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

【摘要】：隨著人類對(duì)生態(tài)環(huán)境的逐漸重視以及在國(guó)家新能源戰(zhàn)略的重大部署下,風(fēng)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(fēng)電裝機(jī)容量逐年上升,同時(shí)也使得風(fēng)力發(fā)電技術(shù)越來(lái)越成熟。但是風(fēng)的間歇性、波動(dòng)性使得風(fēng)能具有不可控性,始終是風(fēng)電技術(shù)的一大難題。因此,解決能源問(wèn)題以及風(fēng)電場(chǎng)長(zhǎng)期穩(wěn)定運(yùn)行的關(guān)鍵所在是迫切需要提高風(fēng)電功率的預(yù)測(cè)精度。本文以風(fēng)電功率預(yù)測(cè)為研究對(duì)象,對(duì)測(cè)風(fēng)塔數(shù)據(jù)進(jìn)行分析,以便發(fā)掘和更好的利用測(cè)風(fēng)數(shù)據(jù)自身的信息。神經(jīng)網(wǎng)絡(luò)模型的建立對(duì)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(qiáng)的泛化能力;同時(shí)又要保證模型具有更高的預(yù)測(cè)精度。針對(duì)特殊天氣下普通預(yù)測(cè)模型難以滿足要求的問(wèn)題,本文采用案例推理技術(shù)對(duì)風(fēng)電功率預(yù)測(cè)模型進(jìn)行改進(jìn)。本文主要研究?jī)?nèi)容有:(1)對(duì)國(guó)內(nèi)外風(fēng)力發(fā)電的現(xiàn)狀、發(fā)展趨勢(shì)以及目前主要存在的問(wèn)題進(jìn)行了總結(jié),引出了風(fēng)電功率預(yù)測(cè)的意義及必要性,同時(shí)對(duì)風(fēng)電功率預(yù)測(cè)模型的研究現(xiàn)狀進(jìn)行總結(jié)分析。(2)針對(duì)測(cè)風(fēng)數(shù)據(jù)不能很好地滿足預(yù)測(cè)樣本要求的問(wèn)題,根據(jù)國(guó)標(biāo)GB/T18709-2002及相關(guān)參考文獻(xiàn)補(bǔ)充對(duì)數(shù)據(jù)進(jìn)行了檢驗(yàn)、填補(bǔ)和修正。采用山西省某風(fēng)電場(chǎng)的實(shí)測(cè)數(shù)據(jù)分析了測(cè)風(fēng)數(shù)據(jù)自身的變化規(guī)律,重點(diǎn)分析了模型的預(yù)測(cè)誤差與風(fēng)電功率影響因素之間的關(guān)系。對(duì)預(yù)測(cè)模型評(píng)價(jià)指標(biāo)進(jìn)行了必要的選取說(shuō)明。(3)本文提出了基于模糊聚類分析的風(fēng)電功率預(yù)測(cè)方法。首先采用減法聚類來(lái)確定模糊C均值的聚類數(shù)和聚類中心,采用風(fēng)電場(chǎng)實(shí)際運(yùn)行的數(shù)據(jù)聚類分析,然后進(jìn)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(duì)性,因此保證網(wǎng)絡(luò)模型的泛化能力的同時(shí)又提高了預(yù)測(cè)的精度。(4)為了提高特殊天氣下風(fēng)電功率的預(yù)測(cè)精度,本文通過(guò)對(duì)普通模型的預(yù)測(cè)效果和風(fēng)電場(chǎng)實(shí)際測(cè)風(fēng)數(shù)據(jù)進(jìn)行分析,建立了基于案例推理的特殊天氣下風(fēng)電功率預(yù)測(cè)模型,采用基于模糊聚類和粒子群優(yōu)化的K近鄰算法進(jìn)行案例檢索,提高了檢索的速度和精度。采用山西省某風(fēng)電場(chǎng)的實(shí)際數(shù)據(jù)進(jìn)行了大量的仿真實(shí)驗(yàn),將仿真結(jié)果與GRNN、LSSVM、GABP模型進(jìn)行了對(duì)比,預(yù)測(cè)誤差有了不同程度的改善。特別是預(yù)測(cè)數(shù)據(jù)發(fā)生突變時(shí),效果更為明顯,從而驗(yàn)證了該方法的有效性,為解決風(fēng)電場(chǎng)特殊天氣下的風(fēng)電功率預(yù)測(cè)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(xué)位授予單位】：太原理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TM614;TP311.13

【參考文獻(xiàn)】

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本文編號(hào)：1601691