浏览全部资源
扫码关注微信
Published： 2018-3 ，
Accepted： 13 September 2017
扫 描 看 全 文
朱晓辉, 杨晓峰, 杜延磊, 井成, 刘桂红, 李紫薇. 2018. 风能方向性参量遥感反演. 遥感学报, 22(2): 253–264
Zhu X H, Yang X F, Du Y L, Jing C, Liu G H and Li Z W. 2018. Estimation of directional distribution of wind energy from satellite observations. Journal of Remote Sensing, 22(2): 253–264
目前可遥感反演的海上风能参量主要为平均风速和平均风功率密度，缺乏对风能方向性参量的反演。本文建立了以风向频率、风能密度方向分布为核心的风能方向性参量体系及相应的反演方法，使用2007年—2016年ASCAT星载散射计观测数据进行了反演实验，并利用海上现场观测数据对反演结果进行比较验证，通过理论分析和模拟实验对反演方法的数据量需求和误差传递进行了分析。结果表明，90%的反演结果通过了所有的同一性检验，验证了其有效性和准确性；风向频率和风能密度方向分布准确反演所需的最小数据量分别为350条和800条；遥感反演的风速风向数据的误差使得最终反演的风能方向性参量趋于离散，真实的风能方向分布越集中，对其影响越敏感。
The offshore wind energy industry has surged in recent years as a main source of clear energy. Meanwhile
remote sensing can retrieve sea surface wind data and provide valuable information to locate suitable offshore wind power plants. Published studies mostly focus on the retrieval of average wind speed and average wind power density. However
wind directional distribution is also required in locating potential offshore wind power plants. Therefore
a method is proposed to retrieve the directional distribution of wind energy from satellite scatterometer observations. According to Chinese national standard (GB/T 18710-2002)
wind directional frequency and directional distribution of wind energy density are used to delineate the directional distribution of wind energy. In addition
certain circular statistical parameters of wind energy
circular mean
and circular standard deviation are selected to depict the characteristic of wind energy. Wind speed and directional data in certain timespans are used as input in the proposed method. These wind data can be obtained from satellite observations or reanalysis datasets. Then
0.1°×0.1° global maps of wind energy directional distribution parameters are retrieved by the proposed method from 2007—2016 advanced scatterometer (ASCAT) wind products. These retrieval results are accordant with antecedent research and scientific facts. To test and verify these retrieved results
they are compared with parameters calculated from 20 NDBC buoys. First
the ASCAT wind products and buoy data are screened for quality control. Then
all buoy-measured winds are converted to 10 m height to match the ASCAT wind retrievals. Subsequently
a time and spatial matchup dataset of ASCAT and buoy data are built
and the differences between them are calculated. A non-parametric hypothesis test is also implied to assess the accuracy of circular error statistics. A total of 80% of the test dataset pairs passed all hypothesis tests and satisfied all assessment criteria (total difference <10%). Meanwhile
100% of the dataset pairs satisfied the criteria and passed the test in which only wind direction parameters were considered. Furthermore
90% of the dataset pairs passed the test for wind energy circular statistical parameters. In addition
80% of the dataset pairs satisfied the total difference criteria of directional distribution wind energy. One model was constructed to describe the relation between the retrieval accuracy of wind directional distributions and record number using the law of large numbers and a basic understanding of the remote sensing process. The function that expresses the relation between record number and retrieval accuracy was obtained
and the minimal record number required to obtain reliable retrieval results was calculated with the aforementioned function by numerical stimulation and curve fitting. Moreover
the impact of the time characteristic of remote sensing data (fixed local time) was considered in the analysis. Models were built to describe the retrieval error of the remote sensing process and the actual offshore wind directional distributions on the basis of normal distribution and circular statistics. These models enabled us to understand the effect of the retrieval error of remote sensing quantitatively and qualitatively. Thus
the deconvolution algorithm was designed to eliminate this effect. Conclusion: (1) The proposed parameter systems can be retrieved by remote sensing data to depict the directional characteristics of wind energy thoroughly. (2) Two methods
namely
the total difference and the Kuiper test
were effectively used to test and verify the difference among the directional distributions. The Kuiper test results showed that the total difference identical criterion of the two directional distributions was built. (3) The validation demonstrated the effectivity and accuracy of the new method and the data product produced by this model. (4) At least 800 records were required for the reliable retrieval of DDED
and the number for DF was 350. (5) The error in the retrieval of wind direction from remote sensing data influenced not only the final retrieval accuracy of the directional distribution of wind energy but also the distribution itself. The more dispersed the distribution
the less the effect of the retrieval error of wind direction on the accuracy of directional distribution retrieval.
风能风能方向性参量ASCAT风向频率风能密度方向分布海面风场方向统计
offshore wind energywind directional distributionASCATwind direction frequencywind power densitysea surface wind vectordirection statistics
Barthelmie R J, Grisogono B and Pryor S C. 1996. Observations and simulations of diurnal cycles of near-surface wind speeds over land and sea. Journal of Geophysical Research: Atmospheres, 101(D16): 21327–21337
Barthelmie R J and Pryor S C. 2003. Can satellite sampling of offshore wind speeds realistically represent wind speed distributions?. Journal of Applied Meteorology, 42(1): 83–94
Batschelet E. 1981. Circular Statistics in Biology. London: Academic Press: 28
Carta J A, Bueno C and Ramírez P. 2008. Statistical modelling of directional wind speeds using mixtures of von Mises distributions: case study. Energy Conversion and Management, 49(5): 897–907
Chang R, Zhu R, Badger M, Hasager C B, Xing X H and Jiang Y R. 2015. Offshore wind resources assessment from multiple satellite data and WRF modeling over South China Sea. Remote Sensing, 7(1): 467–487
中国可再生能源学会风能专业委员会. 2016. 2015年中国风电装机容量统计. 风能(2): 48–63
Chinese Wind Energy Association. 2016. The statistics of installed wind power capacity of China in 2015. Wind Energy(2): 48–63 (
Doubrawa P, Barthelmie R J, Pryor S C, Hasager C B, Badger M and Karagali I. 2015. Satellite winds as a tool for offshore wind resource assessment: the Great Lakes Wind Atlas. Remote Sensing of Environment, 168: 349–359
Estévez J, Gavilán P and Giráldez J V. 2011. Guidelines on validation procedures for meteorological data from automatic weather stations. Journal of Hydrology, 402(1/2): 144–154
中华人民共和国国家质量监督检验检疫总局. 2002. GB/T 18710-2002风电场风能资源评估方法. 北京: 中国标准出版社: 6
General Administration of Quality Supervision, Inspection and Quarantine of the People′s Republic of China. 2002. GB/T 18710-2002 Methodology of wind energy resource assessment for wind farm. Beijing: Standards Press of China: 6
Hasager C B, Mouche A, Badger M, Bingöl F, Karagali I, Driesenaar T, Stoffelen A, Peña A and Longépé N. 2015a. Offshore wind climatology based on synergetic use of Envisat ASAR, ASCAT and QuikSCAT. Remote Sensing of Environment, 156: 247–263
Hasager C B, Vincent P, Badger J, Badger M, Di Bella A, Peña A, Husson R and Volker P J H. 2015b. Using satellite SAR to characterize the wind flow around offshore wind farms. Energies, 8(6): 5413–5439
Heckenbergerova J, Musilek P and Krömer P. 2015. Optimization of wind direction distribution parameters using particle swarm optimization//Abraham A, Krömer P and Snasel V, eds. Afro-European Conference for Industrial Advancement. Cham: Springer: 15–26 [DOI: 10.1007/978-3-319-13572-4_2]
Heckenbergerová J, Musilek P, Mejznar J and Vančura M. 2013. Estimation of wind direction distribution with genetic algorithms//Proceedings of the 26th IEEE Canadian Conference on Electrical and Computer Engineering. Regina, SK, Canada: IEEE: 78–81 [DOI: 10.1109/CCECE.2013.6567681]
Hsu S A, Meindl E A and Gilhousen D B. 1994. Determining the power-law wind-profile exponent under near-neutral stability conditions at sea. Journal of Applied Meteorology, 33(6): 757–765
Kuiper N H. 1960. Tests concerning random points on a circle. Indagationes Mathematicae (Proceedings), 63: 38–47
Masseran N, Razali A M, Ibrahim K and Latif M T. 2013. Fitting a mixture of von Mises distributions in order to model data on wind direction in Peninsular Malaysia. Energy Conversion and Management, 72: 94–102
娜仁花. 2015. 基于模糊综合评价模型的风电场选址研究. 北京: 华北电力大学: 1–2
Na R H. 2015. Researching on Location of Wind Power Farm based on Fuzzy Comprehensive Evaluation Model. Beijing: North China Electric Power University: 1–2
Pewsey A, Neuhäuser M and Ruxton D G. 2013. Circular Statistics in R. Oxford, UK: Oxford University Press: 22, 27
Ricciardulli L and Wentz F J. 2016. Remote sensing systems ASCAT C-2015 daily ocean vector winds on 0.25 deg grid, version 02.1, [0000 to 49024]. Santa rosa, CA: Remote Sensing Systems.www.remss.com/missions/ascathttp://www.remss.com/missions/ascat. [2016-04-05]
夏云峰. 2016. 2015年欧洲海上风电新增并网容量超过3GW. 风能(2): 64–69
Xia Y F. 2016. The increase interconnection capacity of European offshore wind power in 2015 is more than 3 GW. Wind Energy(2): 64–69 (
Yang X F, Li X F, Pichel W G and Li Z W. 2011. Comparison of ocean surface winds From ENVISAT ASAR, MetOp ASCAT scatterometer, buoy measurements, and NOGAPS model. IEEE Transactions on Geoscience and Remote Sensing, 49(12): 4743–4750
Yang X F, Liu G H, Li Z W and Yu Y. 2014. Preliminary validation of ocean surface vector winds estimated from China′s HY-2A scatterometer. International Journal of Remote Sensing, 35(11/12): 4532–4543
郑崇伟, 胡秋良, 苏勤, 杨艳. 2014. 国内外海上风能资源研究进展. 海洋开发与管理, 31(6): 25–32
Zheng C W, Hu Q L, Su Q and Yang Y. 2014. The review of progress of global offshore wind energy. Ocean Development and Management, 31(6): 25–32 (
郑崇伟, 潘静. 2012. 全球海域风能资源评估及等级区划. 自然资源学报, 27(3): 364–371
Zheng C W and Pan J. 2012. Wind energy resources assessment in global ocean. Journal of Natural Resources, 27(3): 364–371 (
朱明. 2015. 实现风电等可再生能源从补充能源向替代能源转变是“十三五”规划的主基调. 风能(11): 18–19
Zhu M. 2015. To transform the role of wind energy and other renewable energy from complementary energy to alternative energy is the fundamtion of the thirteenth Five-Year Plan. Wind Energy(11): 18–19 (
Zhu X H and Yang X F. 2016. Uncertainties in directional offshore wind distribution estimate from satellite remote sensing. Proceedings of 2016 IEEE International Geoscience and Remote Sensing Symposium. Beijing, China: IEEE: 4623–4626 [DOI: 10.1109/IGARSS.2016.7730206]
相关文章
相关作者
相关机构
京公网安备11010802024621