Abstract
The objective of this article is to review the methodologies used in the last 15 years to estimate the power loss in wind turbines due to their exposure to adverse meteorological conditions. Among the methods, the use of computational fluid dynamics (CFD) for the three-dimensional numerical simulation of wind turbines is highlighted, as well as the use of two-dimensional CFD simulation in conjunction with the blade element momentum theory (BEM). In addition, a brief review of other methodologies such as image analysis, deep learning, and forecasting models is also presented. This review constitutes a baseline for new investigations of the icing effects on wind turbines’ power outputs. Furthermore, it contributes to a continuous improvement in power-loss prediction and the better response of icing protection systems.
Highlights
In the Nordic countries, where the wind potential is higher, wind production is abundant in winter as the power available in the wind varies with the cube of the wind speed
This paper focuses on the actual state-of-art of power loss estimation of wind turbines under icing conditions using numerical methods such as computational fluid dynamics (CFD) (Computational Fluid Dynamics)
The results showed that ice formation on the blade-tip of the airfoil’s leading edge reduced wind turbine power performance by 8 to 29% at lower-than-rated wind speeds, showing an evident correlation between both parameters
Summary
In the Nordic countries, where the wind potential is higher, wind production is abundant in winter (the wind is stronger, and the air density is higher) as the power available in the wind varies with the cube of the wind speed. When blades are covered with ice, the aerodynamic properties change (lift and drag coefficients), and as a result, the power generated is reduced or sometimes ice can stop the turbine [2,3,4,5,6]. Annual icing losses were estimated to be about 20% of the yearly power output [6,7] and between 20% and 50% in the aerodynamic performance [7]. The best option for maximizing energy production from a turbine operating under icing conditions involves understanding the performance loss that may be predicted during the icing event [5]
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