本文選題：風力發(fā)電 + 功率預測��； 參考：《昆明理工大學》2017年碩士論文

【摘要】：風能作為一種綠色清潔的能源,以其成本低廉,便于開發(fā)利用的優(yōu)勢,開始從補充能源向戰(zhàn)略替代能源轉變。我國約20%的國土都具有比較豐富的風能資源,無論是發(fā)展規(guī)模還是發(fā)展水平都有很大的進步和提升,風電在我國有著巨大的發(fā)展?jié)摿�。但�?由于風能具有隨機性和間歇性的特點,造成了其功率輸出的不穩(wěn)定,也給電力系統(tǒng)的正常穩(wěn)定運行帶來了挑戰(zhàn)。因此,只有做好風電功率預測的工作,才能有效的管理風電場運行,保證電力系統(tǒng)的安全以及電能質量。基于這個背景,本文以風電場短期功率預測方法為研究內容,通過神經(jīng)網(wǎng)絡預測的手段,對風電場預測方法進行研究和探討,論文的主要工作有如下幾個方面:首先,對風電場功率預測方法進行分類,在綜合比較現(xiàn)存各種方法后,本文決定采用BP神經(jīng)網(wǎng)絡的方法預測風電功率。在介紹了 BP神經(jīng)網(wǎng)絡原理的基礎上,詳細分析了影響風電場輸出的因素,確定了以風速、風向正弦和余弦作為影響風電輸出的最主要因素。其次,選定BP神經(jīng)網(wǎng)絡對風電功率進行預測,以某風電場的歷史運行數(shù)據(jù)作為模型訓練數(shù)據(jù)的來源,接著選取典型測試樣本數(shù)據(jù)來驗證預測的精度。結果表明,BP神經(jīng)網(wǎng)絡有著較好的預測表現(xiàn),但是不太穩(wěn)定。最后,為了進一步提高預測精度,提出了以人工蜂群算法優(yōu)化的BP神經(jīng)網(wǎng)絡預測模型。以相同的樣本數(shù)據(jù)訓練之后,選取同樣的典型測試樣本數(shù)據(jù)進行預測精度的驗證。結果表明,該方法能大大減小BP神經(jīng)網(wǎng)絡的預測誤差。
[Abstract]:Wind energy as a kind of green and clean energy, with its advantages of low cost and easy development and utilization, began to change from supplementary energy to strategic alternative energy. About 20% of our country has abundant wind energy resources, both the development scale and the development level have great progress and promotion, wind power in China has a great potential for development. However, due to the randomness and intermittency of wind energy, the instability of power output and the challenge to the normal and stable operation of power system are brought about. Therefore, the wind power prediction can effectively manage the operation of the wind farm and ensure the safety and power quality of the power system. Based on this background, this paper takes the short-term power forecasting method of wind farm as the research content, through the means of neural network forecast, carries on the research and the discussion to the wind farm forecast method. The main work of the paper has the following aspects: first, The methods of wind farm power prediction are classified. After a comprehensive comparison of the existing methods, this paper decides to use BP neural network to predict wind power. On the basis of introducing the principle of BP neural network, the factors influencing wind farm output are analyzed in detail, and the wind speed, wind direction sinusoidal and cosine are determined as the most important factors affecting wind power output. Secondly, BP neural network is selected to predict wind power, and the historical operation data of a wind farm is used as the source of model training data. Then, typical test sample data are selected to verify the prediction accuracy. The results show that the BP neural network has a good prediction performance, but is not very stable. Finally, in order to further improve the prediction accuracy, a BP neural network prediction model optimized by artificial bee colony algorithm is proposed. After training with the same sample data, the prediction accuracy is verified by selecting the same typical test sample data. The results show that this method can greatly reduce the prediction error of BP neural network.
【學位授予單位】：昆明理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TM614;TP183

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本文編號：1892936