The quantification of the severity of erosion in wind turbine blades is challenging due to the many aspects involved, including meteorology, aerodynamics, materials science, and wind turbine dynamics. A complete model relies on several building blocks, which cover the characterization of precipitation and aerosols, the trajectories of droplets and particles, the operational settings of the wind turbine, and finally the structural response of the leading edge to the large number of impacts across a spectrum of particle sizes and impacts speeds. This paper presents a multidisciplinary model, defining magnitudes representative of erosion severity and formulating their dependencies. The method uses the formula for the erosion incubation time as defined by Springer. The model incorporates the effect of wind velocity and density, particle size, and erosion intensity, and it allows four erosion mechanisms to be considered: rainfall, snowfall, sea spray, and fog. Comparison of the predicted erosion incubation time versus blade inspections shows good qualitative agreement. The equations from the model suggest that the characterization of atmospheric conditions at the site is essential for an accurate estimation of the severity of erosion. Equally important are the material properties, and the impingement process at the leading edge.
Abstract. The continuing transition to renewable energy will require more wind turbines to be installed and operated on land and offshore. On land, wind turbines will increasingly be deployed in hilly or mountainous regions, which are often described together as “complex terrain” in the wind energy industry. These areas can experience complex flows that are hard to model, as well as cold climate conditions that lead to instrument and blade icing and can further impact wind turbine operation. This paper – a collaboration between several International Energy Agency (IEA) Wind Tasks and research groups based in mountainous countries – sets out the research and development needed to improve the financial competitiveness and ease of integration of wind energy in hilly or mountainous regions. The focus of the paper is on the interaction between the atmosphere, terrain, land cover, and wind turbines, during all stages of a project life cycle. The key needs include collaborative research and development facilities, improved wind and weather models that can cope with mountainous terrain, frameworks for sharing data, and a common, quantitative definition of site complexity. Addressing these needs will be essential for the affordable and reliable large-scale deployment of wind energy in many countries across the globe. Because of the widespread nature of complex flow and icing conditions, addressing these challenges will have positive impacts on the risk and cost of energy from wind energy globally.
This paper describes the comparison of a statistical and numerical case study of wind resource assessment and estimation of resultant Annual Energy Production due to ice of a wind park in ice prone cold region. Three years Supervisory Control and Data Acquisition data from a wind park located in arctic region is used for this study. Statistical analysis shows that the relative power loss due to icing related stops is the main issue for this wind park. While Larsen wake model is used for the CFD simulations, where results show that it is important to use the wake loss model for CFD simulations of wind resource assessment and AEP estimation of a wind park. A preliminary case study about wind park layout optimization has also been carried out which shows that AEP can be improved by optimizing the wind park layout and CFD simulations can be a good tool.
Abstract. Ice detection of wind turbine and estimating the resultant production losses is challenging, but very important, as wind energy project decisions in cold regions are based on these estimated results. This paper describes the comparison of a statistical (T19IceLossMethod) and numerical (Computational Fluid Dynamics, CFD) case study of wind resource assessment and estimation of resultant Annual Energy Production (AEP) due to ice of a wind park in ice prone cold region. Three years Supervisory Control and Data Acquisition (SCADA) data from a wind park located in arctic region is used for this study. Statistical analysis shows that the relative power loss due to icing related stops is the main issue for this wind park. To better understand the wind flow physics and estimation of the wind turbine wake losses, Larsen wake model is used for the CFD simulations, where results show that it is important to use the wake loss model for CFD simulations of wind resource assessment and AEP estimation of a wind park. A preliminary case study about wind park layout optimization has also been carried out which shows that AEP can be improved by optimizing the wind park layout and CFD simulations can be a good tool.
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