本文選題：風(fēng)力發(fā)電 + 功率預(yù)測　； 參考：《昆明理工大學(xué)》2017年碩士論文

【摘要】：風(fēng)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(zhuǎn)變。我國約20%的國土都具有比較豐富的風(fēng)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(jìn)步和提升,風(fēng)電在我國有著巨大的發(fā)展?jié)摿�。但�?由于風(fēng)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(fēng)電功率預(yù)測的工作,才能有效的管理風(fēng)電場運行,保證電力系統(tǒng)的安全以及電能質(zhì)量。基于這個背景,本文以風(fēng)電場短期功率預(yù)測方法為研究內(nèi)容,通過神經(jīng)網(wǎng)絡(luò)預(yù)測的手段,對風(fēng)電場預(yù)測方法進(jìn)行研究和探討,論文的主要工作有如下幾個方面:首先,對風(fēng)電場功率預(yù)測方法進(jìn)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(luò)的方法預(yù)測風(fēng)電功率。在介紹了 BP神經(jīng)網(wǎng)絡(luò)原理的基礎(chǔ)上,詳細(xì)分析了影響風(fēng)電場輸出的因素,確定了以風(fēng)速、風(fēng)向正弦和余弦作為影響風(fēng)電輸出的最主要因素。其次,選定BP神經(jīng)網(wǎng)絡(luò)對風(fēng)電功率進(jìn)行預(yù)測,以某風(fēng)電場的歷史運行數(shù)據(jù)作為模型訓(xùn)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(yù)測的精度。結(jié)果表明,BP神經(jīng)網(wǎng)絡(luò)有著較好的預(yù)測表現(xiàn),但是不太穩(wěn)定。最后,為了進(jìn)一步提高預(yù)測精度,提出了以人工蜂群算法優(yōu)化的BP神經(jīng)網(wǎng)絡(luò)預(yù)測模型。以相同的樣本數(shù)據(jù)訓(xùn)練之后,選取同樣的典型測試樣本數(shù)據(jù)進(jìn)行預(yù)測精度的驗證。結(jié)果表明,該方法能大大減小BP神經(jīng)網(wǎng)絡(luò)的預(yù)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(xué)位授予單位】：昆明理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TM614;TP183

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本文編號：1892936