發(fā)布時(shí)間：2018-05-30 23:13

  本文選題：風(fēng)電功率 + 超短期��； 參考：《東北電力大學(xué)》2017年碩士論文

【摘要】：風(fēng)能是至關(guān)重要的低碳能源,有實(shí)現(xiàn)可持續(xù)能源供應(yīng)的潛力,風(fēng)力發(fā)電已成為各國(guó)重點(diǎn)發(fā)展的綠色能源之一。風(fēng)電發(fā)展迅速,裝機(jī)容量逐年增加,預(yù)計(jì)到2020年,全球風(fēng)力發(fā)電裝機(jī)容量將達(dá)到12億千瓦,能夠滿足世界電力總量12%的需求。近幾年我國(guó)風(fēng)電年裝機(jī)容量成倍增長(zhǎng),至2014年底,中國(guó)累計(jì)風(fēng)電裝機(jī)容量114609兆瓦,我國(guó)已成為世界裝機(jī)容量最大的國(guó)家。根據(jù)能源局在2011年發(fā)布的文件《風(fēng)電廠功率預(yù)測(cè)預(yù)報(bào)管理暫行辦法》可知,實(shí)時(shí)預(yù)測(cè)是指自上報(bào)時(shí)刻起未來15分至4小時(shí)的預(yù)測(cè)預(yù)報(bào),時(shí)間分辨率為15分鐘。故本課題研究的實(shí)時(shí)風(fēng)電功率預(yù)測(cè)是以時(shí)間間隔為15分鐘的風(fēng)電功率時(shí)間序列為主要研究對(duì)象,并對(duì)其進(jìn)行滾動(dòng)預(yù)測(cè)16步的超短期風(fēng)電功率預(yù)測(cè)。以此得到的預(yù)測(cè)結(jié)果,可以服務(wù)于風(fēng)電場(chǎng)機(jī)組實(shí)時(shí)有功出力的調(diào)整,對(duì)提高風(fēng)能的利用率有重要意義。本課題從風(fēng)電功率波動(dòng)特性著手,首先閱讀國(guó)內(nèi)外文獻(xiàn),找到或定義刻畫風(fēng)電功率波動(dòng)特性的指標(biāo),分析風(fēng)電功率波動(dòng)的概率分布,分析風(fēng)電功率波動(dòng)的原因;閱讀國(guó)內(nèi)外關(guān)于風(fēng)電功率波動(dòng)特性和風(fēng)電功率預(yù)測(cè)方面的文獻(xiàn),了解風(fēng)電功率預(yù)測(cè)的研究進(jìn)展,分析風(fēng)電功率預(yù)測(cè)誤差的成因,介紹刻畫風(fēng)電功率預(yù)測(cè)誤差的指標(biāo);研究國(guó)內(nèi)外關(guān)于原子稀疏分解理論方面的文獻(xiàn),將原子稀疏分解理論應(yīng)用于風(fēng)電功率時(shí)間序列的前期分解;在現(xiàn)有風(fēng)電功率預(yù)測(cè)模型的基礎(chǔ)上,將原子稀疏分解理論組合現(xiàn)有預(yù)測(cè)模型應(yīng)用于風(fēng)電功率的超短期實(shí)時(shí)預(yù)測(cè),并且分析新的組合預(yù)測(cè)模型對(duì)風(fēng)電功率實(shí)時(shí)預(yù)測(cè)精度的影響;進(jìn)行風(fēng)電功率實(shí)時(shí)預(yù)測(cè)誤差分析,驗(yàn)證新的組合預(yù)測(cè)模型的有效性;最后搭建基于VB編程語言的風(fēng)電功率預(yù)測(cè)平臺(tái)。
[Abstract]:Wind energy is a very important low-carbon energy, and has the potential to achieve sustainable energy supply. Wind power generation has become one of the key green energy. Wind power is developing rapidly and its installed capacity is increasing year by year. It is estimated that by 2020, the installed capacity of global wind power generation will reach 1.2 billion kilowatts, which can meet the demand of 12 percent of the world's total electricity. In recent years, the annual installed capacity of wind power in China has increased exponentially. By the end of 2014, the total installed capacity of wind power in China was 114609 MW, and China has become the largest country in the world. According to the document issued by the Energy Bureau in 2011, "interim measures for power forecasting and forecasting of wind power plants", real-time prediction refers to the forecast for the next 15 to 4 hours from the reporting moment, with a time resolution of 15 minutes. Therefore, the real time wind power prediction in this research is based on the wind power time series with a time interval of 15 minutes, and the ultra short term wind power prediction with 16 steps rolling prediction is carried out. The predicted results can be used to adjust the real time active power output of wind farm units, and it is of great significance to improve the utilization rate of wind energy. This topic starts with the characteristic of wind power fluctuation, first reads the domestic and foreign literature, finds out or defines the index to describe the characteristic of wind power fluctuation, analyzes the probability distribution of wind power fluctuation, and analyzes the reason of wind power fluctuation. This paper reads the literatures on wind power fluctuation characteristics and wind power prediction at home and abroad, understands the research progress of wind power prediction, analyzes the causes of wind power prediction errors, and introduces the indicators of wind power prediction errors. This paper studies the theory of atomic sparse decomposition at home and abroad, applies the theory of atomic sparse decomposition to the pre-decomposition of wind power time series, and based on the existing wind power prediction model, In this paper, the atomic sparse decomposition theory is applied to the ultra-short-term real-time wind power prediction, and the influence of the new combined forecasting model on the wind power real-time prediction accuracy is analyzed, and the error analysis of wind power real-time prediction is carried out. The validity of the new combined forecasting model is verified. Finally, the wind power prediction platform based on VB programming language is built.
【學(xué)位授予單位】：東北電力大學(xué)
【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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本文編號(hào)：1957240