fault samples. This paper proposes a novel fault diagnosis method for wind turbines with alarms that collaboratively uses labeled and unlabeled alarms to improve diagnosis accuracy. First, the proposed method distinguishes different alarm sequences using a designed Siamese convolutional neural network with an embedding layer (S-ECNN) model. Then, the fault category of an unknown alarm sequence is diagnosed based on similarity scores. Specifically, the Skip-gram model is used to mine potential relationships among alarms in unlabeled alarm sequences, and pretrained alarm vectors are obtained. In the S-ECNN model, the pretrained alarm vectors are further optimized and trained using labeled alarm sequences. The similarity scores are calculated based on the distance between the extracted discriminative features of alarm sequences. The effectiveness of the proposed method is validated using actual alarm data from a wind farm. Full article

Small-scale vertical-axis wind power generation technologies such as Savonius wind turbines are gaining popularity in suburban and urban settings. Although vertical-axis wind turbines (VAWTs) may not be as efficient as their horizontal-axis counterparts, they often present better opportunities for integration within building structures. The main issue stems from the suboptimal aerodynamic design of Savonius turbine blades, resulting in lower efficiency and power output. To address this, modern turbine designs focus on optimizing various geometric aspects of the turbine to improve aerodynamic performance, efficiency, and overall effectiveness. This study developed a unique optimization method, incorporating a new blade geometry with guide gap flow for Savonius wind turbine blade design. The aerodynamic characteristics of the Savonius wind turbine blade were extensively analyzed using 3D ANSYS CFX software. The optimization process emphasized the power coefficient as the objective function while considering blade profiles, overlap ratio, and blade number as crucial design parameters. This objective was accomplished using the design of experiments (DOE) method with the Minitab statistical software. The research findings revealed that the novel turbine design “OR0.109BS2BN2” outperformed the reference turbine with a 22.8% higher power coefficient. Furthermore, the results indicated a trade-off between the flow (swirling flow) through the gap guide flow and the impact blockage ratio, which resulted from the reduced channel width caused by the extended blade tip length. Full article

Considering the dynamic stall effects in engineering calculations is essential for correcting the aerodynamic loads acting on wind turbines, both during power production and stand-still cases, and impacts significantly the turbine aeroelastic stability. The employed dynamic stall model needs to be accurate and robust for a wide range of airfoils and range of angle of attack. The present studies are intended to demonstrate the performance of a recently implemented “IAG dynamic stall” model in a wind turbine design tool Bladed. The model is transformed from the indicial type of formulation into a state-space representation. The new model is validated against measurement data and other dynamic stall models in Bladed for various flow conditions and airfoils. It is demonstrated that the new model is able to reproduce the measured dynamic polar accurately without airfoil specific parameter calibration and has a superior performance compared to the incompressible Beddoes–Leishman model and the Øye model in Bladed. Full article

Wind turbine blade leading-edge erosion (LEE) is a cause of increased operation and maintenance costs and decreased annual energy production. Thus, detailed, site-specific quantification of likely erosion conditions are critically needed to inform wind plant owner/operator decisions regarding mitigation strategies. Estimating the damage potential at a wind plant site requires accurate measurement of precipitation intensity, phase, droplet size distributions, wind speeds and their joint statistics. The current work quantifies the effect of disdrometer type on the characterization of LEE potential at a site in the US Southern Great Plains. using observations from three co-located disdrometers (an optical, an impact and a video disdrometer), along with hub-height wind-speed observations from a Doppler lidar and two LEE models: a kinetic energy model and the Springer model. Estimates of total kinetic energy of hydrometeor impacts over the four-year study period vary by as much as 38%, and coating lifetime derived from accumulated distance-to-failure estimates from the Springer model differ by an even greater amount, depending on disdrometer type. Damage potential at this site is concentrated in time, with 50% of impact kinetic energy occurring in 6–12 h per year, depending on which set of disdrometer observations is used. Rotor-speed curtailment during the most erosive 0.1–0.2% of 10 min periods is found to increase blade lifetimes and lead to the lowest levelized cost of energy. Full article

While large renewable power generation schemes, such as wind farms, are well monitored with a wealth of data provided through a SCADA system, the only information about the behaviour of small wind turbines is often only through the metered electricity production. Given the variability of electricity output in response to the local wind or radiation condition, it is difficult to ascertain whether particular electricity production in a metering period is the result of the system operating normally or if a fault is resulting in a sub-optimal production. This paper develops two alternative methods to determine a performance score based only on electricity production and proxy wind data obtained from the nearest available weather measurement. One method based on partitioning the data, consistent with a priori expectations of turbine performance, performs well in common wind conditions but struggles to reflect the effects of different wind directions. An alternative method based on Principal Component Analysis is less intuitive but shown to be able to incorporate wind direction. Full article

The components of an operational wind turbine are continuously impacted by both static and dynamic loads. Regular inspections and maintenance are required to keep these components healthy. The main bearing of a wind turbine is one such component that experiences heavy loading forces during operation. These forces depend on various parameters such as wind speed, operating regime and control actions. When a wind turbine provides frequency containment reserve (FCR) to support the grid frequency, the forces acting upon the main bearing are also expected to exhibit more dynamic variations. These forces have a direct impact on the lifetime of the main bearing. With an increasing trend of wind turbines participating in the frequency ancillary services market, an analysis of these dynamic forces becomes necessary. To this end, this paper assesses the effect of FCR-based control on the main bearing lifetime of the wind turbine. Firstly, a control algorithm is implemented such that the output power of the wind turbine is regulated as a function of grid frequency and the amount of FCR. Simulations are performed for a range of FCR to study the changing behaviour of dynamical forces acting on the main bearing with respect to the amount of FCR provided. Then, based on the outputs from these simulations and using 2 years of LiDAR wind data, the lifetime of the main bearing of the wind turbine is calculated and compared for each of the cases. Finally, based on the results obtained from this study, the impact of FCR provision on the main bearing lifetime is quantified and recommendations are made, that could be taken into account in the operation strategy of a wind farm. Full article

Short-term wind forecasting is critical for the dispatch, controllability and stability of a power grid. As a challenging but indispensable work, short-term wind forecasting has attracted considerable attention from researchers. In this paper, Principal Component Analysis (PCA) is applied to Computational Fluid Dynamics (CFD) calculation results for feature extraction and then combined with sparse sensing to achieve the rapid reconstruction of a three-dimensional wind speed field and pressure field. Before reconstruction, the relationship between the reconstruction error and the noise level, and a number of the basis vectors is systematically studied. In the simulation, the wind shear effect is introduced into the inlet boundary condition, and the reconstruction errors of the uniform inlet are 0.21% and 6.46%, respectively, while the maximum reconstruction errors including the wind shear effect are 1.21% and 6.41%, respectively, which verifies the feasibility of applying a PCA-based reconstruction algorithm to a 3D wind field reconstruction. In addition, to solve the time-consuming problem of most optimization algorithms based on a brute-force combinatorial search, an innovative optimization algorithm based on the QR pivoting is investigated to determine the sparse sensor placements. Simulation results show that when the number of sensors is equal to the number of basis vectors, the error of random placement is even 20 times of the optimal placement, which illustrates that QR pivoting is a powerful optimization algorithm. Finally, a wind tunnel experiment of velocity field reconstruction is performed, to verify the practicability of the optimized method based on QR pivoting, and the results indicate that a reasonably high accuracy 3D wind field can be obtained with only 10 sensors (the error of most points is less than 5% and the minimum error is only 0.74%). In general, the proposed algorithm incorporating PCA, sparse sensing and QR pivoting can quickly reconstruct the 3D velocity and pressure fields with reduced measurement costs, which is of great significance for the development of short-term wind forecasting methods. Full article

Lubrication plays a key role in increasing availability of wind turbines, extending unit life and reducing operating costs. In view of the problems of valve core lag, grease hardening and difficulty in removing waste oil in a centralized lubrication system, an improved centralized lubrication system and waste oil recovery system were designed in this study. Discharge of waste grease in the bearing cavity was simulated under different vacuum conditions. It was shown that vacuum degree of bearing cavity is proportional to oil output speed of waste grease. Performance and fatigue reliability tests of the waste grease suction and drainer device test platform were conducted over 12,000 fatigue cycles. The results show that the vacuum degree error of the waste grease suction and drainer device before and after the test is less than 5%, and the power oil pressure, oil output pressure and oil output quantity of the test product are stable, indicating that the designed waste grease suction and drainer device has excellent sealing and reliability. The waste grease suction and drainer device can eliminate grease discharge resistance in the bearing cavity, facilitating discharge of waste oil and improving wind turbine operation efficiency. Full article

Operating Offshore Floating Vertical Axis Wind Turbines (OF-VAWT) have the potential to perform well in the deep-sea area. Some researchers gave performance prediction by developing simplified computing models. However, these models have imperfections in considering load and motion nonlinearity, especially in extreme environments. In this work, a numerical model is developed composed of Computational Fluid Dynamics (CFD) and Dynamic Fluid Body Interaction (DFBI) to acquire the aero-hydrodynamic load and performance of OF-VAWT in general and extreme environments. Unsteady Reynolds-Averaged Navier-Stokes (URANS), SST k-ω and Eulerian Multi-Phase (EMP) models are combined to generate a gas-liquid two-phase flow field; the Volume of Fluid (VOF) model is employed to capture free-surface and make numerical wind-wave. DFBI superposition motion technology is proposed for local motion definition and motion solution, and overset with sliding meshes is introduced to achieve the grid motion. The numerical approach is verified by the tunnel and tank experimental data from the available literature. Simulation results of general cases, such as variable wind speed, wave height and wave length, are compared to discuss the effect of environmental parameters on load and performance. Comparison shows that this straight-bladed OF-VAWT is more susceptible to wind speed. Furthermore, the aerodynamic load generated by the shut-down rotor is still significant in extreme environment, which has implications for the development of OF-VAWT controller. Full article

Following the International Maritime Organization’s goal to reduce greenhouse gas emissions, the interest in the application of wind-assisted ship propulsion (WASP) in maritime transportation is on the rise. Although a variety of WASP systems exist, the application in maritime shipping is still limited, especially in the case of kite sails. This paper presents a numerical model to carry out a theoretical assessment of the influence of the kite planform area and wind speed on the aerodynamic performance of a kite sail providing propulsive assistance to a 75 m long ship having a Series 60 hull. A static kite sail is assumed, on which a tail wind generates a thrust force to pull the vessel via a tether. While the mass of the kite is neglected, that of the tether is accounted for. A model is implemented for the tensioned tether having a catenary profile. The results generally show a positive correlation between the aerodynamic forces and the kite parameters. As expected, the output parameter values corresponding to the optimal angle of attack for a range of vessel speeds are also found to increase with an increasing relative wind speed. It is concluded that a static 320 m² kite sail would be sufficient to meet the entire propulsion requirements of the vessel under consideration under appropriate wind conditions. Full article

Continuous improvement of wind turbines represent an effective way of achieving green energy and reducing dependence on fossil fuel. Conventional lift-type horizontal axis wind turbines, which are widely used, are designed to run under high wind speed to obtain a high efficiency. Aiming to use the low-speed wind in urban areas, a novel turbine, which is called the Archimedes Spiral Wind Turbine (abbreviated as ASWT), was recently proposed for low-speed wind applications. In the current work, a numerical simulation on the five ASWT rotors with different blade angles was carried out, which were performed to predict the detailed aerodynamic performance and wake characteristics. The results show that the ASWT rotor with a large blade angle has a wider operating tip speed ratio range and a higher tip speed ratio point of maximum power coefficient within a certain range, and yet the ASWT rotor with the larger blade angle has a higher thrust coefficient. Additionally, the ASWT rotor with a large blade angle usually has a large power coefficient and thrust coefficient fluctuation amplitude. On the other hand, the ASWT rotor with a small blade angle permits the undisturbed free stream to pass through the rotor blades more easily than that with a large blade angle. This causes a stronger blockage effect for the ASWT rotor with a large blade angle. Moreover, the blade angle also has a great effect on the shape of the vortex structure. The blade tip vortex of the fixed-angle ASWT rotors is more stable than those of the variable-angle ASWT rotors. The hub vortex of the ASWT rotors with a large blade angle is stronger than those with a small blade angle. Meanwhile, the wake recovery for ASWT rotors with a small blade angle is evidently lower than those with a large blade angle. Full article

Renewable energy sources are preferred for many power generation applications. Energy from the wind is one of the fastest-expanding kinds of sustainable energy, and it is essential in preventing potential energy issues in the foreseeable future. One pertinent issue is the many geometrical alterations that the scientific community has suggested to enhance rotor performance features. Hence, to address the challenge of developing a model that resolves these problems, the purpose of this investigation was to determine how well a scaled-down version of a Savonius turbine performed in terms of power output using a wind tunnel. Subsequently, the effect of the blockage ratio produced in the wind tunnel during the chamber test on the scaled model was evaluated. This study discusses the influences of various modified configurations on the turbine blades’ torque and power coefficients (Cp) at various tip speed ratios (TSRs) using three-dimensional (3D) unsteady computational fluid dynamics. The findings showed that the scaled model successfully achieved tunnel blockage corrections, and the experimental results obtained can be used in order to estimate how the same turbine would perform in real conditions. Furthermore, numerically, the new models achieved improvements in Cp of 19.5%, 16.8%, and 12.2%, respectively, for the flow-guiding channel (FGC at Ⴔ = 30°), wavy area at tip and end (WTE), and wavy area on the convex blade (WCB) models in comparison to the benchmark S-ORM model and under identical wind speed conditions. This investigation can provide guidance for improvements of the aerodynamic characteristics of Savonius wind turbines. Full article

Wind turbine blades experience excessive load due to inaccuracies in the prediction of aerodynamic loads by conventional methods during design, leading to structural failure. The blade element momentum (BEM) method is possibly the oldest and best-known design tool for evaluating the aerodynamic performance of wind turbine blades due to its simplicity and short processing time. As the turbine rotates, the aerofoil lift coefficient enhances, notably in the rotor’s inboard section, relative to the value predicted by 2D experimentation or computational fluid dynamics (CFD) for the identical angle of attack; this is induced by centrifugal pumping action and the Coriolis force, thus delaying the occurrence of stall. This rotational effect is regarded as having a significant influence on the rotor blade’s aerodynamic performance, which the BEM method does not capture, as it depends on 2D aerofoil characteristics. Correction models derived from the traditional hard computing mathematical method are used in the BEM predictions to take into account stall delay. Unfortunately, it has been observed from the earlier literature that these models either utterly fail or inaccurately predict the enhancement in lift coefficient due to stall delay. Consequently, this paper proposes a novel stall delay correction model based on the soft computing technique known as symbolic regression for high-level precise aerodynamic performance prediction by the BEM process. In complement to the correction model for the lift coefficient, a preliminary correction model for the drag coefficient is also suggested. The model is engendered from the disparity in 3D and 2D aerofoil coefficients over the blade length for different wind speeds for the NREL Phase VI turbine. The proposed model’s accuracy is evaluated by validating the 3D aerofoil coefficients computed from the experimental results of a second wind turbine known as the MEXICO rotor. Full article

The wake management of offshore wind farms (OWFs) mainly considers the wake effect. Wake effects commonly occur in offshore wind farms, which cause a 5–10% reduction in power production. Although there have been many studies on wake management, many methods are not accurate enough; for instance, look-up table and static wake model control methods do not consider the time-varying wake state. Dynamic wake management is based on the real-time dynamic wake, so it can increase the energy of the OWFs effectively. For OWFs, dynamic wake control is the main method of dynamic wake management. In this paper, the existing wake model and control progress are discussed, mainly emphasizing the dynamic wake model and the dynamic wake control method, solving the gap of the review for dynamic wake management. This paper presents a digital twins (DT) framework for power and fatigue damage for the first time.. The structure of this paper is as follows: (1) the mechanism of wind farm wake interference is described and then the dynamic wake model is reviewed and summarized; (2) different control methods are analyzed and the dynamic wake management strategies for different control methods are reviewed; (3) in order to solve the problems of dynamic wake detection and real-time effective control, the technology of DT is applied to the dynamic wake control of OWFs. This new DT frame has a promising application prospect in improving power and reducing fatigue damage. Full article

The lifetime of offshore foundations is governed by a combination of harsh environmental conditions and complex service loads. The fatigue limit state (FLS) analysis needs to be performed in the time domain to capture the complex phenomenon. This study aims to investigate different parameters and design modifications that can impact the design life of an offshore jacket foundation. An OC4 jacket foundation is designed in industrial software from DNV and reduced to a super-element model. The super-element model is connected to an NREL 5-MW wind turbine designed in Bladed. The time-series loads are used to compute the fatigue damages faced by the foundation during the service life. The impact of soil non-linearity, marine growth, scour size, the mass of the transition piece, and the grouted connection’s design on the dynamic response and fatigue damages are compared. A 30% increase in life was observed by replacing the concrete transition piece with a lightweight steel configuration. The fatigue damages were considerably greater for the inclined pile in the leg grouted connection than for the leg in the pile concept. The study provides a different perspective by analysing the effect of design parameters and design changes in the complex and computationally expensive time-series domain. Full article

Maximum power point tracking (MPPT) is the key to improve the conversion efficiency of wind energy. Concerning the current research on the MPPT control, based on the accurate tracking of rotor speed probability density function (PDF) shape for wind turbines, a novel MPPT algorithm was introduced in detail to improve the power capture and reduce mechanical damage for wind turbines. Considering the influence of wind speed distribution on the wind power generation system performance, this paper expounds a PDF shape control method of a stochastic system based on the Fokker–Planck–Kolmogorov (FPK) equation. Combining the conventional optimal torque (OT) control algorithm with the FPK equation solved by linear least-square (LLS) method, the novel MPPT control law is designed to make the PDF shape of rotor speed track the desired PDF shape as accurately as possible. The simulation verification of the novel MPPT method is carried out in the 1.5 MW wind turbine system. The results reveal that the novel MPPT method can improve the conversion efficiency of wind energy, reduce the frequent fluctuations of system variables, and significantly optimize the performance of wind power generation system. Full article

With the participation of wind power in grid frequency modulation, the fatigue load of the wind turbine increases accordingly. A new control method that considers both fatigue load and output power of wind turbine (WT) is proposed in this paper. A linear active disturbance rejection control (LADRC) is designed and applied for the pitch angle in the wind turbine load reduction control. The particle swarm optimization (PSO) algorithm is used to optimize the parameters of the wind turbine controller, and the total variation of the wind turbine shaft torque and tower bending moment is added to construct a new objective function to further reduce the fatigue load of the wind turbine. The design-optimized controller is validated on a 5 MW wind turbine in SimWindFarm. The simulation results show that the LADRC controller can accurately track the reference power of the wind turbine, reduce the pitch angle fluctuation of the wind turbine, reduce the fatigue load of the wind turbine, and improve the service life of the wind turbine. Full article

This study aims to evaluate wind speed and significant wave height data from the Copernicus Marine Environment Monitoring Service (CMEMS) reanalysis using buoy measurements for offshore energy application off the east coast of Brazil. Such analysis has become important, since reanalysis datasets can be fundamental tools in identifying regions with wind energy potential that are suitable for the installation of offshore farms. Two sets of reanalysis were used: wind speed (with spatial resolution of 0.25° and temporal resolution of 6 h) and significant wave height (with spatial resolution of 0.2° and temporal resolution of 3 h). For validation, seven MetOcean buoys were selected. In the statistical validation, Pearson’s correlation, coefficient of determination (R²), slope of the straight line, root mean square error (RMSE), mean square error (MSE), probability density function (PDF), mean and standard deviation were calculated. In the evaluation of offshore wind energy resources, the calculation of energy density was performed. The results showed correlations above 0.70 for wind speed and above 0.91 for significant wave height, and additionally, the RMSE values showed maximums of 2.31 m/s for wind speed and 0.28 cm for significant wave height. In the PDF comparison of buoy data and reanalysis, similarities were observed, mainly in the PDF parameters. The energy density presented values consistent with other studies (352–461 W/m²). The results show that the reanalysis data can be applicable in studies focusing on offshore wind potential. Full article