Suzhou Electric Appliance Research Institute
期刊號: CN32-1800/TM| ISSN1007-3175

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測風(fēng)數(shù)據(jù)不同時間尺度對風(fēng)電場產(chǎn)能評估的影響

來源:電工電氣發(fā)布時間:2022-09-26 15:26 瀏覽次數(shù):371

測風(fēng)數(shù)據(jù)不同時間尺度對風(fēng)電場產(chǎn)能評估的影響

馮浩,彭秀芳,項(xiàng)雯,吳浙攀,施晨
(中國能源建設(shè)集團(tuán)江蘇省電力設(shè)計(jì)院有限公司,江蘇 南京 211102)
 
    摘 要:風(fēng)資源狀況模擬的準(zhǔn)確度主要體現(xiàn)在測風(fēng)數(shù)據(jù)時間間隔上。以淮安某風(fēng)電場測風(fēng)塔一年及以上每 10 min 的測風(fēng)數(shù)據(jù)為基準(zhǔn),對不同時間間隔 (30 min、1 h、2 h) 所記錄的測風(fēng)數(shù)據(jù)分別應(yīng)用最小二乘法、平均風(fēng)速和標(biāo)準(zhǔn)差估計(jì)法及極大似然估計(jì)法求解風(fēng)速概率分布進(jìn)行產(chǎn)能評估,對不同時間間隔下計(jì)算所得的發(fā)電量進(jìn)行誤差對比。結(jié)果指出,風(fēng)電場發(fā)電量估計(jì)誤差隨著時間尺度增大而增大,采取 30 min 時間間隔測風(fēng)數(shù)據(jù)計(jì)算出的發(fā)電量與 10 min 實(shí)測數(shù)據(jù)計(jì)算結(jié)果更為接近,當(dāng)風(fēng)電場選址區(qū)域的測風(fēng)數(shù)據(jù)完整性較差時,研究結(jié)果具有一定指導(dǎo)作用。
    關(guān)鍵詞: 測風(fēng)數(shù)據(jù);時間間隔;威布爾參數(shù);產(chǎn)能評估
    中圖分類號:TM614     文獻(xiàn)標(biāo)識碼:A     文章編號:1007-3175(2022)09-0025-05
 
Influence of Different Time-Scale in Wind Measurement Data on
Productivity Evaluation of the Wind Farm
 
FENG Hao, PENG Xiu-fang, XIANG Wen, WU Zhe-pan, SHI Chen
(China Energy Engineering Group Jiangsu Power Design Institute Co., Ltd, Nanjing 211102, China)
 
    Abstract: The accuracy of state stimulation of the wind resource condition mainly reflects in the time interval of the wind measurement data.This research obtained the wind measurement data per 10 min of the anemometer tower of the wind farm in Huai'an for more than one year. In addition,it used the least square method, average wind speed, standard deviation estimation method, and maximum likelihood estimation to evaluate the recorded data of the time interval(30 min/1 h/2 h), solve wind speed probability distribution, conduct the productivity evaluation, and compare estimation errors of generating capacity in the different time interval. The result shows that the estimation error of the generating capacity enlarges with the time-scale increasing. The generating capacity calculated by the wind measurement data per 30 min is close to the computed result of the data per 10 min. All in all, the result has instructional functions when the wind measurement data of the wind farm has poor integrity.
    Key words: wind measurement data; time interval; Weibull parameter; productivity evaluation
 
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