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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]

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]

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]

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]

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]

Since the first plans to develop offshore wind farms (OWFs), concerns have been raised about the impacts on marine megafauna. Today, it is required to assess these impacts over the whole lifecycle of the OWF. Before construction, initial assessments are often conducted by visual surveys, but subsequent monitoring over the lifecycle of the OWF has to be digital due to safety requirements, leading to challenges in data comparability. The aim of this study was to attempt to establish generalizable intercalibration factors for this transition between visual and digital monitoring methods. To this end, intercalibration surveys were conducted at five different sites and at different times of the year within a site, using both visual monitoring at low-altitude and digital monitoring at both low and high altitudes. We tested the potential for intercalibration of the results based on the ratio of abundance estimated from data collected by the different methods. We explored factors such as the species under study and site-specific conditions that may influence intercalibration. We computed more than 100 intercalibration factors and found that, on average, abundance estimates from digital methods were higher than those from visual methods and that flight altitude for digital monitoring did not significantly influence abundance estimates. Aside from divergent abundance estimates depending on monitoring method, the findings also revealed significant heterogeneity, only one-third of which was explained by contextual factors such as taxonomy or the sea conditions. This outcome presents a pessimistic outlook on the prospect for the intercalibration of results between an initial assessment carried out with visual observations and subsequent monitoring with digital methods after OWF construction and until decommissioning. The high heterogeneity prevents seamless transferability of intercalibration factors and highlights the importance of local context. [ABSTRACT FROM AUTHOR]

Siting an offshore wind project is considered a complex planning problem with multiple interrelated objectives and constraints. Hence, compactness and contiguity are indispensable properties in spatial modeling for Renewable Energy Sources (RES) planning processes. The proposed methodology demonstrates the development of a raster-based spatial optimization model for future Offshore Wind Farm (OWF) multi-objective site-prospecting in terms of the simulated Annual Energy Production (AEP), Wind Power Variability (WPV) and the Depth Profile (DP) towards an integer mathematical programming approach. Geographic Information Systems (GIS), statistical modeling, and spatial optimization techniques are fused as a unified framework that allows exploring rigorously and systematically multiple alternatives for OWF planning. The stochastic generation scheme uses a Generalized Hurst-Kolmogorov (GHK) process embedded in a Symmetric-Moving-Average (SMA) model, which is used for the simulation of a wind process, as extracted from the UERRA (MESCAN-SURFEX) reanalysis data. The generated AEP and WPV, along with the bathymetry raster surfaces, are then transferred into the multi-objective spatial optimization algorithm via the Gurobi optimizer. Using a weighted spatial optimization approach, considering and guaranteeing compactness and continuity of the optimal solutions, the final optimal areas (clusters) are extracted for the North and Central Aegean Sea. The optimal OWF clusters, show increased AEP and minimum WPV, particularly across offshore areas from the North-East Aegean (around Lemnos Island) to the Central Aegean Sea (Cyclades Islands). All areas have a Hurst parameter in the range of 0.55–0.63, indicating greater long-term positive autocorrelation in specific areas of the North Aegean Sea. [ABSTRACT FROM AUTHOR]

Seabird collision risk is a key concern in relation to the environmental impacts associated with offshore wind farms (OWFs). Understanding how species respond both to the wind farm itself, and individual turbines within the wind farm, is key to enabling better quantification and management of collision risk. Collision risk is of particular concern for the black-legged kittiwake, Rissa tridactyla, where modelling predicts unsustainable population level impacts. In this study 20 adult breeding kittiwakes, were tracked with GPS from Whinnyfold, Scotland (57°23′07″N, 001°52′11″W) during the breeding season in 2021. An Avoidance-Attraction Index (AAI) was estimated at several bands within macro- and meso-scales (0–4 km from outer boundary and 0–400 m from turbines, respectively), and the Avoidance Rate (AR; used in environmental impact assessments) at macro-scale to estimate avoidance behaviour to three operational OWFs within their foraging range. One offshore wind farm and its buffer zone (0–4 km from outer boundary) was visited more frequently by the majority of tracked individuals (19/20 birds), despite being twice as far as the closest OWF (17.3 and 31.9 km respectively), whilst 10 or less individuals used the remaining two OWFs. At the most frequented OWF we found macro-scale attraction to the closest band (0–1 km) trending towards avoidance in the furthest band (3–4 km). At the meso-scale we found avoidance of areas below the rotor height range (RHR, a.k.a. rotor swept area/zone) up to 120 m from individual turbines, which decreased to 60 m when within the RHR. Our results indicate that kittiwakes may be slightly attracted to the area around OWFs or aggregate here due to displacement but avoid individual turbines. Increased productivity in the OWF area may potentially be drawing birds into the general area, with aversion to individual turbines being responsible for meso-scale observations. [ABSTRACT FROM AUTHOR]

A comparison and evaluation of the AEP(Annual Energy Production) of a wind farm were conducted in this study with a feasibility study and using the actual operation data from the S wind farm on Jeju Island from January 2020 to December 2022. The free wind speed data were selected from the data measured from a nacelle anemometer, the correlation equation between wind speed and AEP was obtained, and the annual average wind speed for the past 20 years was predicted using the MCP method. As a result, comparing the AEP from the operation data with that estimated in the feasibility study, we found that the AEP was reduced by approximately 2.40% in 2020 and 12.14% in 2021, and increased by 6.76% in 2022. The wind speeds over the 20-year lifetimes of the wind turbines were obtained, and the AEP that could be generated at the S wind farm indicated that it could be used for operation. In the future, the S wind farm will operate at between 25% and 30% availability for the remaining 17 years of operation. If the availability falls below 25%, there will be a need to check the reasons for the deterioration of wind turbine performance and the frequency of failures. [ABSTRACT FROM AUTHOR]

Over the past few decades, wind energy has expanded to become a widespread, clean, and sustainable energy source. However, integrating offshore wind energy with the onshore AC grids presents many stability and control challenges that hinder the reliability and resilience of AC grids, particularly during faults. To address this issue, current grid codes require offshore wind farms (OWFs) to remain connected during and after faults. This requirement is challenging because, depending on the fault location and power flow direction, DC link over- or under-voltage can occur, potentially leading to the shutdown of converter stations. Therefore, this necessitates the proper understanding of key technical concepts associated with the integration of OWFs. To help fill the gap, this article performs an in-depth investigation of existing alternating current fault ride through (ACFRT) techniques of modular multilevel converter-based high-voltage direct current (MMC-HVDC) for OWFs. These techniques include the use of AC/DC choppers, flywheel energy storage devices (FESDs), power reduction strategies for OWFs, and energy optimization of the MMC. This article covers both scenarios of onshore and offshore AC faults. Given the importance of wind turbines (WTs) in transforming wind energy into mechanical energy, this article also presents an overview of four WT topologies. In addition, this article explores the advanced converter topologies employed in HVDC systems to transform three-phase AC voltages to DC voltages and vice versa at each terminal of the DC link. Finally, this article explores the key stability and control concepts, such as small signal stability and large disturbance stability, followed by future research trends in the development of converter topologies for HVDC transmission such as hybrid HVDC systems, which combine current source converters (CSCs) and voltage source converters (VSCs) and diode rectifier-based HVDC (DR-HVDC) systems. [ABSTRACT FROM AUTHOR]

Circular supplier evaluation aims at selecting the most suitable suppliers with zero waste. Sustainable circular supplier selection also considers socio-economic and environmental factors in the decision process. This study proposes an integrated method for evaluating sustainable suppliers in intelligent circular supply chains using fuzzy inference and multi-criteria decision-making. In the first stage of the proposed method, supplier evaluation sub-criteria are identified and weighted from economic, social, circular, and Industry 4.0 perspectives using a fuzzy group best–worst method followed by scoring the suppliers on each criterion. In the second stage, the suppliers are ranked and selected according to an overall score determined by a fuzzy inference system. Finally, the applicability of the proposed method is demonstrated using data from a public–private partnership project at an offshore wind farm in Southeast Asia. [ABSTRACT FROM AUTHOR]

Wind farms with type IV wind turbines from various manufacturers are being massively put into operation. These wind turbines comply with the requirements of the grid codes of the countries where they are designed and/or manufactured, but do not factor in the specific features of the distribution networks of other countries to which they are connected. The study at issue involves a comparative analysis of the requirements of grid codes of different countries for the stable operation of wind turbines under standard disturbances. The low voltage ride through (LVRT) characteristic makes it possible to prevent wind turbine shutdowns in case of short-term voltage dips of a given depth and duration. The calculations of transient processes indicate that wind turbines may not meet the requirements of the grid code of a particular country for their stable operation. As a result, standard disturbances will block the reactive current injection and the wind turbine will be switched off. This is often caused by the relay protection devices with a time delay of 1–2 s, which are used in distribution networks and implement the functions of long-range redundancy. Excessive shutdowns of wind turbines lead to emergency rises in the loads for the generating units of conventional power plants, aggravating the post-accident conditions and disconnecting consumers of electricity. This article presents a method for checking the LVRT characteristic settings for compliance with the technical requirements for wind turbines. To prevent wind turbine outages, one should either change the configuration of the LVRT characteristic, upgrade the relay protection devices in the distribution network adjacent to the wind farm, or implement group or individual technical solutions at the wind farm. The performance of the proposed technical solutions is confirmed by the calculations of transient processes. [ABSTRACT FROM AUTHOR]

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Offshore wind turbine (WT) wake interference will reduce power generation and increase the fatigue loads of downstream WTs. Wake interference detection based on aeroacoustic noise is believed to solve these challenges in offshore wind farms. However, aeroacoustic noise is closely related to the aerodynamics around WT blades, and the acoustic detection method requires the mastery of noise emission characteristics. In this paper, FAST.Farm, combined with the acoustic model in OpenFAST, is utilized to investigate the acoustic noise emission characteristics from two 3.4 MW-130 WTs with wake interference. Multi-microphone positions were investigated for the optimal reception selection under 8 m/s and 12 m/s wind speeds with a typical offshore atmospheric turbulence intensity of 6%. The numerical simulation results indicate that wake deficit reduces the total noise emission by about 6 dBA in the overall sound pressure level (OASPL) at 8 m/s, while wake turbulence marginally increases it and its fluctuation. There is a mutual influence between these effects, and the wake deficit effect can be 100% compensated for in the OASPL at 12 m/s. Additionally, downstream observer locations are suggested based on comparisons. These investigations provide new insights into wake interference in offshore wind farms. [ABSTRACT FROM AUTHOR]

Offshore wind turbines (OWTs) have complex operating conditions and are prone to accidents. Timely and accurate vibration prediction can reduce the probability of failure of OWTs. To address this issue, this study proposed a vibration prediction model based on long short-term memory (LSTM) network. The proposed prediction model was applied to an OWT in an offshore wind farm in China, and three evaluation indicators, i.e. mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE), were used to verify the vibration prediction performance of LSTM. The results demonstrated that the prediction accuracy of LSTM was better than that of back propagation (BP) neural network, and LSTM had optimum engineering practicability. The prediction accuracy of LSTM and BP neural networks decreased with an increase in prediction durations. However, the prediction accuracy of LSTM was higher. This study improves the online safety monitoring of OWTs. [ABSTRACT FROM AUTHOR]

Operations and Maintenance (O&M) constitute the longest phase in the lifetime of an offshore windfarm. Because of this, the expenditures during this period are a large part of the overall cost of an offshore windfarm and thereby the Levelised Cost of Energy (LCOE). Innovations are changing the operation and maintenance strategies used during this phase. The offshore wind O&M industry is shifting towards digital solutions and several practical innovations. Both are improving safety standards and the performance of a windfarm. A thorough analysis of the current strategies and approaches in the fixed bottom offshore wind sector is made in this work and describes (i) how under the condition-based (preventive) maintenance strategy the downtime can be reduced and therefore energy output can be increased and (ii) how gradually fewer on-site workers will be needed as a service crew in the near future. [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]

Satellite synthetic aperture radar (SAR) provides ocean surface wind fields at 10 m above sea level. The objective is to investigate the capability of SAR satellite StriX observations for mapping offshore wind farm wakes. The focus is on the conditions under which an apparent wind speed‐up is generated, measured in 48% of the 67 images available. The results compare well to Sentinel‐1 observations, showing a 34% wind speed‐up rate during several years based on 1171 images. Three wind speed‐up cases have been studied in detail using the mesoscale Weather, Research, and Forecasting (WRF) model with two wind farm parameterizations. At 10 m above sea level, the SAR‐based observations and WRF model compare for most cases, though only when turbulent kinetic energy (TKE) is included in the wind farm parameterization. The TKE mixes higher momentum downward in a stable atmosphere, causing surface wind speed‐up near the surface. [ABSTRACT FROM AUTHOR]

As the large-scale development of wind farms (WFs) progresses, the connection of WFs to the regional power grid is evolving from the conventional receiving power grid to the sending power grid with a high proportion of wind power (WP). Due to the randomness of WP output, higher requirements are put forward for the voltage stability of each node of the regional power grid, and various reactive power compensation devices (RPCDs) need to be rationally configured to meet the stable operation requirements of the system. This paper proposes an optimal configuration method for multi-type RPCDs in regional power grids with a high proportion of WP. The RPCDs are located according to the proposed static voltage stability index (VSI) and dynamic VSI based on dynamic voltage drop area, and the optimal configuration model of RPCDs is constructed with the lowest construction cost as the objective function to determine the installed capacity of various RPCDs. Finally, the corresponding regional power grid model for intensive access to WFs is constructed on the simulation platform to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

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This study offers a modular isolated grid-connected DC/DC medium-voltage DC aggregation converter to support offshore full DC wind farms' need for lightweight and highly efficient power aggregation and transmission. The converter can simultaneously have a smaller transformer size and lower switching frequency during operation through the dual-voltage stabilization three-loop control strategy and phase-shift modulation strategy, which greatly reduces the space occupied by the converter and lowers the switching loss, Additionally, the use of a two-level structure at a lower switching frequency has lower loss, which effectively reduces the cost of the power device compared with the commonly used three-level converter. The input series output series connection between the converter sub-modules effectively lowers the voltage stress on each power switching device and facilitates expansion into a multi-module structure, expanding its application in high-voltage and large-capacity environments. This study analyzes the two working modes of the DC/DC converter and its control approach, in addition to providing a detailed introduction to the application scenarios of this converter. Ultimately, the efficacy and practicability of the suggested topology and control scheme are confirmed by simulations and experiments. [ABSTRACT FROM AUTHOR]

Accurate ultra-short-term power prediction for wind farms is challenging under rapid wind speed fluctuations, complicating production planning and power balancing. This paper proposes a new method considering spatial and temporal correlations of wind fluctuations among adjacent wind farms. The method first calculates the time difference between power fluctuations based on wind speed, direction, and relative positions, determining the prior information period. The variational Bayesian model is then used to extract implicit relationships between power fluctuations of adjacent wind farms, enabling power prediction during the prior information period. Finally, the non-prior information period is predicted to complete the ultra-short-term power prediction. Using measured data from three wind farms in Fujian Province, compared to other models, the method demonstrates improved accuracy by effectively leveraging the power fluctuation characteristics of adjacent wind farms, and it has a certain amount of generalizability. [ABSTRACT FROM AUTHOR]

Assessing market power in the presence of different production technologies such as renewable energies, including wind and solar power, is crucial for electric market analysis and operation. This paper investigates structural market power by incorporating wind farms and solar generation over a short-term period. The study examines the issue of market concentration boundaries to assess structural market power by calculating the minimum and maximum market concentration index values in the day-ahead market. It models the technical specifications of power plants, such as the maximum and minimum production limits, ramp-up and ramp-down rates, and minimum required up and down times. By extracting the spatiotemporal correlation of wind power generation from real data, the uncertainty of renewable power generation is represented through a set of scenarios. The analysis explores the correlation effects of wind farms, solar generation, and wind penetration levels under different ownership structures. Simulation results using a modified PJM five-bus system illustrate the effectiveness of the developed method. Our results indicate that integrating renewable energy can reduce the Herfindahl–Hirschman Index (HHI) by up to 30% as wind penetration levels rise from 0% to 40%, fostering a more competitive market structure. However, the correlation between wind farms also increases market volatility, with the standard deviation of the HHI rising by about 25% during peak load periods. These findings demonstrate the practical applicability of the developed methodology for assessing market dynamics in the presence of renewable energy sources. [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]

In the past, lightning strike risk assessment of buildings mainly referred to the Protection against Lightning--Part 2% Risk Management (IEC 62305-2-2010) based on protection angle method. Lightning strike risk assessment of wind farms was conducted according to the Lightning Protection for Wind Energy System (IEC 61400-24-2019), which proposed the method of lightning strike risk assessment for wind turbine. In fact, the basic idea of the two is the same, that is, the source of the lightning strike wind turbine is transformed from the former S1 - S4 to the latter ND - NDJ. According to the above method, wind farm was evaluated, and it has been proved that the practice can not achieve good results. After 2018, China has issued the Guide to Evaluation of Lightning Protection Technology in Buildings (T/GZLY 3 - 2022) and the Technical Specifications for Lightning Interception in Forest Areas (T/LYCY 4062 - 2024) based on semicircle protection, in which the source of risk defined by lightning point was closer to the reality, highly targeted and effective. Taking offshore wind farm as an example, this paper introduced a new method of establishing six evaluation indicators to determine the risk level according to the principle of compliance and the new protection technology of semi-circular method, which can be used as a reference for technical personnel. [ABSTRACT FROM AUTHOR]

In this work, we numerically investigated the effects of yaw angle on the wake and power characteristics of two National Renewable Energy Laboratory (NREL) 5 MW wind turbines based on actuator line method (ALM) and large eddy simulation (LES) under a neutral atmospheric boundary layer (ABL) with specified offshore surface roughness. The turbines are placed in tandem, with a spacing of seven rotor diameters, and the yaw angles range from 0° to 30°. The results indicate that under coordinated yaw conditions, the wakes of the two turbines significantly shift with increasing yaw angles, encroaching on the trailing edge of the turbines. The expansion of the wakes also gradually weakens, leading to a reduction in width. The superposition of the wake generated by the downstream turbine diminishes, leading to both turbines exhibiting approximately comparable physical characteristics within their respective wakes. As the wake of the upstream turbine propagates downstream, a secondary low-speed region emerges between the primary low-speed zone of the wake of downstream turbine and the surrounding atmosphere. With the increase in yaw angle, this secondary low-speed region significantly enhances the rate of wake recovery while also inducing a more pronounced deflection of the wake, thereby demonstrating a stronger entrainment effect. Regarding load characteristics, the time history of power characteristics and the power spectral density (PSD) spectra indicate a good turbine response to the inflow. The power characteristics of the upstream turbine exhibit a scaling law is closely related to the yaw angle. The quantitative relationship is established between yaw angle and the power distribution of the turbines, alongside a proposed correlation between the yaw angle and the cos 2 (γ) scaled power curve. The power of upstream turbine decreases and the power of downstream turbine gradually increases with the increase in yaw angle. It is further found that the downstream turbine demonstrates optimal performance at a yaw angle of 20°due to the influence of the yawed upstream turbine. These analyses provide insights into the characteristics of wind turbine arrays under yaw conditions from the perspective of unsteady wake features, interactions, and aerodynamic performance, which can aid in wind farm unit planning and control strategies. [ABSTRACT FROM AUTHOR]

The definition of an ecological niche makes it possible to anticipate the responses of a species to changing environmental conditions. Broad tolerance limits and a paucity of readily observable niches in the pelagic zone make it difficult to anticipate responses of the plankton community related to anthropogenic or environmental changes. Plankton distributions are closely linked to climate change and shape the seascape for higher trophic levels, so monitoring plankton distributions and defining ecological niches will help to understand and predict ecosystem responses. Here we apply a machine learning autoencoder and a density‐based clustering algorithm to high‐frequency datasets sampled with a ROTV Triaxus in the North Sea. The results indicate that in this highly dynamic environment, local hydrography prevents niche‐based separation of plankton species at the sub‐mesoscale, despite the availability of different habitats. Plankton patches were associated with naturally occurring frontal systems and anthropogenically induced upwelling‐downwelling dipoles in the vicinity of offshore wind farms (OWFs). [ABSTRACT FROM AUTHOR]

In recent years, high-power three-level neutral point clamped (NPC) inverters have significantly increased, particularly in the renewable energy sector and industries. These inverters are crucial in applications such as offshore wind farms and high-power motor drives, where reducing switching losses and improving the overall longevity, efficiency, and reliability of inverter systems are key. While conventional space vector pulse width modulation (SVPWM) has been used to reduce switching losses, it also increases the computational burden in the αβ coordinates. Our study introduces an effective and highly efficient carrier-based discontinuous pulse width modulation (DPWM) method specifically designed for high power factor loads to address this challenge. This approach significantly reduces computational execution time by adding the zero-sequence function into the fundamental signals. The efficiency of our new method is evident in the results: the proposed DPWM has successfully reduced switching losses, leading to a substantial enhancement of system efficiency. Our space vector analysis and simulation results, achieved using MATLAB and PLECS software, further validate the efficiency of our approach. Additionally, experimental results with a smallscale three-phase NPC inverter were conducted to validate the effectiveness and efficiency of the proposed DPWM. [ABSTRACT FROM AUTHOR]

Offshore windfarms are a potential threat to seabirds, partly due to collision risk with turbine blades. Wind influences the mode, height and speed of seabird flight, and therefore the risk of collision with turbines. We investigated how wind influences the flight of black-legged kittiwakes Rissa tridactyla, a gull of conservation concern, in order to incorporate these findings into collision risk estimates and identify mitigation measures. We used GPS telemetry data (23rd June to 10th August 2021) from 20 kittiwakes breeding in Aberdeenshire, UK (57.385°N, 1.868°W) to estimate the effect of wind on behavioural state, proportion of flight at collision risk height, probability of collision when within the rotor-swept zone, and overall collision risk. We found that as windspeed increased, kittiwakes commuted less and rested more. With increasing windspeed, kittiwakes spent a considerably smaller proportion of their flight time in the rotor-swept zone, but had a slightly higher probability of collision while in it. Uncertainty was high for most relationships between windspeed and kittiwake flight metrics. The overall effect of increasing windspeed on collision risk was negative, although we did not model avoidance rate. Effects of windspeed on collision risk were largely mediated through effects on commuting flight, and contingent on wind direction. Collision risk estimates incorporating the effects of windspeed may have greater precision and accuracy, but considerable uncertainty in windspeed-flight parameter relationships remains. Therefore although kittiwake collision risk may be mitigated by raising the 'cut-in' windspeed above which wind turbines generate power, the magnitude of this effect is uncertain. [ABSTRACT FROM AUTHOR]

The diode-rectifier unit (DRU)-based high-voltage direct current (HVDC) transmission system offers an economical solution for offshore wind power transmission. However, this approach requires offshore wind farms to establish a strong grid voltage. To meet this requirement while fulfilling the dynamic characteristics of the DRU, this paper proposes an advanced grid-forming (GFM) control strategy for offshore wind turbines connected to DRU-HVDC. The strategy incorporates a P-U controller and a Q- ω controller based on reactive power synchronization. Furthermore, a novel virtual power-based pre-synchronization method and an adaptive virtual impedance technique are integrated into the proposed GFM control to improve system performance during wind turbine (WT) integration and low-voltage ride-through (LVRT) scenarios. The virtual power-based pre-synchronization method reduces voltage spikes during the integration of new wind turbines, while the adaptive virtual impedance technique effectively suppresses fault currents during low-voltage faults, enabling faster recovery. Simulation results validate the effectiveness of the proposed GFM control strategy, demonstrating improved start-up and LVRT performance through the pre-synchronization and adaptive virtual impedance methods. [ABSTRACT FROM AUTHOR]

Digital data management for maintenance in offshore wind farms The DiMoWind‐Inspect research project explored the essential requirements for implementing digital methods in the maintenance of offshore wind turbines. Currently, data from all stages of the turbines' lifecycles are underutilized, leading to suboptimal maintenance actions being taken in terms of location, timing, and cost. A consistent structuring of previously often unstructured construction and maintenance data developed in the project enables their cross‐disciplinary, user‐specific availability. To accomplish this, the principles of the Reference Designation System for Power Plants RDS‐PP are applied. In this way, additional information, such as maintenance measures or defects, can be structured alongside components. The components of the structures are directly linked with information from inspections and other maintenance activities, following the Building Information Modeling approach. This allows for processing operational information about the condition of the structure and providing it for relevant calculations and assessments. As a use case, a two‐stage concept is presented, utilizing maintenance information to assess the fatigue life of a corrosion‐damaged structural detail in the support structure of an offshore wind turbine. [ABSTRACT FROM AUTHOR]

Aiming at the problem of sub‐synchronous oscillations induced by direct‐drive wind farms with series‐compensated lines, this paper proposes an unknown system dynamics estimation‐based PI controller to achieve sub‐synchronous oscillation suppression. According to the mathematical model of the direct‐drive wind farm grid‐connected system, the relationship between the direct‐drive wind turbine grid‐side converter current inner‐loop PI controller and the sub‐synchronous components is first established. Secondly, the uncertain sub‐synchronous current components and voltage components in the series‐compensated lines of the direct‐drive wind farm are taken as the total disturbance, and an unknown system dynamic estimator‐based PI controller is designed by introducing the first‐order low‐pass filter operation. Then, the stability and convergence of the closed‐loop system are proved by Lyapunov theory. Finally, the Prony method is used to analyse the current signal output by the direct‐drive wind turbine, and the inherent characteristics of the negative damping of the SSO induced by the series‐compensated line of the direct‐driven wind farm are revealed. A comparative numerical simulation is carried out to demonstrate that the sub‐synchronous oscillations of the direct‐drive wind farm with series‐compensated lines can be suppressed under different operating conditions. [ABSTRACT FROM AUTHOR]

This article discusses the challenges of integrating wind and solar energy into the power grid, focusing on the variability of these energy sources and the potential for blackouts due to system frequency falling. It also criticizes the unethical behavior of renewable energy forecasters who provide inaccurate forecasts to demonstrate high accuracy, which hinders the integration of renewable energy. The author argues for improved predictability and reporting metrics to ensure the resilience of the renewable energy transition. The article suggests that the forecast community should adopt stricter guidelines, similar to the accounting industry, for accurate reporting. Additionally, the website forecasters.org provides information on manuscript preparation and submission for the journal Foresight, which is a useful resource for library patrons conducting research and looking to submit articles to the journal. [Extracted from the article]

Connection parameters are the key factors influencing the responses of modular floating structures; due to the complexity of structural response properties, the assessment and optimization of connection parameters are still vital issues in designing modular floating structures. In the present work, for a novel structural configuration of self-adaptive modular floating wind farms based on existing works from the case of a mobile offshore base, a quantified approach for the assessment of connection parameters is established based on frequency domain numerical analysis taking into account both the economic effects and generalized performance. Based on the quantified assessment, an optimization process is carried out, to obtain a connection parameter combination. From the optimized connection parameters, it can be found that the most appropriate tri-axial stiffness according to present studies is in the magnitude from 1 × 106 to 7 × 107 N/m, and the damping ratio is close to 1.0 for most connection structures. By contrast with feasible uniform connection parameters, the optimization methodology is confirmed to be able to reduce both connection parameters and responses. Then, a time domain approach for the calculation of motions of interconnected modular floating structures taking into account geometry nonlinearity is proposed and obtained in good accordance with the frequency domain results, and the effectiveness of both the frequency domain and time domain is validated. [ABSTRACT FROM AUTHOR]

Shallow-water mooring chains of floating offshore wind turbines pose challenges such as nonlinear tension characteristics, high cost and prominent fatigue issues, which seriously hinder the large-scale use of floating wind power. Based on the hydrological and meteorological data of an offshore wind farm in the South China Sea, a sensitivity analysis of the mooring chain fatigue of a semi-submersible floating offshore wind turbine in shallow water was conducted with different environmental loads and mooring chain design parameters. It was found that mooring fatigue is mainly caused by the wave condition within diagonal distribution of the wave dispersion diagram. The mooring chain fatigue calculated using the main monochromatic wave was more conservative than that calculated using the wave scatter diagram with a difference of about 28%. The mooring fatigue calculated using turbulent wind spectrum was greater than that calculated using steady wind. In contrast, waves are the main source of mooring fatigue, and the second-order wave force has an undeniable impact on the mooring fatigue of a semi-submersible floating offshore wind turbine, which accounts for approximately 26% of wave-induced mooring fatigue. In addition, the current-induced mooring fatigue should not be neglected, although its effect is not as large as that caused by the wave or wind loads. An increase in the cross-sectional diameter and the length of a mooring chain, as well as a reasonable clump weight arranged, all contribute to improving the fatigue life of mooring chains in shallow water, which provide useful technical references for the optimization of mooring lines fatigue in shallow-water areas. [ABSTRACT FROM AUTHOR]