Prediction of Extreme Wind Speed for Offshore Wind Farms Considering Parametrization of Surface Roughness

Abstract

Large-scale offshore wind farms (OWF) are under construction along the southeastern coast of China, an area with a high typhoon incidence. Measured data and typhoon simulation model are used to improve the reliability of extreme wind speed (EWS) forecasts for OWF affected by typhoons in this paper. Firstly, a 70-year historical typhoon record database is statistically analyzed to fit the typhoon parameters probability distribution functions, which is used to sample key parameters when employing Monte Carlo Simulation (MCS). The sampled typhoon parameters are put into the Yan Meng(YM) wind field to generate massive virtual typhoon in the MCS. Secondly, when typhoon simulation carried out, the change in wind field roughness caused by the wind-wave coupling is studied. A simplified calculation method for realizing this phenomenon is applied by exchanging roughness length in the parametric wind field and wave model. Finally, the extreme value theory is adopted to analyze the simulated typhoon wind data, and results are verified using measured data and relevant standards codes. The EWS with 50-year recurrence of six representative OWF is predicted as application examples. The results show that the offshore EWS is generally stronger than onshore; the reason is sea surface roughness will not keep growing accordingly as the wind speed increases. The traditional prediction method does not consider this phenomenon, causing it to overestimate the sea surface roughness, and as a result, underestimate the EWS for OWF affected by typhoons. This paper's methods make the prediction of EWS for OWF more precise, and results suggest the planer should choose stronger wind turbine in typhoon prone areas.

Joint Offshore Wind and Wave Energy Resources in the Caribbean Sea

“…Although the roughness length under open sea conditions is smaller (i.e., 0.0002), the studies conducted by Frank et al ( 2000 ) and Barthelmie ( 2001 ) both show that, when wind speed is extrapolated to heights greater than 10 m above sea level, variability in the sea surface roughness length is too small to have a noticeable impact on wind resources at typical hub heights (Barthelmie et al 2007 ). As this value is widely used in other studies (e.g., Ma et al 2021 ), it will also be used here to ensure consistency.…”

Wind Speed Forecast Based on the LSTM Neural Network Optimized by the Firework Algorithm

“…Physical approaches, which are based on a detailed physical description of the atmosphere, used meteorological data such as air temperature, topography, and pressure to predict wind speed, thus leading to intricate calculations and high costs [1]. Statistical methods, such as Autoregressive Integrated Moving Average (ARIMA) model, Seasonal ARIMA (SARIMA), Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model, and Monte Carlo Simulation [2], predict wind speed on the premise of linear assumption and are more accurate than physical methods [3,4]. However, the variation of wind speed contains significant nonlinear and chaotic characteristics, and it is usually difficult to accurately and effectively predict the future wind speed simply by applying these methods or models.…”