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A joint maintenance decision-making framework is proposed to optimise the long-term maintenance plan and lower the maintenance cost for offshore wind farms. The historical wind speed data are screened by using the method of k-means clustering, and Markov chains are established for the wind speed in different seasons. On this basis, the approach of Markov chain Monte Carlo is applied to simulate the distribution of repair vessel's waiting time for maintenance, where the impact of wind speed on maintenance availability is considered. Moreover, the components in wind turbines are divided into four states according to their effective ages, i.e. young, mature, old and failed, respectively. A maintenance decision model is established, with the objective to minimise maintenance cost. Besides, three types of opportunistic maintenance are considered, i.e. failure-based opportunistic maintenance (FBOM), event-based opportunistic maintenance (EBOM) and age-based opportunistic maintenance (ABOM), respectively. The enhanced elitist genetic algorithm (SEGA) is adopted to solve the optimisation problem. The results indicate that among the three types of opportunistic maintenance, ABOM can reduce maintenance cost more effectively, and it is more suitable for long-term maintenance plans of offshore wind farm. [ABSTRACT FROM AUTHOR]

Maritime transportation is an essential component of international trade and global economic growth: according to the 2021 Review of Maritime Transport, transportation by sea is responsible for more than 80% of the global product flow. To facilitate decarbonization and reduce pollutant emissions in the maritime sector, the International Maritime Organization (IMO) has set very ambitious targets, and hydrogen is one of the most promising energy vectors capable of supporting the required low-carbon energy transition. Recently, therefore, more attention has been paid to finding technical solutions for the development of hydrogen-based alternative propulsion systems and, at the same time, a hydrogen distribution infrastructure suitable for shipping. In this scenario, this study aims to analyze, through design, modeling and optimization approaches, the energy and economic performance of an offshore wind power plant integrated with a liquid hydrogen production system to be used for refueling ships far from ports. This study focuses on the development of an optimization procedure dedicated to finding the best technical solution in terms of component sizes and management strategies that ensure the minimum Levelized Cost of Hydrogen (LCOH). have highlighted that the proposed innovative plant configuration for the offshore liquid hydrogen production is able to produce 317 tons per year of green hydrogen with a LCOH of 16.77 €/kg by using 19 MW offshore wind farm and 5 MW PEM electrolyzer. [Display omitted] • Modeling of a liquid hydrogen production system powered by an offshore wind farm. • Analysis of energy and economic performances of the proposed system. • Identification of the optimal plant configuration based on the minimum hydrogen cost. • Investment, operating, maintenance and replacement costs breakdown assessment. • The levelized cost of hydrogen in the optimal configuration is 16.77 €/kg. [ABSTRACT FROM AUTHOR]

The coexistence between offshore wind and fisheries has raised questions about potential impacts on species that are fished. We systematically evaluated the offshore wind farm (OWF) literature for evidence of effects leading to impacts on commercial fisheries species. First, we collated evidence of environmental effects of OWFs on fisheries species and then determined whether these could be interpreted as impacts using fishery‐scale and organism‐scale parameters for pelagic finfish, demersal and reef‐associated roundfish, demersal flatfish, elasmobranchs and shellfish. We appraised consistency and level of agreement of direct evidence and explored the body of indirect evidence. A total of 1268 documents featured evidence of OWF effects on fisheries species, with only 60 documents (274 species records) providing direct evidence. Evidence on finfish far outweighed that for shellfish. Demersal and reef‐associated roundfish were the best‐studied group, while elasmobranchs were poorly evidenced. Most studies considered population rather than stock parameters. There was limited evidence of impacts, owing to inconclusive results and inconsistent effects within the parameters assessed—illustrating the importance of looking across the evidence base rather than focussing on individual studies. Hence, there is currently insufficient direct evidence to confidently determine OWF impacts on fisheries species. Overwhelmingly, the evidence deals with indirect effects, although these should not be disregarded as they can highlight plausible impacts on fisheries species, which could guide research and monitoring targeted at understanding the impacts of OWF—a pressing concern given the increased policy commitment of many nations to these two marine sectors sharing marine space. [ABSTRACT FROM AUTHOR]

Scour of seabed sediments can occur around offshore foundations. For monopile-supported offshore wind turbine structures, the reduction in foundation stiffness due to scour presents certain operational challenges. In cases where scour causes the natural frequency of the structure to become, for example, close to the range of rotor loading frequencies then—due to the increased risk of fatigue damage—turbine support structures are at risk of reduced operation, or even premature decommissioning. In practice, scour protection and/or remediation systems are typically used to mitigate the development of scour. As well as preventing further erosion, scour remediation systems may have a restorative effect on the stiffness of the foundation. This paper describes a one-dimensional (1D) finite-element model for the analysis of natural frequencies for monopile-supported turbine support structures with active scour process around the foundation. The model also incorporates procedures to model the influence of a rock fill scour remediation system on the foundation stiffness. The numerical model is calibrated and validated by comparison with a set of previously described reduced-scale model tests conducted in a flume. The calibrated 1D model is applied to a field case study at a UK offshore wind farm site. [ABSTRACT FROM AUTHOR]

In order for offshore wind power plants, which are one of the renewable energy branches, to provide optimum service, to be low cost and highly beneficial, the installation, operation and maintenance stages should be carried out in a suitable port close to the power plant. In this port selection process, the criteria that ports should have differ according to the turbine structure to be installed. In the literature, there is no study on port location selection for floating wind turbines. In this study, the characteristics that ports should have for the life cycle of floating wind turbines at sea are obtained by examining the studies in the literature. For the selection of suitable ports, the Full Consistency Method (FUCOM), Best Worst Method (BWM), Common Weighting Method, Compromise Ranking of Alternatives from Distance to Ideal Solution Compromise Ranking of Alternatives from Distance to Ideal Solution (CRADIS), Combined Compromise Solution (CoCoSo) and Borda Method were used to contribute to the decision making process. Comparative analyses with other MCDM (Multi-criteria decision making) methods have been carried out to select the appropriate port. In the study, container and Ro-Ro ports in Turkey were used since the port characteristics should be provided at least at minimum level. Samsunport in the Black Sea region was found to be the most suitable port for all three port types (terminal, service and response). The criterion of having a component production facility was found to be the most important criterion for floating based offshore power plant terminal port selection. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The risk of missing data and subsequent incomplete data records at wind farms increases with the number of turbines and sensors. We propose here an imputation method that blends data‐driven concepts with expert knowledge, by using the geometry of the wind farm in order to provide better estimates when performing nearest neighbor imputation. Our method relies on learning Laplacian eigenmaps out of the graph of the wind farm through spectral graph theory. These learned representations can be based on the wind farm layout only or additionally account for information provided by collected data. The related weighted graph is allowed to change with time and can be tracked in an online fashion. Application to the Westermost Rough offshore wind farm shows significant improvement over approaches that do not account for the wind farm layout information. [ABSTRACT FROM AUTHOR]

To accurately analyze the risk resource reserves in the vicinity of wind farms and optimize the application of wind energy resources, a quantitative analysis model of wind energy resources in the vicinity of existing wind farms considering the roughness index and interference characteristics is studied. Collect and preprocess the wind energy resource data in the vicinity of existing wind farms in the national meteorological-related Internet, and build a risk resource analysis index system consisting of wind energy density, roughness index and interference characteristics according to relevant national standards; The parameter estimation method of Weibull distribution is used to calculate the density index of wind energy resources and the available hours index of wind energy resources. The roughness index is determined using the Monin–Obukhov consistent theory, and the correlation coefficient and stability are used to describe the interference characteristic index. The quantitative analysis results of wind energy resources in the vicinity of existing wind farms are obtained according to the calculation results. The experimental results show that the model can accurately calculate the roughness index and interference characteristics of the study area, and obtain high-precision quantitative analysis results for wind energy resources. [ABSTRACT FROM AUTHOR]

ABSTRACT Offshore wind energy development on the Mid‐Atlantic Bight (MAB) portion of the Northwestern Atlantic continental shelf could have adverse impacts on the future of the Atlantic surfclam, Spisula solidissima, fishery. The current and potential future areas designated for offshore wind energy development overlap with the present‐day and projected Atlantic surfclam fishing grounds and so could limit the fishery. Fishery impacts imposed by displacement of fishing outside wind farm areas and possible restrictions on vessel transit through the wind farms were simulated using a spatially explicit fishery model. The distribution of catch, hours fished, landings per unit effort (LPUE), time at sea, fishing mortality, and the number of fishing trips were projected for five time periods encompassing the period of 2016–2055. Simulations showed a significant decline in the mean of all fishery metrics (apart from LPUE) as the area of wind farm restrictions increased in scale. Impacts were consistently larger when vessel transit through and fishing within offshore wind areas were prohibited. Impacts were also larger for MAB regions off New Jersey and Delmarva than regions farther north and east. These simulations highlight the necessity of evaluating future conditions as warming temperatures shift the surfclam range relative to the immobile wind farm locations. The offshore wind industry must consider projected long‐term impacts of developmental expansion on surrounding sedentary benthic species and the commercially important fisheries that rely on them. [ABSTRACT FROM AUTHOR]

Background: Migratory bats perform seasonal movements between their summer and winter areas. When crossing ecological barriers, like the open sea, they are exposed to an increased mortality risk due to energetically demanding long-distance flights and unexpected inclement weather events. How such barriers affect bat migratory movements is still poorly known. Methods: To study bat migration patterns in response to an ecological barrier, we tagged 44 Nathusius' pipistrelles Pipistrellus nathusii with radio-transmitters on the East coast of the United Kingdom (UK) in spring 2021 and 2022. Subsequently, we assessed their movements to continental Europe using the MOTUS Wildlife Tracking System. We investigated route selection, timing of migration, overall migration speed and the influence of wind on airspeed, groundspeed and flight altitude during migratory overseas flights. Results: Barrier effects cause migratory movements along the coast, and crossings over sea are shortened by deviating from the general migration direction. Males depart from the UK later in the season compared to females. The overall migration speed of females was 61 km/day and 88 km/day after their last detection in the UK. Our estimated airspeeds during oversea flights correspond well with airspeeds measured in a wind tunnel. Bats use wind adaptively to reduce airspeed when flying under tailwind and increase airspeed when flying under crosswind conditions. Departures over sea coincidence with tailwinds, enabling bats to more than double their airspeed, reaching ground speeds of up to 16.8 m/s (60.5 km/h). Our analysis suggests that bats select altitudes with favourable wind conditions and that they seek altitudes of several hundred meters, possibly extending up to 2,500 m. Low-altitude migration occurs when wind conditions are less favourable. Conclusions: Our study demonstrates that bat migratory movements are highly influenced by barrier effects, sex-biased timing of migration and the adaptive use of winds. The results of our study contribute to a more comprehensive understanding of the decision-making process and adaptations bats employ during their migration. Elucidating bat migration patterns will enable us to develop effective conservation measures, for example in relation to the development and operation of coastal and offshore wind farms. [ABSTRACT FROM AUTHOR]

Freezing of wind turbines causes loss of wind-generated power. Forecasting or prediction of icing on wind turbine blades based on SCADA sensor data allows taking appropriate actions before icing occurs. This paper presents a newly developed deep learning network model named PCTG (Parallel CNN-TCN GRU) for the purpose of high-accuracy and long-term prediction of icing on wind turbine blades. This model combines three networks, the CNN, TCN, and GRU, in order to incorporate both the temporal aspect of SCADA time-series data as well as the dependencies of SCADA variables. The experimentations conducted by using this model and SCADA data from three wind turbines in a wind farm have generated average prediction accuracies of about 97% for prediction horizons of up to 2 days ahead. The developed model is shown to maintain at least 95% prediction accuracy for long prediction horizons of up to 22 days ahead. Furthermore, for another wind farm SCADA dataset, it is shown that the developed PCTG model achieves over 99% icing prediction accuracy 10 days ahead. [ABSTRACT FROM AUTHOR]

Long-duration energy storage systems are becoming a vital means for decarbonizing the global economy. However, with floating wind farms being commissioned farther offshore, the need to co-locate energy storage with the energy harnessing units is becoming more essential. This paper presents a transient thermal analysis of the charging process of a subsea open-cycle hydro-pneumatic energy storage system. The proposed system is designed for integration with floating wind turbines in deep water sites. Situating the system subsea presents unique opportunities for integration with offshore wind plants through the exploitation of well-known subsea pipeline technology and the surrounding seawater environment, which acts as a natural heat sink/source. The results obtained from numerical modeling in Python© Version 3.7.4 present the variation in various operating parameters with time. The outcomes reveal that the proposed system is able to achieve a work ratio and an energy storage capacity ratio of up to 0.80 and 0.95, respectively. Furthermore, the proposed open-cycle system is predicted to boost the energy storage density by a factor ranging between 2.00 and 8.10 when compared to the energy storage density of conventional closed-cycle units. Namely, the energy storage density of the long-duration energy storage can reach up to 16.20 kWh/m3 when operated in an open-cycle configuration. [ABSTRACT FROM AUTHOR]

The optimization of the efficiency of wind turbine systems is a fundamental task, from the perspective of a growing share of electricity produced from wind. Despite this, and given the complex multivariate dependence of the power of wind turbines on environmental conditions and working parameters, the literature is lacking studies specifically devoted to a careful characterization of wind farm performance. In particular, in the literature, it is overlooked that there are several types of faults which have similar manifestations and that can be defined as static errors. This kind of error manifests as a static bias occurring from a certain time onward, which can affect the anemometer, the absolute or relative pitch of the blades, or the yaw system. Static or systematic errors typically do not cause the functional failure of the wind turbine system, but they deserve attention due to the fact that they cause power production loss throughout the operation time. Based on this, the first objective of the present study is a critical review of the recent papers devoted to three types of wind turbine static errors: anemometer bias, static yaw error, and pitch misalignment. As a result, a comprehensive viewpoint, enhancing the state of the art in the literature, is developed in this study. Given that the use of data collected by Supervisory Control And Data Acquisition (SCADA) systems has, up to now, been prevailing for the diagnosis of systematic errors compared to the use of further specific sensors, particular attention in the present study is thus devoted to the discussion of the phenomena which can be observable through SCADA data analysis. Based on this, finally, a rigorous work flow is formulated for detecting static errors and discriminating among them through SCADA data analysis. Nevertheless, methods based on additional information sources (like further sensors or meteorological data) are also discussed. An important aspect of this study is that, for each considered type of systematic error, some previously unpublished results based on real-world SCADA data are reported in order to corroborate the proposed framework. Summarizing, then, the present is the first paper which considers and discusses several types of wind turbine static errors in a unified viewpoint, correctly interprets apparently controversial results collected in the literature, and finally provides guidelines for the diagnosis of this kind of error and for the quantification of the performance drop associated with their presence. [ABSTRACT FROM AUTHOR]

An enhanced understanding of the mechanisms responsible for wind turbine blade leading-edge erosion (LEE) and advancing technology readiness level (TRL) solutions for monitoring its environmental drivers, reducing LEE, detecting LEE evolution, and mitigating its impact on power production are a high priority for all wind farm owners/operators and wind turbine manufacturers. Identifying and implementing solutions has the potential to continue historical trends toward lower Levelized Cost of Energy (LCoE) from wind turbines by reducing both energy yield losses and operations and maintenance costs associated with LEE. Here, we present results from the first Phenomena Identification and Ranking Tables (PIRT) assessment for wind turbine blade LEE. We document the LEE-relevant phenomena/processes that are deemed by this expert judgment assessment tool to be the highest priorities for research investment within four themes: atmospheric drivers, damage detection and quantification, material response, and aerodynamic implications. The highest priority issues, in terms of importance to LEE but where expert judgment indicates that there is a lack of fundamental knowledge, and/or implementation in measurement, and modeling is incomplete include the accurate quantification of hydrometeor size distribution (HSD) and phase, the translation of water impingement to material loss/stress, the representation of operating conditions within rain erosion testers, the quantification of damage and surface roughness progression through time, and the aerodynamic losses as a function of damage morphology. We discuss and summarize examples of research endeavors that are currently being undertaken and/or could be initiated to reduce uncertainty in the identified high-priority research areas and thus enhance the TRLs of solutions to mitigate/reduce LEE. [ABSTRACT FROM AUTHOR]

The diffusion of offshore wind power plants (OWPPs), useful to fulfil renewable diffusion policy targets, implies several technological challenges for integration in power transmission network. The definition of the OWPP connection to the transmission network affects possible exploitation of the assets, involves an evaluation of active power losses and loadability and points out the need for compensation devices, in order to comply with connection rules fixed by the grid operator. This paper proposes a methodology to determine the most suitable electric connection for an OWPP based on high‐voltage alternating current (HVAC) cables, for providing indications for transmission system operators. In particular, different HVAC connection schemes for OWPPs are analysed, focusing on new 66 kV standard voltage and high‐voltage levels, providing technical insight on transmission cable optimal operation conditions. An economic analysis is carried out by proper estimation of construction costs and production forecast, accounting for active power losses and reliability impact. The most suitable connection solution depending on OWPP size and distance in Italian framework is evaluated by means of synthetic techno‐economic indicators, further analysing possible sensitivities of energy price and capacity factor. [ABSTRACT FROM AUTHOR]

Hydrogen production from offshore wind power is one of the ways to solve the problem of consumption. Through the comparative analysis of electrolytic, hydrogen storage and transportation technology suitable for offshore wind, taking an offshore wind farm in eastern Guangdong province of China as an example, according to four cases of high-voltage AC transmission, onshore centralized hydrogen production, offshore centralized hydrogen production + submarine hydrogen pipeline transportation, offshore centralized hydrogen production + LOHC-FPSO + ship transportation, the economy of each case is analyzed under different offshore distances and different hydrogen prices, and it is concluded that the cost of offshore wind power is very sensitive to offshore distance. In that three hydrogen production cases, under the condition that the price of hydrogen is 25 CNY/kg, when the offshore distance exceeds about 180 km, it is difficult to make profits, and it is not suitable for hydrogen production. When the offshore distance is less than 80 km, offshore wind power has more advantages in producing hydrogen on land. When the offshore distance exceeds about 80 km, case 3 is lower. When the offshore distance exceeds about 150 km, case 2 and case 3 will be superior to the case 1. When the benchmark hydrogen price is 25 CNY/kg, the economy of each case is poor. • Development status and trend of offshore wind power in China. • Selection of electrolysis ,storage and transportation technologies suitable for offshore wind power to hydrogen. • Four scenarios and their hydrogen cost and competitiveness analysis. • Suggestions on developing offshore wind power to hydrogen in Guangdong. [ABSTRACT FROM AUTHOR]

The large yellow croaker, an endemic migratory species in China's coastal waters, holds considerable economic importance. It is very necessary and significant to study the spatio-temporal evolution of large yellow croaker fishery based on environmental factors. In this paper, the HY-1C/D Coastal Zone Imager was applied to reveal distributional details of suspended sediment concentration and chlorophyll-a concentration in large yellow croaker fishery. Furthermore, combined with multi-source data, the characteristics of other environmental parameters such as temperature, salinity, current, wind and fronts were analyzed to reveal the changing of large yellow croaker fishery location. The results show that: (1) The environmental factors of large yellow croaker fishery exhibit pronounced spatial and temporal characteristics. Generally, the temperature (9-28°C), salinity (20-34 ‰) and other water environmental factors of the fishing ground are conducive to the survival and growth of large yellow croaker. The formation and change of fishery are closely related to environmental factors in the water. (2) The change of environmental factors in the water may induce the change of large yellow croaker fishery location. Winter sea surface temperature has significantly risen over the past two decades (2004-2023). With the sea surface temperature rising by approximately 2°C west of 125°E, the overwintering ground has expanded westward, increasing by nearly 29%. In addition, the scale and number of offshore windmills increased by 836% and 456% respectively over the decade (2013-2023). In 2023, offshore wind farms cover an area of 145.47 km2 and have 200 windmills. Wind turbine piles resembling artificial reefs. It can not only promote the waters vertical exchange to enhance the nutrients in upper waters but also provide a protective base for fish spawning. Thus, effectively attracting fish species. The construction of offshore wind farms has extended spawning ground north-westward to the mouth of the Yangtze River Estuary, expanding by nearly 21%. This study improved the cognition level of resources, habitat environment and change trend of large yellow croaker. It also provided technical support for the construction and protection of the large yellow croaker fishery. [ABSTRACT FROM AUTHOR]

Balancing electricity demand and sustainable energy generation like wind energy presents challenges for the smart grid. To address this problem, the optimization of a wind farm (WF) along with the battery energy storage (BES) on the supply side, along with the demand side management (DSM) on the consumer side, should be considered during its planning and operation stages. An optimization framework with two levels to simultaneously decide the layout and operation of the WF/BES is put forward in this paper. The first‐level model consists of determining the WF/BES capacities, the WF configuration, and the connection buses. It is tackled by the mixed‐discrete particle swarm optimization algorithm. The multi‐objective optimization problem (MOOP) model in the second level determines the operation schedule of the WF/BES and other generators taking the DSM into consideration. The MOOP model in the second level is transformed to a single‐objective optimization problem via the maximum fuzzy satisfaction method, and is then solved by the genetic algorithm. The proposed model and the strategy are verified by the Barrow offshore WF test case, which is integrated into the IEEE‐118 system. Simulation results indicate that the wind and load patterns, the DSM and the BES price are the three key factors influencing the WF/BES design optimization. [ABSTRACT FROM AUTHOR]

Rapid global expansion of offshore wind farms, tidal, and wave technologies signifies a new era of renewable energy development. While a promising means to combat the impacts of climate change, such developments necessitate fine-scale monitoring of biological communities to determine impacts associated with construction, operation, and eventual decommission. Here, we evaluate the performance of a gridded, Innovasea Systems, Inc. fine-scale acoustic telemetry positioning system (FSPS, n = 20 acoustic receivers) for tracking behaviors of diverse, temperate fish assemblages in relation to a subsea cable route supporting the Ørsted offshore wind development in coastal New York. We examined array performance through positioning error derived from receiver reference transmitters and tracked animals (n = 260) comprising 17 species of teleost and elasmobranch. We evaluated the effects of environmental variables (temperature, tilt, noise, and depth), transmitter power, individual movement rates, and receiver loss on horizontal positioning error (HPE) and route mean squared error (RMSE). Across a 16-month deployment period, many positions were derived for Atlantic sturgeon (n = 2,612), black sea bass (n = 9,175), clearnose skate (n = 10,306), summer flounder (n = 13,304), and little skate (n = 15,186), suggesting that these species may serve as sentinel candidates for assessing behavioral changes following construction, operation, and decommission. We found that receivers placed at the boundary of the grid exhibited higher HPE and RMSE, however these errors did not significantly change despite large receiver losses (25%). Generalized Linear Models revealed that temperature, noise, tilt, and depth were often significant predictors of HPE and RMSE, however, a substantial amount of variance was not explained by the models (~ 70%). Average movement rates ranged from 1.1 m s−1 (common thresher shark) to 0.03 m s−1 (little skate and summer flounder) but had minimal effects on positioning error. Finally, we observed that higher transmitter powers (158 dB) may lead to higher and more variable HPE values. Overall, these findings provide new insight into the drivers of FSPS array performance and illustrate their broad utility for monitoring fish behavior associated with offshore marine developments. [ABSTRACT FROM AUTHOR]

The power generation performance of wind turbines has consistently been a paramount concern for wind power operators, maintainers, and manufacturers, as it directly determines the profitability of wind farms. However, due to the combined influence of complex environmental conditions within wind farms and inherent deficiencies in wind turbine design, significant variations in power generation performance persist among turbines of the same model. This discrepancy can be attributed to two crucial factors: site conditions and operational efficiency. To achieve more precise and systematic diagnostic work on the power generation performance of wind turbines, this paper focuses on three factors: air density, turbulence intensity, and yaw adaptability. Based on this, three evaluation and diagnosis methods are proposed, including a conversion method for air density based on two‐dimensional interpolation, a turbulence correction method based on the zero‐turbulence curve, and a yaw adaptability diagnosis method based on the convergence degree. Finally, the effectiveness of these proposed methods is verified through the analysis of actual wind field data. [ABSTRACT FROM AUTHOR]

Ongoing wind energy developments play a key role in mitigating the global effects of climate change and the energy crisis; however, they have complex ecological consequences for many flying animals. The Yellow Sea coast is considered as an ecological bottleneck for migratory waterbirds along the East Asian–Australasian flyway (EAAF), and is also an important wind farm base in China. However, the effects of large-scale onshore wind farms along the EAAF on multidimensional waterbird diversity, and how to mitigate these effects, remain unclear. Here we examined how wind farms and their surrounding landscapes affected multidimensional waterbird diversity along the Yellow Sea coast. Taxonomic, functional, and phylogenetic diversity of the waterbird assemblages, and mean pairwise distances and nearest taxon distances with null models were quantified in relation to 4 different wind turbine densities. We also measured 6 landscape variables. Multi-dimensional waterbird diversity (taxonomic, functional, and phylogenetic diversity) significantly decreased with increasing wind turbine density. Functional and phylogenetic structures tended to be clustered in waterbird communities, and environmental filtering drove waterbird community assemblages. Furthermore, waterbird diversity was regulated by a combination of wind turbine density and landscape variables, with edge density of aquaculture ponds, in addition to wind turbine density, having the greatest independent contribution to waterbird diversity. These results suggest that attempts to mitigate the impact of wind farms on waterbird diversity could involve the landscape transformation of wind farm regions, for example, by including high-edge-density aquaculture ponds (i.e. industrial ponds) around wind farms, instead of traditional low-edge-density aquaculture ponds. [ABSTRACT FROM AUTHOR]

Vertical-axis wind turbines (VAWTs), particularly in offshore wind farms, are gaining attention for their capacity to potentially enhance wake recovery and increase the power density of wind farms. Previous research on VAWT wake control strategies have demonstrated that the pitch offset is favorable for VAWT wake recovery. In the present study, an investigation on the wake recovery and its mechanisms for an H-Rotor and a novel X-Rotor VAWTs with fixed blade pitch offsets is conducted through qualitative and quantitative methods. The actuator line method is utilized in this study. Results indicate that the two rotors produce distinct vortex systems that drive the wake recovery process—which is augmented with pitch offsets. Through quantitative studies, the contribution of wake recovery due to advection increases dramatically with pitch offsets in the near wake. With pitch offsets, the inline available power increases up to 2.3 times for the rotors when compared to when there is no pitch offset. The mean kinetic energy flux occurs mostly above and below the rotors as well as the windward side, suggesting the mechanism of power replenishment for these rotors with pitch offsets. These results encourage further research into the effectiveness of wake recovery in the wind-farm level with the ground and atmospheric boundary layer influences. [ABSTRACT FROM AUTHOR]

This paper investigates a critical aspect of wind energy research—the development of wind turbine wake and its significant impact on wind farm efficiency. The study focuses on the exploration and comparison of two mesh refinement strategies, h- and p-refinement, in their ability to accurately compute the development of wind turbine wake. The h-refinement method refines the mesh by reducing the size of the elements, while the p-refinement method increases the polynomial degree of the elements, potentially reducing the error exponentially for smooth flows. A comprehensive comparison of these methods is presented that evaluates their effectiveness, computational efficiency, and suitability for various scenarios in wind energy. The findings of this research could potentially guide future studies and applications in wind turbine wake modeling, thus contributing to the optimization of wind farms using high-order h/p methods. This study fills a gap in the literature by thoroughly investigating the application of these methods in the context of wind turbine wake development. [ABSTRACT FROM AUTHOR]

This article presents an innovative structural system for offshore wind turbines supported on monopiles. The novelty of this system lies in its enhancement of the lateral stiffness of the tubular tower through the incorporation of an outrigger system, widely employed in high-rise buildings. This novel system is modeled as a Timoshenko beam-column element under both static and dynamic loading scenarios, incorporating geometric nonlinearity and accounting for the effect of soil-structure interaction. A comprehensive numerical analysis was conducted to evaluate the system's behavior under various static and dynamic load cases, utilizing soil data from the Red Sea region of Egypt. A modal analysis is also incorporated to investigate the eigenmodes of the proposed system. This study offers valuable insights for designers and researchers, providing guidelines and diagrams for the implementation of the proposed system. [ABSTRACT FROM AUTHOR]

We consider the estimation of the conditional tail moment at extreme levels for the class of Weibull‐type distributions. A two‐step procedure is introduced where in the first stage one estimates the conditional tail moment at an intermediate level, followed by an extrapolation in the second stage. The asymptotic properties of the estimators introduced in the two stages are derived under suitable assumptions. The finite sample properties of the proposed estimator are examined with a simulation experiment. We conclude with two applications on real life data: wind speed measurements collected at an offshore wind farm and PM2.5 air pollution data. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Shanghai Jiao Tong University (1006-2467) is the property of Journal of Shanghai Jiao Tong University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Early fault detection plays a crucial role in the field of predictive maintenance for wind turbines, yet the comparison of different algorithms poses a difficult task because domain-specific public datasets are scarce. Many comparisons of different approaches either use benchmarks composed of data from many different domains, inaccessible data, or one of the few publicly available datasets that lack detailed information about the faults. Moreover, many publications highlight a couple of case studies where fault detection was successful. With this paper, we publish a high quality dataset that contains data from 36 wind turbines across 3 different wind farms as well as the most detailed fault information of any public wind turbine dataset as far as we know. The new dataset contains 89 years worth of real-world operating data of wind turbines, distributed across 44 labeled time frames for anomalies that led up to faults, as well as 51 time series representing normal behavior. Additionally, the quality of training data is ensured by turbine-status-based labels for each data point. Furthermore, we propose a new scoring method, called CARE (Coverage, Accuracy, Reliability and Earliness), which takes advantage of the information depth that is present in the dataset to identify good early fault detection models for wind turbines. This score considers the anomaly detection performance, the ability to recognize normal behavior properly, and the capability to raise as few false alarms as possible while simultaneously detecting anomalies early. Dataset: https://doi.org/10.5281/zenodo.14006163 (accessed on 29 October 2024). Dataset License: CC BY-SA 4.0 International. [ABSTRACT FROM AUTHOR]

Offshore wind energy plays a crucial role in achieving renewable energy targets, with OWFs facing unique environmental challenges that impact turbine performance and structural demands. This study develops an advanced optimization methodology to identify the most effective layout configurations for offshore wind farms (OWFs) with monopile foundations, focusing on enhancing structural integrity and energy generation efficiency. Using a multi-criteria optimization approach, the effects of wind turbine spacing, angular orientation, and height on energy yield and monopile loading were evaluated. Based on a seven-year dataset from the Ouido site in South Korea, where the mean wind speed is 6.95 m/s at a 150 m hub height, optimized configurations were determined. For average wind conditions, a turbine spacing of 250 m, a hub height of 148 m, and an orientation angle of 36.87° minimized wake losses and distributed structural loads effectively. Under rated wind speeds of 10.59 m/s, a spacing of 282 m, a hub height of 155 m, and an orientation angle of 45° further enhanced performance. These designs reduced wake interference by 25%, decreased monopile fatigue loads by 18%, and lowered the levelized cost of electricity (LCOE) by up to 15%. This study's findings provide a robust framework for optimizing OWFs to increase energy yield, improve operational efficiency, and ensure economic viability. [ABSTRACT FROM AUTHOR]

The rapid growth of offshore wind farms (OWFs) as renewable energy sources has heightened concerns about maritime traffic safety and management in high-density traffic zones. These areas, characterized by complex interactions among diverse ship types and spatial constraints, require advanced situational awareness to prevent collisions and ensure efficient operations. Traditional maritime traffic systems often lack the granularity to assess the multifaceted risks around OWFs. Existing research has explored local traffic patterns and collision risks but lacks comprehensive frameworks for evaluating traffic complexity at both micro and macro levels. This study proposes a new complexity assessment model tailored to OWF areas, integrating micro-level ship interactions and macro-level traffic flow conditions to capture a holistic view of traffic dynamics. Using extensive historical AIS data from the Yangtze River Estuary, the model evaluates the impact of the proposed OWF on existing traffic complexity. The results demonstrate that OWFs increase navigational complexity, particularly in route congestion, course adjustments, and encounter rates between ships. Different ship types and sizes were also found to experience varying levels of impact, with larger ships and tankers facing greater challenges. By providing a quantitative framework for assessing traffic complexity, this research advances the field's ability to understand and manage the risks associated with OWFs. The findings offer actionable insights for maritime authorities and OWF operators, supporting more effective traffic management strategies that prioritize safety and operational efficiency in high-density maritime areas. [ABSTRACT FROM AUTHOR]

Offshore wind is a promising renewable energy generation technology and is arousing great attention in regards to pursuing carbon neutrality targets. Accurately simulating offshore wind generation can help to better optimize its operation and planning. It is also a concern that mechanical resonance is a threat to the wind turbines' lifespan. In this paper, the time-series simulation of offshore wind generation with consideration of resonance zone (RZ) control is investigated. The output model for multiple wind farms with different spatial correlations is proposed. Additionally, the capacity value (CV) of the joint wind farms is also evaluated through a reliability-based model. The case study illustrates the offshore wind power output simulation and CV results under different farm correlation scenarios and RZ control strategies. It is shown that strong spatial correlation brings great synchronicity in wind farms' output and results in a lower CV. The RZ control in wind simulation is validated and proven to have a marginal impact on the total output when multiple wind farms are evaluated together. [ABSTRACT FROM AUTHOR]

Wind farm layout optimization is essential to maximize the energy production of renewable energy systems. A new layout optimization method for offshore wind farms is proposed to minimize power deficits due to the wake effect without limitation on the number of turbines, the shape, or the extension of the concession area. The main engine of the algorithm is a gradient-descent method in which throughout the optimization process, new turbines are progressively and randomly included within the concession area and quickly expand outward, looking for areas with less perturbation, in turn, pushing the previous ones. When the optimization process ends, to avoid local maxima, it enters into a process of suppression of the set of locations that cause the greatest potential (power deficit). Then, a map of potential for the entire area is created, and a greedy algorithm places new turbines to complete the layout with the final number of turbines. The overall process is completed in 25 s. To drastically speed up the search process and the creation of the potential map, a simplification has been validated and added: for turbines affected by multiple wakes, the resulting power has been calculated by using a linear aggregation of power deficits, instead of the usual linear (or quadratic) aggregation of speed deficits. Owing to this type of aggregation, an analogy is established between power deficit and repulsive non-isotropic electrostatic potential energy, which allows using the properties of conservative fields. Thanks to this, the process is 20 times faster than any other layout optimization algorithm found in the revised literature. Irregular concession areas are easily treated using Stokes' theorem to detect outer points. [ABSTRACT FROM AUTHOR]

The article discusses the utilization of hydrogen using hydrogen storage alloys in the context of renewable energy and global warming prevention. It highlights the development of applications such as a chemical compressor and a hydrogen transportation system using hydrogen storage alloys. The text emphasizes the importance of developing technologies for producing, storing, transporting, and using hydrogen to realize a hydrogen society, with a focus on applications in Japan and potential future commercialization of hydrogen storage alloy technology. [Extracted from the article]

The article discusses how endangered Black-faced Spoonbills alter their migration patterns across the Yellow Sea due to the presence of offshore wind farms. The study highlights two cases of GPS-tracked Spoonbills changing their routes after encountering wind farms, leading to delays and altered behaviors. The research emphasizes the potential impact of offshore wind farms on migratory bird species and calls for further studies to assess cumulative effects and behavioral responses. [Extracted from the article]

Offshore wind energy is characterized by its clean and renewable nature, leading to rapid growth in the industry. However, the incidence of ship collisions with offshore wind turbines has also risen with the increasing number of offshore wind farms, particularly in commercial shipping lanes. To gain a thorough understanding of dynamic response between wind turbines and vessels, this paper extensively reviews studies related to wind turbine–ship collisions over the past 2 decades to cover four key aspects: (i) the fundamental requirements and background of collision analysis study, (ii) the analysis of dynamic response and collision characteristics of the fixed-bottom and floating offshore wind turbines (OWTs) subjected to ship collision forces, (iii) the influence of key collision factors that include impact positions, initial ship kinetic energy, and soil–structure interaction on the structural response for the wind turbines, and (iv) a discussion of protection measures to mitigate the collision damage to the substructure. The limitations in the existing studies are discussed, and future research directions are suggested. [ABSTRACT FROM AUTHOR]

Accurate prediction of wind speed is a prerequisite for the safe and accurate operation of wind power generation, however, WRF models typically do not produce sufficiently accurate wind speed predictions. This study proposed a Seasonal and Temporal Correlation - Deep Forest (STC-DF) model for offshore wind speed prediction. Different from traditional methods, the STC-DF model takes the advantages of the deep forest algorithm to automatically learn complex feature interactions without manual feature engineering. The model is designed to capture the seasonal and temporal characteristics of wind speed variations. To test the effectiveness of the proposed method, we applied the trained STC-DF model to an offshore wind farm in Hainan Province, China. Seven days of data from each season were selected for testing. The results show that the STC-DF model can effectively reduce the error caused by WRF forecast. The error index of the corrected wind speed reduced more than 40%, the accuracy of wind speed forecast increased 15%. And the method passed the multi-model comparison test and robustness experiment. These research results show that the STC-DF model has strong versatility and good correction ability, and is suitable for wind speed forecasting in different regions, which is a feasible method to improve the reliability of offshore wind power generation. [ABSTRACT FROM AUTHOR]

The rapid development of the wind industry is accompanied by increasing environmental impacts. Currently, there is a lack of research on the impacts of offshore wind farm (OWF) on tropical cyclone (TC) intensity, including the mechanisms involved. This research is carried out by using a coupled and an uncoupled numerical model to investigate the impact of OWF on an autumn TC in the northeastern South China Sea. The results show that the wind speed deficit caused by OWF leads to an increase in surface pressure on the inflow side. This causes the surface pressure in the TC periphery to increase by advection, even if the TC is some distance away from the OWF. The increase in pressure gradient from the periphery to the TC center enhances the TC secondary circulation, thereby intensifying the TC. When the TC enters the OWF, the above mechanisms weaken and the ocean dominates the TC intensification. This is because the reduction in wind speed caused by the OWF results in a weaker sea surface current velocity, which weakens the flow of upstream cold water into the OWF, warming the sea surface temperature (SST) within the OWF. This implies that the horizontal gradient of the local SST is an important factor to be considered in the development of OWF. Sensitivity experiments indicate that OWF can also intensify other types of TC, and that higher cut‐out wind speeds lead to stronger intensification effects. These results also provide a new perspective on TC intensity forecasts. Plain Language Summary: The rapid development of the offshore wind industry has significant impacts on the surrounding environment. There is limited research on the impact of offshore wind farms on the tropical cyclone (TC) intensity. This study indicates that wind farms in the northern South China Sea intensify an autumn TC that makes landfall in China. Wind farms intensify the TC by increasing the surface pressure in the TC periphery when the TC is far from the wind farm. When the TC is close to the wind farm, it is the warming sea water within the wind farm that intensifies the TC. Tropical cyclone can also be slightly intensified when the number of turbines is small. These results help to understand the impact of offshore wind farms on TCs and to improve TC intensity forecasts. Key Points: The tropical cyclone (TC) intensifies before approaching offshore wind farms due to increased peripheral pressure caused by wind farmsThis intensification within wind farms is mainly due to the reduced transport of cold water upstream associated with the wind speed deficitHigher cut‐out wind speeds can lead to a more pronounced intensification of the TC before it enters wind farms [ABSTRACT FROM AUTHOR]

The cascaded H‐bridge multi‐level converter is the main topology of high‐voltage and high‐capacity converter in renewable energy power generation, energy storage system and reactive power compensation equipment. The parameter design of cascaded H‐bridge multi‐level converters directly determines the material cost and performance. Taking the output current distortion rate and the ripple voltage of the capacitor of the cascaded H‐bridge multi‐level converter as the constraint conditions, this paper proposes an overall parameter optimal design method of the cascaded H‐bridge multi‐level converter based on the optimization of the total material cost. Finally, the effectiveness of the proposed parameter design method is verified by the simulation result. [ABSTRACT FROM AUTHOR]

Bringing floating offshore wind turbines (FOWTs) to a real industrial maturity and reducing the levelized cost of floating wind energy are key to significantly increasing the penetration of renewables in the energy mix of Mediterranean countries, especially if in combination with suitable energy storage systems, such as those involving green hydrogen production. The present study analyses techno‐economic aspects of some of the technologies related to FOWTs and hydrogen production by means of offshore‐generated energy, aiming to evaluate the potential of a floating wind farm integrated with a power‐to‐gas energy storage system in a specific installation site near the Sardinian shores. In comparison to the pioneering studies to date, a more detailed computational model is used, able to account for several critical factors like a better description of metocean conditions, constraints on grid capacity, and a state‐of‐the‐art model to define the farm layout. Concerning hydrogen production, a comparison between the statistical approach, which is commonly used in the field, and a fully time‐dependent method is performed. Proposed results obtained with the statistic and the time‐dependent approach show values ranging between 3.79 and 5.47€/kg, respectively. These outcomes are thought to provide an interesting comparison between different fidelity approaches and realistic reference values for the levelized cost of hydrogen by floating wind in the Mediterranean Sea. [ABSTRACT FROM AUTHOR]

Eight scenarios where high efficiency reversible solid oxide cells (rSOC) are combined with an offshore wind farm are identified. Thanks to the PyPSA power system modelling tool combined with a sensitivity study, optimized rSOC system capacities, hydrogen storage capacities, and subsea cable connection capacities are investigated under various combinations of rSOC system capital cost, prices paid for hydrogen, and electricity prices, which give indications on the most profitable scenario for offshore hydrogen production from a 600 MW wind farm situated 60 km from shore. Low electricity prices (yearly average 45 £/MWh) combined with mild fluctuations (standard deviation 6 or 13 £/MWh) call for dedicated hydrogen production when the hydrogen price exceeds 4 £/kg. High electricity prices (yearly average 118 or 204 £/MWh), combined with extreme fluctuations (standard deviation between 73 and 110 £/MWh), make a reversible system economically profitable. The amount of hydrogen which is recommended to be reconverted into electricity depends on the price paid for hydrogen. Comparison of the optimized cases to the default case of a wind farm without hydrogen production improved profit by at least 3% and up to 908%. Comparison to the default case of dedicated hydrogen production, showed that in the case of low hydrogen prices, an unprofitable scenario can be made profitable, and improvement of profit in the case of a profitable default case starts at 4% and reaches numbers as high as 324%. [ABSTRACT FROM AUTHOR]

Wind energy being a free source of energy is becoming popular over the past decades and is being studied extensively. Integration of wind turbines is now being expanded to urban and offshore settings in contrast to the conventional wind farms in relatively open areas. The direct installation of wind turbines poses a potential risk, as it may result in financial losses in scenarios characterized by inadequate wind resource availability. Therefore, wind energy availability analysis in such urban environments is a necessity. This research paper presents an in‐depth investigation conducted to predict the exploitable wind energy at four distinct locations within Nottingham, United Kingdom. Subsequently, the most suitable location, Clifton Campus at Nottingham Trent University, is identified where a comprehensive comparative analysis of power generation from eleven different wind turbine models is performed. The findings derived from this analysis suggest that the QR6 wind turbine emerges as the optimal choice for subsequent experimental investigations to be conducted in partnership with Nottingham Trent University. Furthermore, this study explores the selection of an appropriate probability density function for assessing wind potential considering seven different distributions namely, Gamma, Weibull, Rayleigh, Log‐normal, Genextreme, Gumbel, and Normal. Ultimately, the Weibull probability distribution is selected, and various methodologies are employed to estimate its parameters, which are then ranked using statistical assessments. [ABSTRACT FROM AUTHOR]

Integration of large‐scale wind farms (WFs) into the grid has to meet the critical constraints set in the national grid code. Wind farm operators (WFOs) are inclined to comply with these constraints and avoid heavy penalty costs for violating such regulations. However, this may result in reduced power sent to the grid. Moreover, the addition of new rules to account for the increased penetration of WFs brings challenges to the profitability of the WFs. A battery energy storage system (BESS), if sized optimally, can be a reliable method to fulfill the grid code requirements without sacrificing profit. This paper provides a techno‐economic model to find the optimal rated capacity and power for a BESS in WFs. This optimization model takes the absolute production and delta production constraints into account. Two approaches are studied for integrating these constraints into the grid code. It is shown that the flexible strategy financially outperforms the strict addition of the new rules. This will be useful, especially to attract investments in wind energy projects despite the abovementioned limitations in the grid code. All the modeling and analysis are done for a potential offshore wind power plant (OWPP) in Turkey. Simulation results show the effectiveness of the optimal BESS in increasing the amount of energy delivered to the grid and improving the profitability of the OWPP. [ABSTRACT FROM AUTHOR]

Simulating wind turbine blade icing quickly is important for wind farms to issue early warnings and effectively deal with the adverse effects of cold weather. However, current numerical simulation methods suffer from high computational costs and lack straightforward acceleration techniques for practical ice prediction. Here, we developed a fast and simple blade icing simulation method via an integrated physical simulation and statistical analysis method. This method consists of two steps: firstly, numerical simulation with CFD, and secondly, table look-up calculations. Over 10,000 sets of wind turbine blade icing simulations based on FENSAP-ICE and an NACA64-A17 wing were conducted to develop this method and analyze the influences of environmental factors on blade icing. The results show that ice thickness generally increases with an increase in wind speed, a decrease in temperature, and an increase in liquid water content (LWC), but there is a nonlinear relationship between them. For example, ice thickness has a linear relationship with the LWC within a certain range but hardly changes with a LWC beyond that range. The validation results show that the fast simulation method established in this paper has good consistency with the original numerical simulation method. It can greatly improve the computational efficiency of icing simulations while retaining the accuracy of numerical simulations. It takes less than 1 s to complete over 1000 sets of icing simulations, which offers potential for the fast prediction of wind turbine blade icing in the future. [ABSTRACT FROM AUTHOR]

In this study, we investigate a yaw control strategy in a two-turbine wind farm with 3.5 MW turbines, aiming to optimize power management. The wind farm is equipped with a nacelle-mounted multi-plane LiDAR system for wind speed measurements. Using an analytical model and integrating LiDAR and SCADA data, we estimate wake effects and power output. Our results show a 2% power gain achieved through optimal yaw control over a year-long assessment. The wind predominantly blows from the southwest, perpendicular to the turbine alignment. The optimal yaw and power gain depend on wind conditions, with higher turbulence intensity and wind speed leading to reduced gains. The power gain follows a bell curve across the range of wind inflow angles, peaking at 1.7% with a corresponding optimal yaw of 17 degrees at an inflow angle of 12 degrees. Further experiments are recommended to refine the estimates and enhance the performance of wind farms through optimized yaw control strategies, ultimately contributing to the advancement of sustainable energy generation. [ABSTRACT FROM AUTHOR]

The objective of this paper is to study the Wind Farm Layout Optimization/expansion problem. This problem is formulated here as a Multi-Objective Optimization Problem considering the total power output and net efficiency of Wind Farms as objectives along with specific constraints. Once formulated, this problem needs to be solved efficiently. For that, a new approach based on a combination of five Multi-Objective Optimization algorithms, which is named the Parallel Collaborative Multi-Objective Optimization Algorithm, is developed and implemented. This technique is checked on seven test cases; for each case, the goal is to find a set of optimal solutions called the Pareto Front, which can be exploited later. The acquired solutions were compared with other approaches and the proposed approach was found to be the better one. Finally, this work concludes that the proposed approach gives, in a single run, a set of optimal solutions from which a designer/planner can select the best layout of a designed Wind Farm using expertise and applying technical and economic constraints. [ABSTRACT FROM AUTHOR]

Given the unpredictable and intermittent nature of wind energy, precise forecasting of wind power is crucial for ensuring the safe and stable operation of power systems. To reduce the influence of noise data on the robustness of wind power prediction, a wind power prediction method is proposed that leverages an enhanced multi‐objective sand cat swarm algorithm (MO‐SCSO) and a self‐paced long short‐term memory network (spLSTM). First, the actual wind power data is processed into time series as input and output. Then, the progressive advantage of self‐paced learning is used to effectively solve the instability caused by noisy data during long short‐term memory network (LSTM) training. Following this, the improved MO‐SCSO is employed to iteratively optimize the hyperparameters of spLSTM. Ultimately, a combined MO‐SCSO‐spLSTM model is constructed for wind power prediction. This model is validated with the data of onshore wind farms in Austria and offshore wind farms in Denmark. The experimental results show that compared with the traditional LSTM prediction method, the proposed method has better prediction accuracy and robustness. Specifically, in the onshore and offshore wind power prediction experiments, the proposed method reduces the minimum MAE by 5.44% and 4.96%, respectively, and reduces the MAE range by 4.45% and 17.21%, respectively, which could be conducive to the safe and stable operation of power system. [ABSTRACT FROM AUTHOR]

The penetration of offshore wind farms (OWFs) in city‐close power systems is rapidly increasing. System inertia will be further reduced. Active frequency support of wind power is essential to solve the load frequency control (LFC) problem. Here, the dynamic virtual inertia control (VIC) method is employed to enhance frequency stability within the permitted operating states of OWFs. An adaptive distributed model predictive control (DMPC) method is proposed and applied to an interconnected power system. The dynamic VIC‐based LFC model is derived and used to construct the predictive model of DMPC. To expand the adaptation of the analytical linearized model of OWFs in different operating points, the adaptive law is further designed to dynamically adjust the parameters of DMPC. The simulation results demonstrate the effectiveness of the proposed control method. The frequency fluctuations can be well‐restrained under different disturbances. [ABSTRACT FROM AUTHOR]

The article "A meso–micro atmospheric perturbation model for wind farm blockage" in the Journal of Fluid Mechanics introduces an improved Atmospheric Perturbation Model (APM) for large wind farms, focusing on mesoscale parametrization. The model incorporates dispersive stresses, turbulent momentum fluxes, and wake-model coupling to better predict turbine inflow velocities and power output. Validation studies demonstrate the model's accuracy in capturing atmospheric flow and its superiority over standard wake models, making it a valuable tool for studying wind farm performance and efficiency. The research provides insights into the complex interactions between atmospheric conditions and wind energy generation, offering significant contributions to the renewable energy sector. [Extracted from the article]