本文選題：風電功率 + 超短期�。� 參考：《東北電力大學》2017年碩士論文

【摘要】：風能是至關重要的低碳能源,有實現(xiàn)可持續(xù)能源供應的潛力,風力發(fā)電已成為各國重點發(fā)展的綠色能源之一。風電發(fā)展迅速,裝機容量逐年增加,預計到2020年,全球風力發(fā)電裝機容量將達到12億千瓦,能夠滿足世界電力總量12%的需求。近幾年我國風電年裝機容量成倍增長,至2014年底,中國累計風電裝機容量114609兆瓦,我國已成為世界裝機容量最大的國家。根據(jù)能源局在2011年發(fā)布的文件《風電廠功率預測預報管理暫行辦法》可知,實時預測是指自上報時刻起未來15分至4小時的預測預報,時間分辨率為15分鐘。故本課題研究的實時風電功率預測是以時間間隔為15分鐘的風電功率時間序列為主要研究對象,并對其進行滾動預測16步的超短期風電功率預測。以此得到的預測結果,可以服務于風電場機組實時有功出力的調整,對提高風能的利用率有重要意義。本課題從風電功率波動特性著手,首先閱讀國內外文獻,找到或定義刻畫風電功率波動特性的指標,分析風電功率波動的概率分布,分析風電功率波動的原因;閱讀國內外關于風電功率波動特性和風電功率預測方面的文獻,了解風電功率預測的研究進展,分析風電功率預測誤差的成因,介紹刻畫風電功率預測誤差的指標;研究國內外關于原子稀疏分解理論方面的文獻,將原子稀疏分解理論應用于風電功率時間序列的前期分解;在現(xiàn)有風電功率預測模型的基礎上,將原子稀疏分解理論組合現(xiàn)有預測模型應用于風電功率的超短期實時預測,并且分析新的組合預測模型對風電功率實時預測精度的影響;進行風電功率實時預測誤差分析,驗證新的組合預測模型的有效性;最后搭建基于VB編程語言的風電功率預測平臺。
[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.
【學位授予單位】：東北電力大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TM614

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本文編號：1957240