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Offshore wind farms are great options for addressing the world's energy and climate change challenges, as well as meeting rising energy demand while taking environmental and economic impacts into account. Floating wind turbines, in specific, depict the next horizon in the sustainable renewable energy industry. In this study, a life-cycle cost analysis for floating offshore wind turbines is developed by combining the most recent data and parametric formulas from databases and relevant papers. The cost analysis models focused on cost minimization with special emphasis on Operation and Maintenance Cost (OPEX), Decommissioning Cost (DECOM), and Levelized Cost of Energy (LCOE), which are important factors in wind power economy. Given that floating wind energy is still developing, the presented scenarios should be beneficial in making future decisions. The cost analysis scenarios include on-site and off-site maintenance scenarios for OPEX. In addition, four alternative scenarios for DECOM have been examined: mechanical recycling, mechanical-incineration, incineration processes, and landfill. According to the findings of these scenarios, OPEX varies from 16.89 to 19.93 £/MWh and DECOM between 3.47 and 3.65 £/MWh, whilst the total LCOE varied from 50.67 to 66.73 £/MWh. [ABSTRACT FROM AUTHOR]

In recent years, the demand and requirement for renewable energy have significantly increased due to concerns regarding energy security and the climate crisis. This has led to a significant focus on wind power generation. As the deployment of wind turbines continues to rise, there is a growing need to assess their lifespan and improve their stability. Access to accurate load data is crucial for enhancing safety and conducting remaining life assessments of wind turbines. However, maintaining and ensuring the reliability of measurement systems for long-term load data accumulation, stability assessments, and residual life evaluations can be challenging. As a result, numerous studies have been conducted on load prediction for wind turbines. However, existing load prediction models based on 10 min statistical data cannot adequately capture the short-term load variations experienced by wind turbines. Therefore, it is essential to develop models capable of predicting load with a high temporal resolution to enhance reliability, especially with the increasing scale and development of floating wind turbines. In this paper, we developed prediction models with a 50 Hz resolution for the bending strain at the tower bottom of offshore wind turbines by combining SCADA data and acceleration data using machine learning techniques and analyzed the results. The load prediction models demonstrated high accuracy, with a mean absolute percentage error below 4%. [ABSTRACT FROM AUTHOR]

Wind power, as a renewable energy for coping with global climate change challenge, has achieved rapid development in recent years. The breakdown of wind turbines (WTs) not only leads to high repair expenses but also may threaten the stability of the whole power grid. How to reduce the operation and the maintenance (O&M) cost of wind farms is an obstacle to its further promotion and application. To provide reliable condition monitoring and fault diagnosis (CMFD) for WTs, this paper presents a comprehensive survey of the existing CMFD methods in the following three aspects: energy flow, information flow, and integrated O&M system. Energy flow mainly analyzes the characteristics of each component from the angle of energy conversion of WTs. Information flow is the carrier of fault and control information of WT. At the end of this paper, an integrated WT O&M system based on electrical signals is proposed. [ABSTRACT FROM AUTHOR]

Even where physical conditions appear perfectly suited for wind power production, there is significant variation in the number of turbines installed. This pattern suggests that physical conditions are a pre-requisite for, but not a determinant of, that production. This study reports the results of an analysis of the county-level correlates of wind power installations in the north–south corridor of the central United States, which contains much of the country's greatest land-based wind resources. This study focuses on the relative effects of social capital, global climate change concern, and local biodiversity, while controlling for other potential explanations that previous research has identified as leading to support for or to opposition to turbine installation. We find (1) that greater local biodiversity is associated with fewer turbine installations; (2) that the percent of the public who believe humans are causing climate change is not associated with the number of installed turbines; and (3) that a higher degree of county-level social capital is associated with fewer installations. These findings suggest the predominance of local considerations over global ones when it comes to the actual siting of turbines. [ABSTRACT FROM AUTHOR]

No accepted fluid theory exists for power extraction from unpressurized confined flow. The absence of a valid model to determine baseline uniform power extraction in confined flows creates difficulties in characterizing the coefficient of power. Currently, the primary body of research has been limited to Diffuser Augmented Wind Turbines (DAWTs) and passive fluid accelerators. Fluid power is proportional to the cube of velocity; therefore, passive acceleration is a promising path to effective renewable energy. Hypothetical models and experiments for passive accelerators yield low ideal power limits and poor performance, respectively. We show that these results derive from the misapplication of Betz's Law and lack of a general theory for confined flow extraction. Experimental performance is due to the low efficiency of DAWTs and prior hypotheses exhibit high predictive error and continuity violations. A fluid model that accurately predicts available data and new experimental data, showing disk specific maximum CP for the confined channel at 38% of power available to disk, is presented. This is significantly lower than the 59% Betz freestream limit yielded by hypothetical models when the area ratio equals one. Experiments and their results are presented with non-DAWT accelerators, where new experimental results exceed CP limits predicted previously and correlate with the proposed predictive model. [ABSTRACT FROM AUTHOR]

The objective of the present study is to show a comprehensive assessment of the wind resource dynamics along the Spanish coastal environment of the Iberian Peninsula. After studying the historical resources (reported at 100 m height) for the 20-year period from 1999 to 2018 by analyzing the ERA5 time series of wind speed data, the 10 locations with highest historical wind resources are considered. For these, the study of the future dynamics for the 30-year period from 2021 to 2050 under the climate change scenario RCP 4.5 is carried out. After further selection, mean and maximum values, as well as the seasonal and monthly variability of the wind power density, are obtained for six locations along the Spanish coasts. Furthermore, a performance and economic dynamics assessment is presented for four different wind turbine technologies with rated capacities ranging between 3 and 9.5 MW. A further comparison with other locations in the Baltic Sea and the Black Sea is presented to provide a critical image of the Spanish wind resources dynamics in the European framework. The results indicate a noticeable gain of wind resources in various locations of the Atlantic and Mediterranean coasts, with others presenting slight losses. [ABSTRACT FROM AUTHOR]