本文選題：風電功率預(yù)測　切入點：模糊C均值聚類　出處：《太原理工大學》2017年碩士論文　論文類型：學位論文

【摘要】：隨著人類對生態(tài)環(huán)境的逐漸重視以及在國家新能源戰(zhàn)略的重大部署下,風能作為一種清潔能源得到了大規(guī)模的發(fā)展,不僅全球的風電裝機容量逐年上升,同時也使得風力發(fā)電技術(shù)越來越成熟。但是風的間歇性、波動性使得風能具有不可控性,始終是風電技術(shù)的一大難題。因此,解決能源問題以及風電場長期穩(wěn)定運行的關(guān)鍵所在是迫切需要提高風電功率的預(yù)測精度。本文以風電功率預(yù)測為研究對象,對測風塔數(shù)據(jù)進行分析,以便發(fā)掘和更好的利用測風數(shù)據(jù)自身的信息。神經(jīng)網(wǎng)絡(luò)模型的建立對訓(xùn)練數(shù)據(jù)具有較高的依賴性,訓(xùn)練數(shù)據(jù)的選擇既要包含足夠廣的選擇范圍,這樣模型具有更強的泛化能力;同時又要保證模型具有更高的預(yù)測精度。針對特殊天氣下普通預(yù)測模型難以滿足要求的問題,本文采用案例推理技術(shù)對風電功率預(yù)測模型進行改進。本文主要研究內(nèi)容有:(1)對國內(nèi)外風力發(fā)電的現(xiàn)狀、發(fā)展趨勢以及目前主要存在的問題進行了總結(jié),引出了風電功率預(yù)測的意義及必要性,同時對風電功率預(yù)測模型的研究現(xiàn)狀進行總結(jié)分析。(2)針對測風數(shù)據(jù)不能很好地滿足預(yù)測樣本要求的問題,根據(jù)國標GB/T18709-2002及相關(guān)參考文獻補充對數(shù)據(jù)進行了檢驗、填補和修正。采用山西省某風電場的實測數(shù)據(jù)分析了測風數(shù)據(jù)自身的變化規(guī)律,重點分析了模型的預(yù)測誤差與風電功率影響因素之間的關(guān)系。對預(yù)測模型評價指標進行了必要的選取說明。(3)本文提出了基于模糊聚類分析的風電功率預(yù)測方法。首先采用減法聚類來確定模糊C均值的聚類數(shù)和聚類中心,采用風電場實際運行的數(shù)據(jù)聚類分析,然后進行神經(jīng)網(wǎng)絡(luò)模型的訓(xùn)練,既充分考慮了樣本空間的特征,又使得模型具有針對性,因此保證網(wǎng)絡(luò)模型的泛化能力的同時又提高了預(yù)測的精度。(4)為了提高特殊天氣下風電功率的預(yù)測精度,本文通過對普通模型的預(yù)測效果和風電場實際測風數(shù)據(jù)進行分析,建立了基于案例推理的特殊天氣下風電功率預(yù)測模型,采用基于模糊聚類和粒子群優(yōu)化的K近鄰算法進行案例檢索,提高了檢索的速度和精度。采用山西省某風電場的實際數(shù)據(jù)進行了大量的仿真實驗,將仿真結(jié)果與GRNN、LSSVM、GABP模型進行了對比,預(yù)測誤差有了不同程度的改善。特別是預(yù)測數(shù)據(jù)發(fā)生突變時,效果更為明顯,從而驗證了該方法的有效性,為解決風電場特殊天氣下的風電功率預(yù)測提供了一種可行性方法。
[Abstract]:With the gradual attention of human beings to the ecological environment and the important deployment of national new energy strategy, wind energy as a clean energy has been developed on a large scale, not only the global wind power installed capacity is increasing year by year. At the same time, it also makes wind power technology more and more mature. However, the intermittent and volatility of wind makes wind energy uncontrollable, which is always a big problem in wind power technology. The key to solve the problem of energy and the long-term stable operation of wind farm is to improve the accuracy of wind power prediction. The establishment of neural network model is highly dependent on the training data, the selection of training data should include a wide range of selection, so the model has a stronger generalization ability; At the same time, it is necessary to ensure that the model has higher prediction accuracy. In view of the problem that the ordinary prediction model is difficult to meet the requirements under special weather, This paper uses case-based reasoning technology to improve the prediction model of wind power. The main research content of this paper is to summarize the current situation, development trend and existing problems of wind power generation at home and abroad. The significance and necessity of wind power prediction are introduced. At the same time, the research status of wind power prediction model is summarized and analyzed. According to the national standard GB/T18709-2002 and related reference supplement, the data are checked, filled and corrected. The variation law of wind measurement data itself is analyzed by using the measured data of a wind farm in Shanxi Province. The relationship between the prediction error of the model and the influence factors of the wind power is analyzed. The necessary selection of the evaluation index of the prediction model is given.) in this paper, a forecasting method of wind power based on fuzzy clustering analysis is proposed. First, subtraction clustering is used to determine the clustering number and center of fuzzy C-means. The data clustering analysis of the actual operation of wind farm is adopted, and then the neural network model is trained, which not only fully considers the characteristics of the sample space, but also makes the model have pertinence. In order to improve the prediction accuracy of wind power under special weather, this paper analyzes the prediction effect of the common model and the actual wind data of wind farm. In this paper, a case-based reasoning (CBR) model for predicting wind power in special weather is established, and the K-nearest neighbor algorithm based on fuzzy clustering and particle swarm optimization (PSO) is used for case retrieval. The speed and precision of retrieval are improved. A large number of simulation experiments are carried out using the actual data of a wind farm in Shanxi Province, and the simulation results are compared with the GRNNX LSSVMU GABP model. The prediction error has been improved in different degrees, especially when the prediction data is abrupt, the effect is more obvious, which verifies the effectiveness of the method, and provides a feasible method for wind power prediction under special weather conditions.
【學位授予單位】：太原理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TM614;TP311.13

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本文編號：1601691