本文選題：山地風(fēng)電 + 改進(jìn)應(yīng)用模式��； 參考：《中南大學(xué)》2014年碩士論文

【摘要】：：風(fēng)能資源是一種清潔可再生能源,開展風(fēng)資源利用對促進(jìn)社會經(jīng)濟(jì)可持續(xù)發(fā)展具有重要作用。某地區(qū)風(fēng)能資源有沒有利用價值,需要對該地區(qū)風(fēng)資源的儲量進(jìn)行一個科學(xué)的評估。風(fēng)況分析研究及風(fēng)資源的評估是風(fēng)機(jī)選型、布置的基礎(chǔ),對推動風(fēng)資源開發(fā)利用都具有重要的意義。 論文以內(nèi)陸中部地區(qū)復(fù)雜山地風(fēng)電場作為研究對象,利用風(fēng)電場區(qū)域內(nèi)已有一年測風(fēng)資料,針對復(fù)雜山地風(fēng)電場評估及風(fēng)機(jī)布置的局限性,采用模擬預(yù)估技術(shù),基于預(yù)估軟件評估的原理及山地應(yīng)用誤差原因分析,提出對測風(fēng)站點數(shù)據(jù)進(jìn)行估算模型適用訂正,旨在探討通過測風(fēng)資料的適用訂正來改進(jìn)該預(yù)估模式在復(fù)雜山地的應(yīng)用效果；并采用改進(jìn)粒子群優(yōu)化算法對風(fēng)機(jī)布置進(jìn)行了優(yōu)化研究。本文主要開展了如下研究： 1)基于完整測風(fēng)數(shù)據(jù),經(jīng)統(tǒng)計分析計算平均風(fēng)速、風(fēng)頻、風(fēng)能等風(fēng)況參數(shù)；并利用不同統(tǒng)計算法對Weibull分布參數(shù)進(jìn)行了估計。針對WAsP模式在復(fù)雜山地的預(yù)估結(jié)果超出實際值較大的局限性,論文以測風(fēng)站點的統(tǒng)計結(jié)果對其進(jìn)行修正處理,修正結(jié)果更加貼近實際風(fēng)況。 2)鑒于預(yù)估的修正風(fēng)速擬合成Weibull分布與WAsP內(nèi)定模式預(yù)估的Weibull分布特性相似,論文采用對測風(fēng)數(shù)據(jù)進(jìn)行WAsP估算模型的適用修正以改進(jìn)WAsP模式在復(fù)雜山地的應(yīng)用效果；并證明了該WAsP應(yīng)用改進(jìn)方式是可行的。 3)基于WAsP改進(jìn)模式結(jié)合SRTM地形高程數(shù)據(jù)繪制的高分辨率地形圖對所研究風(fēng)電場進(jìn)行風(fēng)資源評估,評價該風(fēng)電場具有風(fēng)資源開發(fā)價值。 4)基于評估結(jié)果對復(fù)雜山地49.5MW風(fēng)電場進(jìn)行風(fēng)機(jī)選型、風(fēng)機(jī)布置及發(fā)電量計算；風(fēng)機(jī)選型為Vestas V100/1.8MW,計算風(fēng)電場理論年發(fā)電量為129GWh；扣除尾流折減后電量為114GWh。并采用改進(jìn)粒子群優(yōu)化算法對風(fēng)機(jī)布置進(jìn)行了優(yōu)化。論文提出的WAsP改進(jìn)應(yīng)用方式為復(fù)雜山地風(fēng)資源評估提供了一個更精確的評估應(yīng)用模式；結(jié)合自主開發(fā)的優(yōu)化算法為復(fù)雜山地風(fēng)資源評估研究開發(fā)一套完整的評估系統(tǒng)奠定了一定的基礎(chǔ)。
[Abstract]:Wind energy resource is a kind of clean and renewable energy, and developing wind resource utilization plays an important role in promoting the sustainable development of social economy. It is necessary to make a scientific assessment of the reserves of wind resources in a certain area. Wind condition analysis and wind resource evaluation are the basis of wind turbine selection and layout, and are of great significance to promote the development and utilization of wind resources. The paper takes the wind farm in the middle of the inland area as the research object, using the wind data of wind farm area for one year, aiming at the limitation of wind farm evaluation and fan layout in the complex mountain area, adopting the simulation and prediction technology. Based on the principle of estimating software evaluation and the analysis of the cause of mountain application error, this paper puts forward the applicable revision of the estimation model for wind station data, in order to discuss how to improve the application effect of the prediction model in the complex mountain area through the application revision of wind data. An improved particle swarm optimization algorithm is used to optimize the fan layout. This paper mainly carried out the following research: 1) based on the complete wind data, the average wind speed, wind frequency and wind energy parameters are calculated by statistical analysis, and the Weibull distribution parameters are estimated by different statistical algorithms. In view of the limitation that the predicted results of WAsP model in complex mountainous areas exceed the actual values, the statistical results of wind survey stations are revised and processed in this paper, and the revised results are more close to the actual wind conditions. 2) in view of the fact that the predicted modified wind speed pseudo synthetic Weibull distribution is similar to the Weibull distribution predicted by WAsP internal model, this paper uses the WAsP estimation model to modify the wind data to improve the application effect of WAsP model in complex mountainous area. It is proved that the improved method of WAsP application is feasible. 3) High resolution topographic map based on WAsP improved mode and SRTM topographic elevation data is used to evaluate wind resources of the wind farm studied, and to evaluate the wind resource development value of the wind farm. 4) based on the evaluation results, the wind turbine selection, fan layout and power generation calculation of complex mountainous 49.5MW wind farm are carried out, the fan selection is Vestas V100- 1.8MWW, and the theoretical annual power generation of wind farm is 129GWh. after deducting the wake reduction, the electricity quantity is 114GWh. An improved particle swarm optimization algorithm is used to optimize the fan layout. The improved application mode of WAsP proposed in this paper provides a more accurate assessment model for the assessment of complex mountain wind resources. Combined with the self-developed optimization algorithm, it lays a foundation for the research and development of a complete evaluation system for the wind resources in complex mountain areas.
【學(xué)位授予單位】：中南大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TM614

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本文編號：1801464