• Energy Research

A wind resource estimation study based on a series of 62 satellite wind field maps is presented. The maps were retrieved from imaging synthetic aperture radar (SAR) data. The wind field maps were used as input to the software RWT, which calculates the offshore wind resource based on spatial averaging (footprint modelling) of the wind statistic in each satellite image. The calculated statistics can then be input to the program WAsP and used in lieu of in-situ observations by meteorological instruments. A regional wind climate map based on satellite SAR images delineates significant spatial wind speed variations. The site of investigation was Horns Rev in the North Sea, where a meteorological time series is used for comparison. The advantages and limitations of these new techniques, which seem particularly useful for mapping of the regional wind climate, are discussed. Copyright © 2005 John Wiley & Sons, Ltd.

Abstract. Impact fatigue caused by collision with rain droplets, hail stones and other airborne particles, also known as leading-edge erosion, is a severe problem for wind turbine blades. Each impact on the leading edge adds an increment to the accumulated damage in the material. After a number of impacts the leading-edge material will crack. This paper presents and supports the hypothesis that the vast majority of the damage accumulated in the leading edge is imposed at extreme precipitation condition events, which occur during a very small fraction of the turbine's operation life. By reducing the tip speed of the blades during these events, the service life of the leading edges significantly increases from a few years to the full expected lifetime of the wind turbine. This life extension may cost a negligible reduction in annual energy production (AEP) in the worst case, and in the best case a significant increase in AEP will be achieved.

Precipitation is a key driver of leading-edge erosion of wind turbine blades, which leads to a loss in annual energy production and high cost for repair of wind turbines. Precipitation type, drop size and their frequency are relevant parameters, but not easily available. Reflectivity-Rain Rate (Z‑R) relationships as well as annual sums of rainfall amount and rainfall kinetic energy potentially could be used to estimate leading-edge erosion. Although Z‑R relationships and amounts are known for several places, their spatial variation and dependence on weather types is unknown in the North Sea and Baltic Sea area. We analysed time series of multiple disdrometers located on the coast of the North Sea and Baltic Sea to characterize the variation and weather-type dependence of the Z‑R relationship, precipitation type, rainfall amount and kinetic energy.The Z‑R relationship as indication for the mean drop-size distribution showed small variations within different locations, but had a large variability for specific, but rare weather types. Only the precipitation types snow and hail showed some tendencies of weather-type dependence. Rainfall amount and rainfall kinetic energy were higher for stations in the eastern part of the North Sea compared to the western part and the Baltic Sea. Highest values were related to advection from the West. Overall, variations with location and weather type were found. These results will need to be considered in leading-edge erosion modelling and site assessment.

This research presents an industry level gap analysis for Chinese offshore wind, which serves as a way to illuminate how China may fast track industry evolution. The research findings provide insight into how the Chinese government strongly and systematically decrees state-owned Chinese firms to expand into overseas markets to speed up learning efforts. Insights are offered regarding the nation-level strategic plans and institutional support policies mobilized by China in order to be able to conquer market shares internationally by building a strong home market and then facilitating an end-to-end and fully financed export solution. This is interesting in itself and in particular so because it now also includes complex billion-dollar megaprojects such as turnkey offshore wind farm assets with an expected lifespan of 30+ years. Research findings are provided on how European and Chinese firms may successfully forge long-term alliances also for future Chinese wind energy export projects. Examples of past efforts of collaboration not yielding desired results have been included as well. At policy level, recommendations are provided on how the evolution of the Chinese offshore wind power industry can be fast-tracked to mirror the revolutionary pace, volume, and velocity which the Chinese onshore wind power industry has mustered.

We study the wind climate and its long-term variability in the North Sea and South China Sea, areas relevant for offshore wind energy development, using satellite-based wind data, because very few reliable long-term in-situ sea surface wind observations are available. The Special Sensor Microwave Imager (SSM/I) ocean winds extrapolated from 10 m to 100 m using the Charnock relationship and the logarithmic profile method are compared to Weather Research and Forecasting (WRF) model results in both seas and to in-situ observations in the North Sea. The mean wind speed from SSM/I and WRF differ only by 0.1 m/s at Fino1 in the North Sea, while west of Hainan in the South China Sea the difference is 1.0 m/s. Linear regression between SSM/I and WRF winds at 100 m show correlation coefficients squared of 0.75 and 0.67, standard deviation of 1.67 m/s and 1.41 m/s, and mean difference of −0.12 m/s and 0.83 m/s for Fino1 and Hainan, respectively. The WRF-derived winds overestimate the values in the South China Sea. The inter-annual wind speed variability is estimated as 4.6% and 4.4% based on SSM/I at Fino1 and Hainan, respectively. We find significant changes in the seasonal wind pattern at Fino1 with springtime winds arriving one month earlier from 1988 to 2013 and higher winds in June; no yearly trend in wind speed is observed in the two seas.

In view of the high cost and sparse spatial resolution of offshore meteorological observations, ocean winds retrieved from satellites are valuable in offshore wind resource assessment as a supplement to in situ measurements. This study examines satellite synthetic aperture radar (SAR) images from ENVISAT advanced SAR (ASAR) for mapping wind resources with high spatial resolution. Around 181 collected pairs of wind data from SAR wind maps and from 13 meteorological stations in Hangzhou Bay are compared. The statistical results comparing in situ wind speed and SAR-based wind speed show a standard deviation (SD) of 1.99 m/s and correlation coefficient of R = 0.67. The model wind directions, which are used as input for the SAR wind speed retrieval, show a high correlation coefficient (R = 0.89) but a large standard deviation (SD = 42.3°) compared to in situ observations. The Weibull probability density functions are compared at one meteorological station. The SAR-based results appear not to estimate the mean wind speed, Weibull scale and shape parameters and wind power density from the full in situ data set so well due to the lower number of satellite samples. Distributions calculated from the concurrent 81 SAR and in situ samples agree well.

Abstract Leading edge erosion on wind turbine blades can reduce aerodynamic efficiency and cause increased maintenance costs, potentially impacting the overall economic viability. Erosion-safe operation is the concept of reducing the blade tip speed during periods of heavy rain, thereby significantly reducing the erosion development and progression. This study explores the application of reinforcement learning, namely using a double deep Q-network, to implement erosion-safe operation. The proposed methodology involves learning a policy for tip speed control that maximizes revenue over a specific period of time. We demonstrate the concept based on 5 years of simulation of the DTU 10MW reference turbine and mesoscale weather simulation from Horns Rev. The trained model was found to increase the cumulative revenue by 1.6 % compared to not using erosion-safe operation. The model was able to effectively adapt to varying weather conditions and stochastic damage progression. Based on 10,000 random simulations, the trained model outperforms two baseline models in more than 98 % of the simulations.