本文選題：風(fēng)速概率分布 + 威布爾分布��； 參考：《華北電力大學(xué)(北京)》2017年碩士論文

【摘要】：風(fēng)能受到風(fēng)速變化的影響,具有很大的隨機(jī)性、間歇性和不可控性,因此,研究風(fēng)速分布的概率、選擇合適的風(fēng)速概率分布模型、準(zhǔn)確估計(jì)模型參數(shù)對(duì)風(fēng)資源的合理利用有重大意義。本文圍繞風(fēng)速的概率分布模型和模型參數(shù)的估計(jì)算法來(lái)展開研究。作為一種常用的風(fēng)速概率分布模型,兩參數(shù)威布爾分布在概率密度較低的風(fēng)速帶的估計(jì)精度不高,因此,本文提出將三參數(shù)威布爾分布作為一種風(fēng)速概率分布的新模型;運(yùn)用灰色估計(jì)法、極大似然估計(jì)法、線性回歸估計(jì)法和矩估計(jì)法對(duì)三參數(shù)威布爾分布的模型參數(shù)進(jìn)行估計(jì),比較四種算法在不同樣本下的優(yōu)劣,確定合適的算法。針對(duì)云南兩個(gè)風(fēng)電場(chǎng)不同季度的風(fēng)速數(shù)據(jù),將三參數(shù)威布爾分布作為當(dāng)?shù)仫L(fēng)速概率分布的模型,運(yùn)用上述四種算法對(duì)模型的參數(shù)進(jìn)行估計(jì),繪制風(fēng)速概率分布擬合圖,計(jì)算模型的擬合優(yōu)度;針對(duì)兩個(gè)風(fēng)電場(chǎng)的全年風(fēng)速的實(shí)測(cè)數(shù)據(jù),分別將兩參數(shù)威布爾分布和三參數(shù)威布爾分布作為當(dāng)?shù)仫L(fēng)速概率分布的模型,估計(jì)出兩種模型的參數(shù),比較兩種模型在不同風(fēng)速帶的估計(jì)精度;通過(guò)對(duì)兩個(gè)風(fēng)電場(chǎng)實(shí)測(cè)風(fēng)速數(shù)據(jù)的驗(yàn)證,本文得到以下結(jié)論:1、當(dāng)采用灰色估計(jì)法和極大似然估計(jì)法時(shí),三參數(shù)威布爾分布擬合實(shí)測(cè)風(fēng)速概率分布是合理的;2、在概率較低的風(fēng)速帶,三參數(shù)威布爾分布比兩參數(shù)威布爾分布的估計(jì)精度更高,能更好地反映當(dāng)?shù)氐娘L(fēng)資源分布情況。
[Abstract]:Wind energy is influenced by wind speed change, which is random, intermittent and uncontrollable. Therefore, the probability of wind speed distribution is studied and the appropriate wind speed probability distribution model is selected. Accurate estimation of model parameters is of great significance to the rational utilization of wind resources. This paper focuses on the probability distribution model of wind speed and the estimation algorithm of model parameters. As a commonly used wind speed probability distribution model, the estimation accuracy of two-parameter Weibull distribution in the wind speed band with low probability density is not high. Therefore, the three-parameter Weibull distribution is proposed as a new model of wind speed probability distribution. The grey estimation method, maximum likelihood estimation method, linear regression estimation method and moment estimation method are used to estimate the model parameters of the three-parameter Weibull distribution. The advantages and disadvantages of the four algorithms in different samples are compared and the appropriate algorithm is determined. According to the wind speed data of two wind farms in different seasons in Yunnan, the three-parameter Weibull distribution is taken as the model of local wind speed probability distribution, and the parameters of the model are estimated by the above four algorithms, and the fitting map of wind speed probability distribution is drawn. The two parameter Weibull distribution and three parameter Weibull distribution are used as the models of local wind speed probability distribution, respectively, and the parameters of the two models are estimated according to the measured wind speed data of two wind farms. The estimation accuracy of the two models in different wind speed bands is compared, and by verifying the wind speed data measured in two wind farms, this paper obtains the following conclusion: 1, when the grey estimation method and the maximum likelihood estimation method are used, It is reasonable to fit the measured wind speed probability distribution with three parameter Weibull distribution. In the wind speed belt with lower probability, the three parameter Weibull distribution is more accurate than the two parameter Weibull distribution, which can better reflect the local wind resource distribution.
【學(xué)位授予單位】：華北電力大學(xué)(北京)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TM614

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