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ABSTRACT Accurate ultra‐short‐term wind power prediction techniques are crucial for ensuring the efficient and safe operation of wind farms and power systems. Combined models based on data decomposition‐prediction techniques have shown excellent performance in ultra‐short‐term wind power forecasting. This study introduces a novel ultra‐short‐term multi‐step prediction model for wind power, which integrates the sparrow search algorithm (SSA), variational mode decomposition (VMD), gated recurrent unit (GRU), and support vector regression (SVR). An optimization variational mode decomposition technique is developed by adaptively determining VMD hyperparameters using SSA. The optimization VMD decomposes the original wind power sequence into sub‐modes, and the resulting sequence of decomposed sub‐modes calculates permutation entropy (PE) values. Sub‐modes with similar PE values are combined, reorganized, and categorized into high‐frequency and low‐frequency. High‐frequency sub‐modes data with high complexity and non‐stationarity are predicted by the GRU neural network. Low‐frequency sub‐modes data with low complexity and strong nonlinearity are predicted with SVR. The proposed model was evaluated against seven others using three error metrics: MAE, RMSE, and R2, along with their corresponding enhancement percentages. Experimental results indicate that the proposed model extracts detailed and trend information from the wind power series more effectively and stably than the comparison models. It also demonstrates superior multi‐step prediction performance, offering significant value for practical engineering applications. [ABSTRACT FROM AUTHOR]

Marine current power is attracting more attention as a renewable energy option. Similar to wind power, marine current power often requires a maximum power point tracking (MPPT) method to optimize power extraction from the free‐flowing water. Research into MPPT methods for marine current power remains limited. Therefore, this paper presents a comprehensive investigation of MPPT methods for marine current power, building upon similar research in wind power. Three methods, namely the optimal tip speed ratio (OTSR), optimal torque (OT), and two variants of the perturb and observe (P&O) method, are explored. Using a simulation model developed for a specific marine current energy converter, where hydrodynamic calculations are coupled with electrical simulations, the study demonstrates that the OTSR method achieves MPPT with a comparably fast convergence time. After a change in water speed, the OTSR method achieves optimal operation within two turbine rotations. Additionally, the P&O methods are shown to achieve MPPT, albeit with a significantly longer convergence time. However, the P&O methods can be more convenient since no model of the system is required, and no water speed measurements are necessary. The proposed implementation of the OT method underperforms but positions the system close to the optimal operational point. [ABSTRACT FROM AUTHOR]

Summary: As wind energy is sustainable, pollution‐free, easily available, and free of cost, it has become an efficient source of renewable energy for electricity generation. But, the problem with wind energy is that it varies with time, seasons, and location. This makes the Wind Energy Conversion System (WECS) unstable as it frequently needs to match the load demands. The balance in power generation by wind energy is essential since it has to be connected to various grids. So, this unbalanced energy production can affect the stability of the associated power grids as well. It also results in expensive regulatory measures, storage options, and load shedding. So, the stable operation of the WECS is highly essential to adapt it as a trustable source of electricity production. The stable operation of the WECS requires a robust and advanced system for control. Better control of the wind power extracting model is achieved by controlling the Maximum Power Point Tracking (MPPT) and blade pitch. So, an Adaptive MPPT and Blade Pitch Controller (BPC) for the WECS have been developed in this article, with the support of a hybrid optimization algorithm. In order to enhance the working principles of this controller, two effective algorithms such as Sand Cat Swarm Optimization (SCSO) and Galactic Swarm Optimization (GSO) are integrated and named Hybrid Sand Cat Galactic Swarm Optimization (HSC‐GSO). With the help of the recommended HSC‐GSO, the functionality of the controller is enhanced and also at the same time this algorithm helps to optimize the three gains in the Proportional Integral Differential (PID) controller of both MPPT and BPC, respectively. Moreover, with the support of the proposed HSC‐GSO the damping oscillations in the WECS output power and voltage are minimized. In the end, the numerical analysis is conducted for the presented system by comparing it with the traditional techniques. From the overall result analysis, the stability of the recommended adaptive WECS is 97, which is higher than the conventional algorithms such as DHOA, SCSO, GSO, and DA. Thus, it has been proved that the proposed HSC‐GSO algorithm for the parameters optimization in the PID controller of MPPT and the PID controller of BPC attains high robustness, increased steady‐state stability, and efficient transient response than the traditional techniques. [ABSTRACT FROM AUTHOR]

Golden eagles (Aquila chrysaetos) face many anthropogenic risks including illegal shooting, electrocution, collision with wind turbines and vehicles, and lead poisoning. Minimizing or offsetting eagle deaths resulting from human‐caused sources is often viewed as an important management objective. Despite understanding the leading anthropogenic sources of eagle fatalities, existing scientific research supports few practical solutions to mitigate these causes of death. We implemented a non‐lead ammunition distribution program in southeast Wyoming, USA, and evaluated its effectiveness as a compensatory mitigation action to offset incidental take (i.e., fatalities) of golden eagles at wind energy facilities. In 2020 and 2022, we distributed non‐lead ammunition to 699 hunters with big‐game tags specific to our >400,000‐ha study area. These hunters harvested 296 pronghorn (Antilocapra americana), 14 deer (Odocoileus spp.), and 33 elk (Cervus canadensis) in the study area, which accounted for 6.9% and 6.5% of the harvest in these hunt units in 2020 and 2022, respectively. We used road surveys in 2020 to estimate a density of 0.036 (95% CI = 0.018–0.058) golden eagles/km2 during the big game hunting season in our study area. Model output suggests that our non‐lead ammunition distribution program offset the fatality of 3.84 (95% CI = 1.06–23.72) eagles over the course of these 2 hunting seasons. Our work illustrates the potential usefulness of non‐lead ammunition distribution programs as an action to mitigate eagle fatalities caused by wind facilities or other anthropogenic causes of death. [ABSTRACT FROM AUTHOR]

When planning a new wind farm, optimising the turbine layout to maximise power output is crucial. Traditional approaches based on analytical wake models often fail in complex terrain. In such areas, wake‐to‐wake interactions and terrain‐induced effects need to be taken into account via high‐fidelity optimisation. Building on our prior research on how best to maximise offshore wind farm power production by both micro‐siting (layout optimisation) and wake steering (yaw angle optimisation), this study focuses on the optimisation of two onshore wind turbines located around a 2D hill. The proposed approach is based on high‐fidelity simulations (LES) to account for the complexity of the flow and on Bayesian optimisation (BO) to systematically and efficiently find the optimal configuration for the two turbines given a set of parameters. The LES are performed with the high‐order finite‐difference wind farm simulator WInc3D, which has been modified for this study to deal with complex terrains and turbines with yaw and tilt. Following the validation of the immersed boundary method (IBM) and wall modelling used to model the complex terrain and of a modified actuator disc model (ADM) to account for tilted turbines, two BO campaigns are presented in this study, with different sets of parameters to optimise. The main flow features and some statistics for each optimum case are discussed using the flow fields with and without the optimised configuration of the upstream turbine to analyse its impact on the downstream turbine and on the overall power of the system. For the first optimisation, the design variables include the wind turbines' streamwise locations and their hub heights (4 parameters). This optimisation study benefits from an elevated upstream turbine hub, leading to a significant power increase in the downstream turbine, thanks to an acceleration of the flow in front of the hill. For the second optimisation, the design space is modified based on the data from the first optimisation, by replacing the turbine's hub height with the possibility of changing the tilt angle of the upstream turbine (4 parameters). It is found that considerable power gains can be obtained while maintaining a modest upstream turbine hub height, by introducing a positive tilt angle in the upstream turbine. Overall, it is found that by setting up the turbines in ways that exploit the speed‐up of the flow around the hill, both optimised layouts achieve considerable enhancements in power density over a reference configuration. [ABSTRACT FROM AUTHOR]

Floating offshore wind (FOW) is a renewable energy source that is set to play an essential role in addressing climate change and the need for sustainable development. However, due to the increasing threat of climate emergency, more wind turbines are required to be deployed in deep water locations, further offshore. This presents heightened challenges for accessing the turbines and performing maintenance, leading to increased costs. Naturally, methods to reduce operational expenditure (OpEx) are highly desirable. One method that shows potential for reducing OpEx of FOW is LIDAR‐assisted pitch control. This approach uses wind velocity measurements from a nacelle‐mounted LIDAR to enable feedforward control of floating offshore wind turbines (FOWTs) and can result in reductions to the variations of structural loads. Results obtained from a previous study of combined feedforward collective and individual pitch control (FFCPC + FFIPC) are translated to OpEx reductions via reduced component failure rates for future FOW developments, namely, in locations awarded in the recent ScotWind leasing round. The results indicate that LIDAR‐assisted pitch control may allow for an up to 5% reduction in OpEx, increasing to up to 11% with workability constraints included. The results varied across the three ScotWind sites considered, with sites furthest from shore reaping the greatest benefit from LIDAR‐assisted control. This work highlights the potential savings and reduction in the overall levelised cost of energy for future offshore wind turbine projects deliverable through the implementation of LIDAR‐assisted pitch control. [ABSTRACT FROM AUTHOR]

This paper presents a graph‐based dynamic yaw model to predict the dynamic response of the hub‐height velocities and the power of a wind farm to a change in yaw. The model builds on previous work where the turbines define the nodes of the graph and the edges represent the interactions between turbines. Advances associated with the dynamic yaw model include a novel analytical description of the deformation of wind turbine wakes under yaw to represent the velocity deficits and a more accurate representation of the interturbine travel time of wakes. The accuracy of the model is improved by coupling it with time‐ and space‐dependent estimates of the wind farm inflow based on real‐time data from the wind farm. The model is validated both statically and dynamically using large‐eddy simulations. An application of the model is presented that incorporates the model into an optimal control loop to control the farm power output. [ABSTRACT FROM AUTHOR]

Fast and accurate large‐eddy simulation (LES) of the atmospheric boundary layer plays a crucial role in advancing wind energy research. Long‐duration wind farm studies at turbine‐resolving scales have become increasingly important to understand the intricate interactions between large wind farms and the atmospheric boundary layer. However, the prohibitive computational cost of these turbulence‐ and turbine‐resolving simulations has precluded such modeling to be exercised on a regular basis. To that end, we implement and validate the generalized actuator disk (GAD) model in the computationally efficient, graphics processing unit (GPU)–resident, LES model FastEddy. We perform single‐turbine simulations under three atmospheric stabilities (neutral, unstable, and stable) and compare them against observations from the Scaled Wind Farm Technology (SWiFT) facility and other LES codes from the recent Wakebench turbine wake model benchmark. Our idealized LES results agree well with observed wake velocity deficit and downstream recovery across stability regimes. Turbine response in terms of rotational speed, generated power, torque, and thrust coefficient are well predicted across stability regimes and are consistent with the LES results from the benchmark. The FastEddy simulations are found to be at least two orders of magnitude more efficient than the traditional CPU‐based LES models, opening the door for realistic LES simulations of full wind plants as a viable standard practice. [ABSTRACT FROM AUTHOR]

Measuring boundary layer stratification, wind shear, and turbulence remains challenging for wind resource assessment. In particular, larger eddy scales have the greatest impact on turbine load fluctuations, and there are few in situ methods to observe them adequately. Satellite remote sensing using synthetic aperture radar (SAR) is an alternative approach. In this study, eddy‐related signatures in 704 high‐resolution images are related to stratification through a bulk Richardson number (Ri$$ Ri $$) measured by a buoy near Martha's Vineyard, the US epicenter of offshore wind. Variations in SAR‐observed atmospheric boundary layer eddies, or lack of them, correspond to specific Ri$$ Ri $$ regimes. Accounting for strong vertical wind shear, typically under stable stratification, is critical for energy production and turbine loads, and SAR directly identifies these conditions by the absence of energetic eddies. SAR also provides a regional climatology of atmospheric stratification for offshore wind assessment, complementing other observations, and with potential application worldwide. [ABSTRACT FROM AUTHOR]

Turbulent and gusty wind conditions can cause generator overspeed peaks to exceed a threshold that then lead to wind turbine shutdowns, which then decrease the energy production of the wind turbines. We derive so‐called "gust measures" that predict when generator overspeed peaks may occur. These gust measures are then used to develop advanced controllers to mitigate generator overspeed peaks so that wind turbines can operate more robustly in difficult wind conditions without exceeding generator overspeed thresholds that would lead to turbine shutdown events. The advanced controllers are demonstrated in nonlinear aeroelastic simulations using the open‐source wind turbine simulation tool OpenFAST. To increase the realism of the simulations, they are run using field‐replicated wind conditions and a wind turbine model based on data from an experimental field campaign on a downscaled demonstrator of a novel extreme‐scale, two‐bladed, downwind rotor design. [ABSTRACT FROM AUTHOR]

Flow control devices have garnered significant attention from domestic and international wind energy scholars. In an effort to capture more wind energy and boost the output power of wind turbines, this study incorporates flow control devices into the upstream area of the wind turbine. The study then assesses the impact of the spacing, tilt angle, and height of the flow control devices on the turbine's performance. The study demonstrates that the addition of flow control devices does lead to a notable enhancement in wind turbine power, with a maximum power output growth rate of 9.74%. The maximum growth rates for the average output power of the wind turbine due to the three factors (α, H, and Lw) are 4.20%, 3.76%, and 3.54%, respectively. The degree of influence of the three factors is as follows: α > H > Lw. [ABSTRACT FROM AUTHOR]

As the international energy market pays more and more attention to the development of clean energy, wind power is gradually attracting the attention of various countries. Wind power is a sustainable and environmentally friendly resource of energy. However, it is unstable. Therefore, it is important to develop algorithms for its prediction. In this paper, we apply a recently developed algorithm that effectively combines the tensor train decomposition with the higher order dynamic mode decomposition (TT‐HODMD). The dynamic mode decomposition (DMD) is a data‐driven technique that does not need a prior mathematical model. It is based on the measurement data or time slots. As demonstrated, for prediction it is important to use the higher order DMD (HODMD). In turn, HODMD might lead to very large scale arrays that are sparse. The tensor train decomposition provides a highly efficient way to work with such arrays. It is demonstrated that the combined TT‐HODMD algorithm is capable of providing quite accurate prediction of wind power for months ahead. [ABSTRACT FROM AUTHOR]

Alfvén waves are important carriers of solar wind energy into the Earth's magnetosphere and upper atmosphere. The observed distribution of Alfvénic power at low altitude exhibits significant dawn‐dusk asymmetry with different causes and impacts on the dayside and nighside. The nightside asymmetry has been shown to be controlled by the meridional gradient in the ionospheric Hall conductance. It is not clear what controls the dayside asymmetry. Both the ionospheric conductance gradient and orientation of the y‐component of the interplanetary magnetic field (IMF) are possible factors. We examined both possibilities using global magnetohydrodynamic (MHD) simulations. Our results show: (a) The dayside distribution of Alfvénic power is very sensitive to the orientation of IMF By, with enhanced power in the dawn (dusk) sector of the northern hemisphere for positive (negative) By (and opposite in the southern hemisphere); and (b) gradients in ionospheric conductance exert a weaker influence on the dayside asymmetry. Plain Language Summary: Alfvénic energy flux, which has an important impact on the low‐altitude space environment, is observed to be enhanced in the postnoon (prenoon) sector relative to prenoon (postnoon). In this study, we use global MHD simulations to determine the extent to which the dayside asymmetry in Alfvénic energy flux is controlled by the interplanetary magnetic field (IMF) and gradients in ionospheric conductance. Most of the dayside Alfvénic power is generated on open magnetic field lines, so we might expect the IMF By component to exert a significant influence on pre/postnoon flux distributions. The simulation experiments confirm this expectation and further show that the effects of gradients in ionospheric conductance are comparatively weaker. The results explain why some satellite observations on average exhibit a asymmetric enhancement in Alfvénic energy fluxes. Key Points: Dawn‐dusk asymmetry in dayside Alfvénic power is due to different magnetic field configurations associated with the direction of IMF ByIonospheric conductance contributes less to the asymmetric dayside Alfvénic Poynting flux distribution around noon [ABSTRACT FROM AUTHOR]

To improve the forecast accuracy of wind power, diffusion model based on prior knowledge (DMPK) is proposed. Different from the traditional diffusion model (DM), where the noise perturbation in the diffusion or generation process is random, the noise added in DMPK is modified aiming to the characteristics of wind power signals. The distribution of wind power forecast errors is not a standard Gaussian. Wind power forecast errors are related to forecast methods, weather conditions, and other factors, containing both random signals and certain regularity. This paper adapts the Gaussian distribution to fit the historical forecast error to represent the prior knowledge of wind power. Then, the sampling distribution is derived from its relationship with the fitted prior distribution to replace the standard Gaussian in DM. Taking the prior knowledge into account during the process of noise sampling, the data in the forward process of DMPK can be guided by the distribution of historical errors for diffusion, while the generated result by the reverse process is more consistent with the actual wind power signal. Finally, the superiority of the proposed method is verified by using the wind power data from two real‐world wind farms. [ABSTRACT FROM AUTHOR]

It is tough and complex to forecast wind speed due to its intermittent and stochastic nature as well as sudden and abrupt variations in the wind speed. Further, it is required to handle the variety of scenarios e.g. cyber‐attacks, unexpected power device malfunction, communication/sensor outages etc. that can cause the missing data.This paper proposes and employs a de‐noising autoencoder algorithm for wind speed forecasting to ensure the handling of missing data information. At the next step, the data is processed via variational mode decomposition technique to mitigate the noise and improves the model's prediction accuracy. Furthermore, the bi‐directional long‐short term memory deep learning approach is tied with convolution neural network to increase prediction accuracy and anticipating the sudden/abrupt changes in wind speed accurately. Finally, actual wind speed related data is examined to scrutinize meticulousness of projected forecast methodology particularly during sudden/abrupt changes in the wind speed. The parameter indicators of the wind speed forecasting technique exhibit the capability of improved predictions under the diversified conditions. [ABSTRACT FROM AUTHOR]

A major obstacle standing in the way of full‐scale adoption of renewable energy sources is their intermittency and seasonal variability. To better understand the power generation dynamics, the effect of air density due to temperature on power and energy generation figures was modelled. The model uses historical ERA5 data and considers changes in weather patterns in a subarctic climate where seasonal changes are most pronounced. The power generation figures of using a mean and a dynamic air density value were compared and the results show that power generation estimates may be under‐ and overestimated by on average 5% up to 10% in winter and summer, respectively. This can have implications for the sizing of power transmission infrastructure and energy storage in both on‐grid and off‐grid applications as well as power availability. The topic is highly relevant in the Nordic countries where roughly 10% of new global added wind capacity is installed annually. [ABSTRACT FROM AUTHOR]

Continuous fault ride‐through (CFRT) issues often arise in wind power systems. CFRT results in continuous voltage fluctuations which is characterized by "initially decreasing and then increasing" and can significantly disrupt the normal operation of wind power systems. To mitigate CFRT related problems, one approach is the utilization of energy storage (ES) based dynamic voltage restorers (DVRs). Nevertheless, the prohibitive costs and substantial ES requirements hamper practical implementation. In this article, a control scheme incorporating adaptive mode switching and coordinated control is proposed. First, the adaptive mode switching control leverages the advantages of two DVR compensation methods to reduce the injected voltage amplitude. The mode switching is activated by the DC link voltage and utilizes the PQR transformation to unlock the phase‐locked loop (PLL). Subsequently, an adaptive coordination coefficient is introduced, which considers the margin of ES regulation power and rotor inertia, to maintain power balance during CFRT. This proposed control strategy not only enables wind turbines to seamlessly ride through continuous faults without disconnecting from the grid but also leads to a reduction in the ES compensation requirement. To validate the effectiveness of the proposed approach, simulations based on Simulink and hardware‐in‐loop (HIL) experiments are conducted. [ABSTRACT FROM AUTHOR]

This article aims to investigate different dc‐bus voltage balancing control modes for a modular HVDC drive train applied to offshore wind energy. The modular structure discussed consists of a stacked polyphase bridge converter connected to the modules of a segmented generator. Three voltage control modes are analysed: non‐voltage balancing, voltage balancing allowing overcurrent, and voltage balancing with power derating. Assembly imperfections and operation differences can cause parametric deviations across modules leading to generation unbalance. When operating with voltage balancing with power derating, none has ac overcurrent or dc overvoltage. However, when the turbine is already operating at nominal power, a reduction in total power is required for this to occur. On the other hand, non‐voltage balancing or voltage balancing allowing overcurrent could bring more generated power to the system as the optimal output power allows dc overvoltage or ac overcurrent in some of the modules, respectively. Therefore, there is a trade‐off between efficiency and oversizing the system's components. A sensitivity analysis is performed to identify the expected dc‐bus voltage deviation. Experimental results in a low‐power prototype validate the voltage balancing control modes and a case study demonstrates the impact on energy yield in an offshore wind turbine. [ABSTRACT FROM AUTHOR]

Large‐scale integration of renewable energy into the power grid results in a lack of system inertia, posing challenges to the optimal operation and scheduling of systems considering frequency stability. This article proposes a unit commitment model that considers both inertia constraints and the uncertainty of load and renewable energy. First, the time‐domain expression of the system frequency response is calculated based on the aggregated System Frequency Response (SFR) model, considering the system's maximum frequency deviation and the maximum Rate of Change of Frequency (RoCoF) limit. This calculation determines the minimum inertia requirement for the system. Furthermore, inertia constraints suitable for mixed‐integer programming model are derived to address the nonlinearity of conventional frequency constraints. Second, considering the uncertainties of load and wind energy from renewable sources, a unit commitment model with inertia constraints is constructed, and a robust method is used to solve the uncertainties. Finally, the accuracy of the proposed inertia constraints and unit commitment model is validated using case study of IEEE standard test cases and a provincial power grid in China. [ABSTRACT FROM AUTHOR]

Intelligent fault diagnosis for wind energy systems requires identifying unique characteristics to differentiate various fault types effectively, even when data discrepancy occurs due to the unpredictable and dynamic nature of its environment. This article addresses some of the challenges of fault classification in wind energy systems by proposing an integrated approach that combines deep learning features with a resampled supervisory control and data acquisition (SCADA) dataset. The methodology involves resampling the imbalanced SCADA dataset using synthetic minority oversampling technique (SMOTE) and near‐miss undersampling techniques, extracting deep learning features using deep convolutional neural network, and feeding them into an XGBoost (extreme gradient boosting) classifier with tuned parameters using adaptive elite‐particle swarm optimization (AEPSO). The effectiveness of the proposed method is demonstrated through validation conducted on a different imbalanced dataset showing superior performance metrics in terms of accuracy. Additionally, the study contributes to methodological advancements in wind turbine fault diagnosis by providing a rigorous framework for fault classification. It is confirmed that utilizing the extracted deep learning features into the resampled data can significantly affect the classification performance metrics. Furthermore, the proposed integrated approach shows significance for fault diagnosis enhancement in wind energy systems and advancing the field towards more efficient and reliable operation. [ABSTRACT FROM AUTHOR]

This paper investigates the robust model predictive load frequency control problem for smart grids with wind power under cyber attacks. To accommodate intermittent power generation, the demand response of the system is considered by involving the air conditioning loads in the frequency regulation. In addition, the system uncertainties produced by the air conditioning load users and wind turbines when replacing traditional generator sets are considered. By using the cone complementary linearization algorithm and the linear matrix inequality technique, a resilience robust model predictive control strategy with mixed H2/H∞$H_2/H_\infty$ performance indexes is proposed. Furthermore, a rigorous derivation of the recursive feasibility of robust model predictive control is given. Finally, the simulation results of the two‐area load frequency control scheme show that the proposed model predictive control strategy is capable of realizing the load frequency control of the multi‐area smart grid and is robust to the parameter uncertainties and frequency regulation of the system. The results also show that the proposed model predictive control strategy has some resistance to cyber attacks and external disturbances. [ABSTRACT FROM AUTHOR]

The threat of long‐term low wind output processes (LWOP) on the supply ability of the power system is escalating with the increasing integration of wind power. Accurate prediction of LWOP is crucial for maintaining the stable operation of the power system. However, the occurrence probability of LWOP is low and the available samples are lacking, limiting the high‐accuracy predictive modeling of LWOP. Therefore, a novel prediction method for LWOP under very few samples based on improved Wasserstein deep convolutional generative adversarial networks (W‐DCGAN) is proposed here. Firstly, a multi‐dimensional identification method is proposed to accurately identify historical LWOP. Then, an LWOP sample generation model based on improved W‐DCGAN is established. The model integrates a long short‐term memory layer into the deconvolutional layer of the generator to enhance the temporal characteristics of generated samples. Finally, three prediction algorithms are used to construct LWOP prediction models based on both generated and actual samples, respectively. The wind power operation data from a province in China is taken as an example to verify the effectiveness of the proposed method. The results show that the prediction accuracy of LWOP can be improved by 14.36%–55.85%. [ABSTRACT FROM AUTHOR]

Accurate estimation of wind power is essential for predicting and maintaining the power balance in the power system. This paper proposes a novel approach to enhance the accuracy of wind power estimation through a hybrid model integrating neural networks and error discrimination‐correction techniques. In order to improve the accuracy of estimation, a bidirectional gating recurrent unit is developed, forming an initial wind power estimation curve through training. Additionally, a sequential model‐based algorithmic configuration optimizes bidirectional gating recurrent unit's network hyperparameters. To tackle estimation errors, a multi‐layer perceptron combined with sequential model‐based algorithmic configuration is employed to create a classification model that automatically discerns the quality of estimates. Subsequently, an innovative correction model, based on grey relevancy degree and relevancy errors, is devised to rectify erroneous estimates. The final estimates result from a summation of the initial estimates and the values derived from error corrections. By analysing the real data from a wind farm in northwest China, a simulation test validates the proposed hybrid model. Experimental results demonstrate a substantial improvement in modelling accuracy when compared to the initial model. [ABSTRACT FROM AUTHOR]

Because of its inherent properties, the use of thermoplastic composites is expanding in a number of industries, including aerospace, rail transportation, and wind power generation. However, the development of 2D and 3D reinforced thermoplastic composite structures is hampered by the high viscosity and poor wetting of thermoplastic matrices, resulting in their application being mainly limited to nonload‐bearing components. This article provides a comprehensive overview of the technology and current applications of thermoplastic composites, covering materials, preform structures, manufacturing methods, process parameters, performance, and applications. It also provides an outlook on their future development. Highlights: Thermoplastic composite has economic and environmental benefits.Summarized the current technology and application of thermoplastic composites.The manufacturing difficulty is the thermoplastic polymers wet fibers.One promising approach to the issue is prepreg technology.Prepreg technology make 3D thermoplastic composites become possible. [ABSTRACT FROM AUTHOR]

Human safety is the most important factor to determine any grounding system, therefore low‐frequency grounding resistance (LFGR) of wind power generation systems should be very low. Lightning protection is another factor for the grounding system design. Grounding conductors (GCs) are used to reduce the LFGR. A long GC however, does not reduce peak voltages for lightning currents with steep‐front waveshapes. Thus, the GC length is an important factor for an effective grounding system. This paper proposes a method for determining the effective length of GC based on traveling wave theory in time domain. Based on this calculation, it describes a grounding system design for wind power generation systems that considers both, the LFGR and the lightning protection. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Reliable and highly resolved information about onshore wind energy potential (WEP) is essential for expanding renewable energy to eventually achieve carbon neutrality. In this pilot study, simulated 60 m wind speeds (ws60m) from a km‐scale, convection‐permitting 3.3 km‐resolution ICON‐LAM simulation and often‐used 31 km‐resolution ERA5 reanalysis are evaluated at 18 weather masts. The estimated ICON‐LAM and ERA5 WEPs are then compared using an innovative approach with 1.8 million eligible wind turbine placements over southern Africa. Results show ERA5 underestimates ws60m with a Mean Error (ME) of −1.8 m s−1 (−27%). In contrast, ICON‐LAM shows a ME of −0.1 m s−1 (−1.8%), resulting in a much higher average WEP by 48% compared to ERA5. A combined Global Wind Atlas‐ERA5 product reduces the ws60m underestimation of ERA5 to −0.3 m s−1 (−4.7%), but shows a similar average WEP compared to ERA5 resulting from the WEP spatial heterogeneity. Plain Language Summary: Onshore wind energy is expected to play a major role in the global energy transition. However, reliable and highly resolved information on the onshore wind energy potential (WEP) crucial for expansion planning is missing over southern Africa. This study evaluated high resolution 3.3 km ICON‐LAM atmospheric simulations and 31 km ERA5 reanalysis against 60 m wind speed (ws60m) observations and compared the corresponding derived WEPs. The results show that ERA5 underestimates ws60m by 27%, resulting in a 48% lower WEP assessment than ICON‐LAM, whose ws60m simulation results show a very small bias. Underestimation of wind energy yields may hinder further expansion of wind energy, as less economic performance is expected, which underlines the importance of highly resolved weather data. Key Points: Simulated ERA5 and km‐scale ICON‐LAM wind speeds are evaluated and corresponding southern Africa wind energy potentials are calculatedERA5 underestimates 60 m wind speed, whereas ICON‐LAM produces lower biases in the wind speed simulationsHigher wind energy potentials are revealed from wind speeds simulated by ICON‐LAM compared to ERA5, which is often used for such assessments [ABSTRACT FROM AUTHOR]

In response to growing reliance on electricity and gas systems, this paper introduces a stochastic bi‐level model for the optimized integration of these systems. This integration is achieved through sizing and allocating of power‐to‐gas (P2G) and gas‐to‐power (G2P) units. The first level of the model focuses on decisions related to P2G and G2P unit installations, while the second level addresses optimal system operation considering decisions made from first level and stochastic scenarios. The primary aim is to enhance energy‐sharing capabilities through coupling devices and mitigate wind generation curtailment. An economic evaluation assesses the model's effectiveness in reducing costs. N − 1 contingency analysis gauges the integrated system's ability to supply load under emergency conditions. Two new indices, performance of the electricity system and performance of the natural gas system, are proposed for N − 1 contingency analysis. These indices quantify the proportion of the supplied load to the total load, thereby illustrating the system's capacity to meet demand. For numerical investigation, the proposed model is applied to a modified IEEE 14‐bus power system and a 10‐node natural gas system. Numerical results demonstrate a 9.426% reduction in investment costs and a significant 10.6% reduction in wind curtailment costs through proposed planning model. [ABSTRACT FROM AUTHOR]

Wind farms are a clean and efficient source of renewable energy. However, they cause negative impacts on raptors. Here, we present a review of the existing scientific literature on the effects of wind farms on raptors' ecology with a particular interest in the potential solutions. After collecting 216 studies, we found a consensus in the literature that raptors exhibit avoidance behaviors, and that the abundance of raptors decreases after wind farm installation, although it might recover over time. The position of wind farms on mountaintop ridges poses a particular danger to large soaring raptors, as they rely on orographic uplift to gain altitude. Adult mortality significantly affects population dynamics, particularly in endangered species, but young inexperienced individuals show a higher collision risk. The combination of different methods including field monitoring, GPS telemetry and systematic search for carcasses is an adequate approach to further investigate the problem and solutions. Shutdowns on demand, the installation of deterrents, turbine micro‐sitting and the repowering of wind farms have been suggested as potential solutions, although results are contradictory and case‐specific. Furthermore, it is essential to report the potential occurrence of conflicts of interest in scientific papers, as they can influence the interpretation of the results. Finally, from a future perspective, it is crucial to assess the effectiveness of solutions to mitigate the negative effects of wind farms to promote raptor conservation. This becomes increasingly relevant in the context of renewable energy development and increasing energy demand worldwide. [ABSTRACT FROM AUTHOR]

ABSTRACT The contribution explores the significance of employing dry‐air technology as an efficient method for controlling and mitigating corrosion inside offshore wind turbine nacelles and towers. The article describes a dry‐air approach that curtails the necessity for costly corrosion protection measures and it explains its ability to diminish and regulate relative humidity. Additionally, it is outlined how the utilization of a dry‐air strategy results in the creation of an internal overpressure within the wind turbine structure. This overpressure effectively serves as a barrier against the ingress of moisture‐laden, salt‐infused air. Comparative site‐sensor data are presented and discussed for two dehumidification techniques. Finally, practical solutions are presented. [ABSTRACT FROM AUTHOR]

Accurate wind speed modeling is beneficial for the design of wind energy conversion systems. Models of wind speed are used to assess the adequacy and dependability of a power supply. However, precise wind speed modeling is challenging due to the sporadic availability of wind speed. In this note, we propose a wind speed model with an autoregressive (AR) structure. A hybrid model is developed under linear and nonlinear parts based on a quasi‐linear autoregressive exogenous neural network (Q‐ARX‐NN). The model's structure is composed of a regression vector and its coefficients. The coefficients are divided into linear and nonlinear coefficients. A set of linear coefficients is identified under the algorithm of least square error (LSE), and a set of nonlinear coefficients is modeled by using a neural network to refine the residual error of the nonlinear part. Some artificial neural network (ANN) models can be set as nonlinear part sub‐models to sharpen the model's accuracy. The proposed model is tested for wind speed modeling to estimate wind energy production. Various nonlinear parts of the sub‐model are tested, such as neural networks, radial basis function networks, and ANN networks. Moreover, we evaluate the effects of the order of the model by varying hidden and output nodes, which can be summarized as the number of coefficients of the regression vector. Using specific wind turbine performance data, prediction models estimate production power. © 2024 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

The rapid development of offshore wind farms (OWFs) calls for economical and reliable power transmission technology. This review paper focuses on the diode‐rectifier‐unit based high voltage direct current (DRU‐HVDC) transmission systems. The main technical features of DRU‐HVDC are highlighted and the comparisons with MMC‐HVDC and LCC‐HVDC are conducted. Considering the uncontrollability of DRU and the necessity of offshore wind turbines (WTs) to establish offshore network, the existing decentralized and centralized control strategies are reviewed in detail. For the fully‐grid‐forming converter, the operation principle of the P–V and Q–f power control is explained from the OWF‐DRU level and the WT level, respectively. For the partial‐grid‐forming converter, why the voltage control loop can be eliminated is analysed. Focusing on the start‐up issue caused by the unidirectional‐power‐flow characteristic of DRU, several start‐up solutions are reviewed, including medium voltage alternative current (MVAC) umbilical cable, auxiliary MMC, energy storage for WTs, auxiliary low voltage DC cable. Then considering different fault locations, the fault characteristic and protection of the DRU‐HVDC system is presented, including the offshore AC faults, DRU faults, HVDC faults and onshore AC faults. Finally, the future research topics worth exploring for DRU‐HVDC system are identified. [ABSTRACT FROM AUTHOR]

Wind farm can obtain the maximize profit by optimizing micro‐locations and cables. The factors that affect profit include the power output of wind turbines, cost and et al., where power output is affected by wake effect, cable cost is related to the length and type of collector cable. The profit is calculated on the premise that the costs and power loss of collector cable are determined. Obviously, there is a hierarchical relationship between the above problems. Therefore, a bi‐level optimization model with constraints is constructed in this paper, where the upper‐level objective function is the maximum profit, and the lower‐level objective functions are consists of minimum the cable cost and the power loss of collector cable; Moreover, an improved algorithm (IDEDA), based on differential evolution and Dijkstra, is used to optimize above model; Finally, simulation experiments are carried out for IDEDA and four algorithms for two different wind conditions, and the results show that IDEDA performs better compared to the other four algorithms in terms of profit and cable cost. [ABSTRACT FROM AUTHOR]

This paper investigates the benefits and challenges of the multi‐MW direct‐drive offshore wind Vernier generators. It is worth noting that the comparison of generator topologies presented in ref. [1] was for the same power level and therefore a reduced machine volume for the surface‐mounted permanent magnet Vernier (SPM‐V) generators. This would mainly impact the capital expenditure (CAPEX) cost between the different investigated machines. Whereas in this paper, the performance is compared for the same machine volume that allows the Vernier generators to produce a much higher energy yield than a conventional SPM generator. As the extra energy yield would be beneficial over the lifetime of the turbine, this will have a bigger impact than the CAPEX cost savings with a reduced machine volume. In addition, a novel Vernier machine with magnets on both the stator and rotor has been proposed to further improve the energy yield. In addition to the basic electromagnetic performance, the levelised cost of energy (LCOE) of the three generator topologies, that is, the conventional SPM, SPM‐V and the proposed Vernier machines, has been compared. The direct‐drive powertrain systems with SPM‐V and the proposed Vernier generators can achieve LCOE of 12.3% and 24% lower than that of the conventional SPM generators, indicating their huge potential as an alternative to reduce the overall cost of energy. [ABSTRACT FROM AUTHOR]

This article presents an analytical model for the diurnal cycle of slope‐normal profiles of potential temperature and wind speed characterizing thermally driven slope winds, generated by a daily‐periodic surface energy budget. The model extends the solution proposed by Zardi and Serafin, originally formulated for a pure sinusoidal surface forcing temperature. To account for the asymmetric features characterizing the daytime and nighttime phases, a full Fourier series expansion is derived, the coefficients and phases of which are prescribed from the surface energy budget driven by the daily‐periodic radiation model described in Part 1 of the present work. The model is applicable for any slope angle (0∘≤α≤90∘$$ {0}^{\circ}\le \alpha \le {90}^{\circ } $$) and orientation, at any latitude and elevation (up to 2500 m), and for all seasons. Despite some inherent limitations, the most remarkable being the absence of moist processes and latent heat fluxes, the model captures most key features of daily‐periodic slope wind systems, in particular the asymmetry between daytime and nighttime phases. Moreover, it allows exploration of the sensitivity of these flows to the various factors concurring in their development, and offers a basis for more realistic analytical solutions for slope winds. [ABSTRACT FROM AUTHOR]

Wind power fluctuation significantly impacts the safe and stable operation of the wind farm power grid. As the installed capacity of grid‐connected wind power expands to a certain threshold, these fluctuations can detrimentally affect the wind farm's operations. Consequently, wind power prediction emerges as a critical technology for ensuring safe, stable and efficient wind power generation. To optimize power grid dispatching and enhance wind farm operation and maintenance, precise wind power prediction is essential. In this context, we introduce a joint deep learning model that integrates a compact pyramid structure with a residual attention encoder, aiming to bolster wind farm operational safety and reliability. The model employs a compact pyramid architecture to extract multi‐time scale features from the input sequence, facilitating effective information exchange across different scales and enhancing the capture of long‐term sequence dependencies. To mitigate vanishing gradients, the residual transformer encoder is applied, augmenting the original attention mechanism with a global dot product attention pathway. This approach improves the gradient descent process, making it more accessible without introducing additional hyperparameters. The model's efficacy is validated using a dataset from an actual wind farm in China. Experimental outcomes reveal a notable enhancement in wind power prediction accuracy, thereby contributing to the operational safety of wind farms. [ABSTRACT FROM AUTHOR]

In recent years, hybrid towers have become increasingly established, especially for tall wind turbines. The concrete part of these towers, commonly constructed using segmental construction methods, consists of several circular ring segments stacked on top of each other. To reduce the amount of fabrication and erection effort, the joints between the segments are typically designed as dry ground joints. This paper deals with experimental investigations to determine the torsional load‐bearing capacity of such dry ground concrete joints subjected to combined loading of prestressing, bending, and torsion. Based on the experience of previous investigations, a test set‐up consisting of three reinforced concrete segments with circular ring cross‐sections was developed. The herein newly developed evaluation concept enabled precise identification of the failure point for each test run utilizing the measured tangential displacements of the joint. The test results clarify the weaknesses of the current design model regarding the insufficient interaction with the bending moment and show very good accordance with the new calculation model, the so‐called fiber model. [ABSTRACT FROM AUTHOR]

The combined deployment of a PV and SW system in a residential building with an electric vehicle The interest in independent and sustainable energy supply is steadily increasing in times of continuously rising electricity prices and growing environmental awareness. While photovoltaic (PV) systems have almost become commonplace on roof surfaces and balcony railings, small wind (SW) turbines are still relatively rare. This is despite the fact that wind power accounts for over 50 % of the renewable energy generated in Germany and is subject to significantly lower fluctuations throughout the year compared to solar radiation. The aim of the calculations presented in this article therefore was to exemplarily examine whether the combined use of photovoltaic and small wind turbine systems on residential buildings represents both an energetically and an economically viable course of action. It was also necessary to analyse the importance of using and charging an electric vehicle in the context of the system combination. The resulting economic efficiency of the combined systems was determined by comparing software‐based yield forecasts for a specific location with the load profile of an average 3‐person household. [ABSTRACT FROM AUTHOR]

To effectively manage turbine blade weight and blade deflection under severe weather conditions, longer and stiffer blades are required, fiber glass producers have devoted significant efforts to developing and commercializing high‐modulus (HM) glass fiber products of the first generation. The current focuses aim at the commercialization of the second generation and the development of the third‐generation products. This article briefly reviews four key areas: (a) the benefit of longer blades on wind energy generation, (b) characteristics of HM glass fibers of various generations, (c) fundamental science and understanding behind HM glass fiber development, and (d) finally statistically based composition (C)–property (P) and structure (S)–property (P) modeling approaches in new glass design. [ABSTRACT FROM AUTHOR]

Wind renewable energy (WRE) is an essential component of the global sustainable energy portfolio. Recently, there has been increasing discussion on the potential supplementation of this conventional mitigation portfolio with Solar Radiation Modification (SRM). However, the impact of SRM on conventional mitigation measures has received limited attention to date. In this study, we explore one part of this impact, the potential effect of one type of SRM, Stratospheric Aerosol Injections (SAI), on WRE. Using hourly output from the Earth System Model CNRM‐ESM2‐1, we compare WRE potential under a medium emission scenario (SSP245) and a high emission scenario (SSP585) with an SRM scenario that has SSP585 baseline conditions and uses SAI to offset warming to approximately SSP245 global warming levels. Our results suggest that SAI may affect surface wind resources by modifying large‐scale circulation patterns, such as a significant poleward jet‐shift in the Southern Hemisphere. The modeled total global WRE potential is negligibly reduced under SAI compared to the SSP‐scenarios. However, regional trends are highly variable, with large increases and decreases in WRE potential frequently reaching 12% across the globe with SAI. This study highlights potential downstream effects of SRM on climatic elements, such as wind patterns, and offers perspectives on its implications for our mitigation efforts. Plain Language Summary: Wind renewable energy (WRE) is a pivotal element of the global transition toward sustainable energy. Recently, there has been a surge of interest in Solar Radiation Modification (SRM) as a potential means of managing climate impacts, but its effects on existing strategies like WRE have not been much addressed in the current literature. This research examines the potential impact of one type of SRM, Stratospheric Aerosol Injections (SAI), on WRE potential. An Earth System Model is used to compare WRE potential under three climate scenarios: one with moderate emissions, one with high emissions and one where SAI is used to reduce warming in the high‐emission scenario to match the moderate‐emission global warming level. The findings suggest that SAI may result in a minimal reduction in long‐term global mean WRE potential. However, the effects vary considerably by region and season, with areas experiencing changes in WRE potential that frequently reach approximately 12%. This research highlights the potential for SAI to exert complex effects on wind patterns, which could influence the future of renewable energy strategies. Key Points: Stratospheric Aerosol Injections do not fully compensate for atmospheric circulation changes from climate change but create new dynamicsTotal global wind energy potential is negligibly reduced under Stratospheric Aerosol Injections but regional trends tend to vary substantiallyResults are based on hourly output from CNRM‐ESM2‐1. Changes are likely to be larger in other GeoMIP models in the northern hemisphere [ABSTRACT FROM AUTHOR]

In this paper, the performance of a wind energy‐supported hybrid hot air dryer was examined experimentally. Wind energy provided overall electrical energy needed for the activation of the dryer. In the experiments, the drying behavior of white cabbage leaves with the same surface area and three different geometries: circle, rectangle, and square cross‐section was examined using three different constant temperatures (50°C, 60°C, and 70°C), and two different constant air speeds (0.5 and 1 m/s). The main purpose of using different geometries during the drying process was to determine the optimum cross‐section and working parameters for white cabbage leaves. The based aim of the study was to contribute to both increasing the energy efficiency of processes that cause high energy consumption, such as drying, and ensuring their continuity throughout the four seasons, by using a clean renewable energy source such as wind. Consequent the experiments, it was seen that the drying time decreased by approximately 52.63% and the energy consumption reduced by approximately 47.9% with the increase in temperature and air speed in rectangular‐sectioned cabbages compared to other geometries. In addition, in the longest‐term experiment, a maximum of 40% of the stored energy was used. This revealed that the developed hybrid system has approximately 52.5% higher energy efficiency than other drying units supporting with PV panels and also provides an average at the rate of 45% advantage in terms of product drying time. [ABSTRACT FROM AUTHOR]

The planned expansion of wind energy in the German Bight is creating much more densely staggered wind farms and wind farm clusters. This results in a significantly greater influence of the generated wakes on energy production of neighboring wind farms. The Dornier‐128 research aircraft operated by the Technische Universität of Braunschweig was used to measure the wind field in the lee of single and multiple wind farm clusters in the German Bight on 4 days during July 2020 and July 2021. The data at 120 m aMSL (above mean sea level) were analyzed to identify wake areas and the wind speed decrease behind the wind farm clusters. The observations were then compared to a range of numerical data including the mesoscale model Weather Research and Forecasting (WRF) applying a wind farm parameterization (WRF with wind farm parameterization [WRF‐WF]) to model wake effects and an engineering model with different setups. A model calibrated on a single wind farm is established as the baseline. A modification with a lower wake recovery, the TurbOPark model, and a WRF‐coupled model make up the three additional declinations considered. Overall, the models compared well to the measurement data in the direct vicinity of the wind farms and up to 20–30 km downstream of the wind farm clusters. The accuracy in wind speed prediction of the model results decreased with distance to the wind farms, where the mesoscale model (WRF‐WF) exhibited a more consistent performance across varying distances. [ABSTRACT FROM AUTHOR]

Offshore wind power is an important technology for low‐carbon power grids. To improve the accuracy, a short‐term offshore wind power prediction method based on significant weather process classification and multitask learning considering neighboring powers is presented in this paper. First, a novel weather process classification method, in which the samples are divided into pieces of waves based on extreme points and are quantified with labels of energy level and fluctuation level, is proposed to classify samples into multiple types of significant weather processes for independent modeling. Second, a multitask learning method, in which the power sequences in neighboring offshore wind farms are innovatively introduced as a new input feature, is proposed for modeling wind power prediction for each wind farm inside a neighboring region under each weather process class. Case studies are presented to verify the effectiveness and superiority of the proposed method. Based on this new method, the 4‐h ultra‐short‐term root mean squared error (RMSE), 24‐h day‐ahead RMSE, 4‐h ultra‐short‐term mean absolute error (MAE), and 24‐h day‐ahead MAE can be reduced by 1.45%, 2.1%, 1.15%, and 1.85%, respectively, compared with benchmark methods, which verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

By multivariate econometric analysis of UK offshore wind farms, we demonstrate a life cycle effect of the capacity factor of offshore wind. From the bathtub curve findings in reliability engineering, we would expect a long, flat segment of stable operation in the middle of the life cycle production schedule. Our general findings deviate from the standard curve as it does not have a long segment where production is stable. Production starts falling too early. Lack of stable operation period is harmful to wind farm economics. It indicates immature technology and a potential for improvement. We examine the effect life cycle production schedules have on project economics and compare with a common assumption of a flat production schedule. We find that project profitability (net present value) is very sensitive to the life cycle profile of production. [ABSTRACT FROM AUTHOR]

This study comprises two essential components: a scoping review and interviews with fishermen, both aimed at understanding the Sustainable Development Goals (SDGs) and their relevance to Offshore Wind Energy (OWE) enterprises, particularly in mitigating impacts on local fisheries. Key findings from the scoping review indicate a scarcity of research explicitly dedicated to OWE enterprises' practices concerning. The review's findings emphasize the need for further research to understand the rationale behind these decisions and to explore how OWE enterprises can optimize their practices to address SDG 2. The second part of the study involves interviews with fishermen near proposed offshore wind farm sites in Taiwan. These interviews revealed that fishermen's primary concerns revolve around SDGs 2, 8, 14, and 17. However, OWE enterprises do not prioritize SDG 2, even though fishermen express concerns related to food security, proposing the adoption of marine ranching to enhance seafood supply. Moreover, SDG 17 stands out as a priority for fishermen, while OWE enterprises overlook connections with fisheries associations and fishermen. In conclusion, this study highlights the complexity of the SDG‐related issues surrounding OWE enterprises and their impact on local fisheries. [ABSTRACT FROM AUTHOR]

Soluble lead redox flow battery (SLRFB) is an emergent energy storage technology appropriate for integrating solar and wind energy into the primary grid. It is an allied technology of conventional lead‐acid batteries. This appraisal compares lead‐acid batteries and SLRFB apropos their general characteristics. SLRFBs can overcome the inadequate cycle‐life of Lead‐Acid batteries as the electrodes of SLRFB do not participate in the reaction, which helps extending its durability. However, SLRFB has challenges of dendrite formation, oxygen evolution reaction, passivation of PbO2 and shunt current. These problems need to be resolved before SLRFBs can be projected for large‐scale energy storage applications. In this technical update, we have reviewed the recent studies pertinent to dendrite formation, mechanism of the lead electrode, and reversibility of the PbO2 electrode in the state‐of‐art of SLRFB along with progress in advances while developing a 12 V – 250 Wh 8‐cell SLRFB stack. [ABSTRACT FROM AUTHOR]

The market coupling of adjacent market zones is a key element in terms of coping with rising levels of uncertainty in the European energy system due to the adoption of more volatile renewable energy sources. An adequate analysis of this issue yet requires an appropriate consideration of uncertainty along with a detailed modelling of market coupling. Thus, we present a novel approach combining a comprehensive unit commitment and dispatch model with a sophisticated methodology to reflect the uncertainty of wind power generation by considering forecast updates for the German market region in the market clearing process. Subsequently, we investigate the impact of uncertainty on cross‐border electricity flows in a European case study. Our results show that all adjacent market zones and all relevant technologies significantly contribute to balancing forecast updates in Germany, underlining the benefits of intraday market coupling. Further, we observe that a joint and unrestricted intraday market is the most cost‐efficient market solution. Finally, our analyses show that more extensive market coupling is indispensable for the further integration of renewable energy sources in the future. [ABSTRACT FROM AUTHOR]

The traditional control method of doubly fed induction generators (DFIGs) has poor robustness due to the excessive use of machine parameters, and does not fully consider the control conditions of nonideal power grids, resulting in serious fluctuations in current and power under grid disturbances. To solve these problems, model‐free predictive rotor current control (MFPRCC) based on an ultra‐local model is proposed in this article. The ultra‐local model is adaptive because it can accurately emulate the overall structure of the system and estimate the total disturbance of the system in real time. Under a nonideal grid, the cascaded delayed signal cancellation (CDSC) modules are connected in series behind the outer power loop to extract the fundamental component of the rotor current reference. The proposed method is compared with the traditional model predictive rotor current control (MPRCC) method, and the effectiveness of the proposed method is verified on a 1.5 kW DFIG experimental platform. [ABSTRACT FROM AUTHOR]

One of the most common faults in electric machines is a turn‐to‐turn short circuit (TTSC), which may destroy coil insulation and demagnetise the magnet. In addition, the phase‐to‐phase short circuit (PPSC) fault, which can have even more destructive effects than the TTSC fault, is introduced and analysed. The equivalent magnetic network (EMN) method, with high modelling accuracy and a short computation time, is employed for healthy and faulty machines. The current signal under fault conditions is analysed in the dqo frame, showing the presence of the second harmonic component in its waveform. This fault detection index is processed using the signal processing technique of discrete(wavelet transform (DWT). Besides, energy analysis is used to distinguish TTSC and PPSC faults. Finally, finite element and EMN modelling results are compared with the experimental data of the prototyped permanent magnet generator. The results show that the combination of the proposed EMN method and DWT has very good accuracy and speed. Furthermore, the proposed fault detection method remains unaffected by various linear loads with different power factors. The cover image is based on the article Turn‐to‐turn and phase‐to‐phase short circuit fault detection of wind turbine permanent magnet generator based on equivalent magnetic network modelling by wavelet transform approach by Mehrage Ghods et al., https://doi.org/10.1049/elp2.12452 [ABSTRACT FROM AUTHOR]

In the environment, there is an abundance of gust energy which is challenging to harvest with conventional rotating wind turbines, such as the gusts generated by passing vehicles along roadsides. Addressing the irregular and low‐frequency characteristics of gusts, a bladeless wind turbine triboelectric nanogenerator (BWT‐TENG) with enhanced aerodynamic performance is proposed, enabling effective harvesting of random gust energy. First, a bladeless wind turbine with a cylindrical bluff body shape is designed, and its aerodynamic principles under gust‐driven conditions are elucidated through the computational fluid dynamics method. Subsequently, parameter optimization is conducted for the multilayered TENG. Systematic experiments demonstrated that the BWT‐TENG achieved a peak power density of 4.08 W m−3 driven by a gust of 10 m s−1, and can even operate at frequencies as low as 0.1 Hz. Finally, experiments showcased the BWT‐TENG powering a warning light in a simulated rainfall environment and harvesting gust energy from vehicles passing by real roadside to power wireless gyroscopic sensors, thereby achieving self‐powered structural health monitoring of roads or bridges. This work provides a novel strategy for utilizing TENGs in the harvest of environmental gust energy and demonstrates the vast potential of TENGs in the field of self‐powered structural health monitoring. [ABSTRACT FROM AUTHOR]

Thermal stratification can cause various water quality issues in large water bodies. To address this, a new wind‐powered artificial mixing system is designed and experimentally tested for various Savonius rotor combinations (three‐stage and four‐stage rotors). These turbines directly utilize wind energy to draw air into the water column for aeration, bypassing the need for electrical conversion. The rotor performances were tested in terms of power and torque coefficients. Additionally, these rotors were tested for artificial mixing efficiencies in a specially designed water tank that can mimic thermal stratification typically observed in an actual water supply reservoir. Among the rotors, the three‐stage rotor with a 60° phase shift was found to exhibit superior power and torque coefficients, achieving a power efficiency value of 0.14. As for the mixing efficiency, the four‐stage rotor with a 45° phase shift excelled in mixing efficiency, reaching 95%. Practitioner Points: A new wind‐powered artificial mixing system is designed and tested for various Savonius rotor combinations.While keeping the total rotor height constant, the three‐stage Savonius rotor class shows superior performance against the four‐stage Savonius rotor class in terms of power and torque efficiency.Apart from the rotor performance results, the four‐stage Savonius rotors show greater artificial mixing efficiency than the three‐stage Savonius rotors.Single‐pump/diffuser artificial destratification system exhibits better mixing efficiency than multiple‐pump/diffuser systems. [ABSTRACT FROM AUTHOR]