Use of Composite Materials in Wind Turbine Blades: A Comprehensive Review
Abstract
Volume 8,Issue 2
Use of Composite Materials in Wind Turbine Blades: A Comprehensive ReviewAbstract The increasing demand for sustainable energy has accelerated the development of high-performance wind turbine systems. In this context, material selection for turbine blades plays a critical role in ensuring structural efficiency, fatigue resistance, and long-term reliability. This study evaluates the mechanical and structural advantages of carbon fiber-reinforced polymer (CFRP) composites over glass fiber-reinforced polymer (GFRP) materials in wind turbine blade applications. Results from the l [...] |
Development of Binderless Waste-Derived Briquettes: Effect of Plastic Content on Combustion Performance and KineticsAbstract Waste-derived briquettes offer a promising alternative to fossil fuels and are characterised by low production costs, high volumetric calorific value, robust mechanical strength, and excellent durability. To optimize their adoption, four variants of waste-derived briquettes were produced with different mass ratios of sawdust to polyethylene terephthalate (PET) plastic (100:0, 90:10, 80:20, and 70:30) using high-pressure compaction without binders. Thermogravimetric analysis (TGA) and the Coats-R [...] |
Stochastic Assessment of Renewable Energy Reliability: A Case Study of North Euboea, Greeceby
Abstract The increasing penetration of renewable energy sources (RES) in the energy mix, particularly solar photovoltaic and wind power, poses significant challenges to electricity grid reliability due to their inherent stochastic variability. This study develops a stochastic framework to assess the ability of RES to balance electricity demand, with a focus on storage requirements and reliability implications. Using North Euboea, Greece, as a representative case study, normalized hourly time series of el [...] |
Photovoltaic Power Forecasting Without Local Data: A Spatially-Aware Approach Using Neighboring Plantsby
Abstract The increasing demand for renewable energy sources has intensified the adoption of photovoltaic systems. This study proposes predictive models for solar power generation that operate without dependence on on-site meteorological stations. The proposed approach integrates generation data from geographically distributed plants and accounts for the distance to meteorological stations when constructing climatic variables. Two machine learning techniques, Random Forest (RF) and Long Short-Term Memory [...] |
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