發(fā)布時(shí)間：2018-05-29 22:02

  本文選題：風(fēng)電功率 + 超短期預(yù)測(cè)　； 參考：《東北電力大學(xué)》2017年碩士論文

【摘要】：在我國(guó),風(fēng)電是目前最有潛力的,可以大力發(fā)展的非水電可再生能源。但同時(shí)風(fēng)能的諸多自身特性,包括隨機(jī)性、不確定性等,使大規(guī)模風(fēng)電并網(wǎng)存在一些困難。為了實(shí)現(xiàn)大規(guī)模風(fēng)能的開(kāi)發(fā)利用,以超短期風(fēng)電功率預(yù)測(cè)為背景,吉林省多個(gè)風(fēng)電場(chǎng)的實(shí)測(cè)數(shù)據(jù)為基礎(chǔ),從風(fēng)電功率的數(shù)據(jù)補(bǔ)齊、多步滾動(dòng)的預(yù)測(cè)方法、風(fēng)電功率預(yù)測(cè)的不確定性分析以及預(yù)測(cè)誤差的非參數(shù)擬合四個(gè)方面進(jìn)行了全面的分析與研究。對(duì)于風(fēng)力發(fā)電的特性分析、功率預(yù)測(cè)、儲(chǔ)能配置等研究都需要在歷史數(shù)據(jù)的基礎(chǔ)上進(jìn)行展開(kāi),但實(shí)際中往往會(huì)由于各種原因?qū)е聰?shù)據(jù)不完整,缺失的數(shù)據(jù)可能會(huì)使系統(tǒng)變得混亂、難控制,或者存在越來(lái)越多的不確定性變化,這些情況都會(huì)對(duì)后續(xù)的分析估計(jì)造成很大的障礙�；谧畲笙嚓P(guān)最小冗余原則對(duì)風(fēng)電場(chǎng)風(fēng)電功率數(shù)據(jù)進(jìn)行補(bǔ)齊,首先分析得出與功率有關(guān)的變量,然后根據(jù)互信息理論,對(duì)變量通過(guò)最大相關(guān)最小冗余的原則進(jìn)行特征選取,挖掘特征與功率之間的聯(lián)系,最后根據(jù)這種聯(lián)系對(duì)功率數(shù)據(jù)進(jìn)行補(bǔ)齊。結(jié)果表明特征選取是對(duì)高維數(shù)據(jù)進(jìn)行降維的有效辦法,從原始特征集中選出特征子集,保留原始特征集的有效信息,從而補(bǔ)齊缺失的數(shù)據(jù)。風(fēng)電功率預(yù)測(cè)的準(zhǔn)確率越高,風(fēng)能的利用率越高,因此,需要確定合理有效的預(yù)測(cè)方法,建立多步滾動(dòng)的風(fēng)電功率預(yù)測(cè)模型。相關(guān)向量機(jī)(RVM)是一種稀疏概率模型的學(xué)習(xí)機(jī),具有很好的泛化學(xué)習(xí)能力,能有效地預(yù)測(cè)風(fēng)電功率并且運(yùn)行時(shí)間極快。同時(shí)引入集合經(jīng)驗(yàn)?zāi)B(tài)分解(EEMD),將功率數(shù)據(jù)的初始序列分解成若干組平穩(wěn)的序列,該方法可以顯著提高預(yù)測(cè)精度,縮短運(yùn)行時(shí)間。由于任何預(yù)測(cè)都具有不確定性,因此帶有置信區(qū)間的單點(diǎn)預(yù)測(cè)范圍可以降低電網(wǎng)和風(fēng)電場(chǎng)運(yùn)行的風(fēng)險(xiǎn),整個(gè)系統(tǒng)的運(yùn)行也就更安全穩(wěn)定。對(duì)風(fēng)電功率預(yù)測(cè)的不確定性進(jìn)行分析,可以把預(yù)測(cè)功率的單一值轉(zhuǎn)化成功率的估計(jì)區(qū)間。結(jié)果表明相關(guān)向量機(jī)的預(yù)測(cè)模型可以提供給定置信水平下的預(yù)測(cè)波動(dòng)范圍。對(duì)預(yù)測(cè)誤差進(jìn)行擬合分布評(píng)價(jià),通過(guò)對(duì)預(yù)測(cè)誤差的分布特征可以分析得出非參數(shù)估計(jì)與預(yù)測(cè)方法、預(yù)測(cè)時(shí)間間隔、預(yù)測(cè)誤差概率分布形態(tài)以及風(fēng)電場(chǎng)裝機(jī)容量的關(guān)系,從而使系統(tǒng)穩(wěn)定持續(xù)地運(yùn)行。結(jié)果表明非參數(shù)估計(jì)分布模型對(duì)不同規(guī)模的風(fēng)電場(chǎng)和不同條件的分布均能較好地?cái)M合,其中單峰的擬合效果更好。
[Abstract]:Wind power is the most potential non-hydropower renewable energy in China. But at the same time, wind energy has many characteristics, such as randomness and uncertainty, which makes large-scale wind power grid difficult. In order to realize the development and utilization of large-scale wind energy, based on the forecast of ultra-short-term wind power and the measured data of several wind farms in Jilin Province, the prediction method of wind power compensation and multi-step rolling is introduced. The uncertainty analysis of wind power prediction and the nonparametric fitting of prediction error are analyzed and studied comprehensively. For wind power generation characteristics analysis, power prediction, energy storage configuration and other studies need to be carried out on the basis of historical data, but in practice, due to various reasons, the data are often incomplete. The missing data may make the system chaotic, difficult to control, or there are more and more uncertain changes, which will cause great obstacles to the subsequent analysis and estimation. Based on the principle of maximum correlation and minimum redundancy, the wind power data of wind farm is compensated. Firstly, the variables related to power are analyzed, and then, according to the mutual information theory, the variables are selected by the principle of maximum correlation and minimum redundancy. The relation between feature and power is mined, and the power data is corrected according to this relation. The results show that feature selection is an effective method to reduce the dimension of high-dimensional data. The feature subset is selected from the original feature set, and the effective information of the original feature set is retained. The higher the accuracy of wind power prediction, the higher the utilization rate of wind energy. Therefore, it is necessary to determine a reasonable and effective forecasting method and to establish a multi-step rolling wind power prediction model. Correlation vector machine (RVM) is a kind of learning machine with sparse probability model. It has good generalization ability and can effectively predict wind power and run very fast. At the same time, the set empirical mode decomposition (EMD) is introduced to decompose the initial sequence of power data into a number of stationary sequences. This method can significantly improve the prediction accuracy and shorten the running time. Because of the uncertainty of any prediction, the single point prediction range with confidence interval can reduce the risk of power grid and wind farm operation, and the operation of the whole system is safer and more stable. By analyzing the uncertainty of wind power prediction, the single value of predicted power can be transformed into the estimated interval of success rate. The results show that the prediction model of correlation vector machine can provide the range of predicted fluctuations at a given confidence level. The relationship between nonparametric estimation and prediction method, prediction time interval, probability distribution pattern of prediction error and installed capacity of wind farm can be obtained by analyzing the distribution characteristics of prediction error. Thus, the system runs steadily and continuously. The results show that the non-parametric distribution model can fit the distribution of wind farms of different scale and different conditions, and the fitting effect of single peak is better.
【學(xué)位授予單位】：東北電力大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TM614

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本文編號(hào)：1952593