本文選題：區(qū)間二型模糊邏輯系統(tǒng)　切入點(diǎn)：主成分分析　出處：《蘭州交通大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著全球能源需求的日益增高和化石燃料的急劇下降,風(fēng)力發(fā)電技術(shù)在能源領(lǐng)域得到了高度重視,其中,準(zhǔn)確的風(fēng)電功率預(yù)測(cè)是風(fēng)力發(fā)電大規(guī)模開發(fā)利用的有效手段之一。精確、可靠的風(fēng)電功率預(yù)測(cè)對(duì)于優(yōu)化電網(wǎng)運(yùn)行的成本和改進(jìn)電力系統(tǒng)的可靠性極其重要,其中,短期風(fēng)電功率預(yù)測(cè)對(duì)電力系統(tǒng)安全穩(wěn)定的運(yùn)行、電網(wǎng)的調(diào)度以及提前安排風(fēng)電機(jī)組的維護(hù)有著重要的意義,是新能源領(lǐng)域非常重要的研究方向之一。目前的風(fēng)電功率的預(yù)測(cè)方法通常可分為物理方法、統(tǒng)計(jì)方法、空間相關(guān)性預(yù)測(cè)方法、組合預(yù)測(cè)方法等。更先進(jìn)的現(xiàn)代統(tǒng)計(jì)方法,如神經(jīng)網(wǎng)絡(luò)、SVM等,能從過(guò)去的時(shí)間序列中描述出輸入與輸出的非線性聯(lián)系,已在風(fēng)電功率超短期或短期預(yù)測(cè)中取得成功的應(yīng)用。二型FLS(Fuzzy Logic Systems,模糊邏輯系統(tǒng))作為一種強(qiáng)有力的時(shí)間序列建模方法,已被成功應(yīng)用于混沌時(shí)間序列預(yù)測(cè)、風(fēng)速預(yù)測(cè)、電力負(fù)荷預(yù)測(cè)、交通流預(yù)測(cè)中,具有很好的應(yīng)用潛力。考慮到風(fēng)電功率數(shù)據(jù)的隨機(jī)性與間歇性,以及區(qū)間二型FLS方法在預(yù)測(cè)領(lǐng)域中的成功應(yīng)用,它理應(yīng)是風(fēng)電功率預(yù)測(cè)的有力工具之一。進(jìn)一步,在二型FLS的基礎(chǔ)上,通過(guò)PCA(Principal Component Analysis,主成分分析)對(duì)輸入降維,從而達(dá)到避免“規(guī)則爆炸”的難題。本文主要研究?jī)?nèi)容如下:(1)研究PCA、二型模糊集的基本原理與算法實(shí)現(xiàn),同時(shí)進(jìn)一步研究二型FLS的組成以及各個(gè)組成部分的算法實(shí)現(xiàn)。(2)研究區(qū)間二型模糊邏輯系統(tǒng)的建模問(wèn)題,基于BP算法進(jìn)行參數(shù)的調(diào)整,并應(yīng)用SVD-QR算法進(jìn)行一定程度的規(guī)則約簡(jiǎn)。建立了二型非單值區(qū)間二型FLS的多步預(yù)測(cè)模型,并且通過(guò)提前20、40及60min的風(fēng)電功率預(yù)測(cè)證明了方法的可行性與有效性。(3)考慮二型FLS的“規(guī)則爆炸”問(wèn)題,再通過(guò)對(duì)PCA方法的研究,將其結(jié)合二型FLS,提出基于PCA方法與一型非單值區(qū)間二型FLS及PCA方法與二型非單值區(qū)間二型FLS相結(jié)合的預(yù)測(cè)方法。(4)為了驗(yàn)證所提出方法的有效性,將本文的不同方法應(yīng)用于不同地區(qū)短期風(fēng)電功率例中,在同等條件下,可看出本文方法的預(yù)測(cè)精度高于支持向量機(jī)和一型模糊邏輯方法。同時(shí),模型的模糊規(guī)則數(shù)少,較好地解決了模糊模型的規(guī)則“爆炸”問(wèn)題,這使得PCA-區(qū)間二型FLS方法在風(fēng)電功率預(yù)測(cè)領(lǐng)域具有很好的應(yīng)用潛力。
[Abstract]:With the increasing global energy demand and the sharp decline of fossil fuels, wind power technology has received great attention in the field of energy, among which, Accurate wind power prediction is one of the effective methods for large-scale development and utilization of wind power generation. Accurate and reliable wind power prediction is very important for optimizing the operation cost of power network and improving the reliability of power system. Short-term wind power prediction is of great significance to the safe and stable operation of power system, the dispatch of power grid and the arrangement of maintenance of wind turbine units in advance. It is one of the most important research directions in the field of new energy. The current wind power prediction methods are usually divided into physical methods, statistical methods, spatial correlation prediction methods, combined forecasting methods, and more advanced modern statistical methods. Neural networks such as SVM can describe the nonlinear relationship between input and output from past time series. FLS(Fuzzy Logic systems (fuzzy logic system), as a powerful modeling method of time series, has been successfully applied to the prediction of chaotic time series and wind speed. Power load forecasting and traffic flow forecasting have good application potential. Considering the randomness and intermittency of wind power data, and the successful application of interval type 2 FLS method in forecasting field, It is supposed to be one of the powerful tools for wind power prediction. Further, on the basis of type 2 FLS, the input dimension is reduced by PCA(Principal Component Analysis (PCA). In order to avoid the problem of "rule explosion", the main contents of this paper are as follows: 1) the basic principle and algorithm realization of PCA, type 2 fuzzy set are studied. At the same time, the composition of type 2 FLS and the algorithm realization of each component are studied. (2) the modeling problem of interval type 2 fuzzy logic system is studied, and the parameters are adjusted based on BP algorithm. The SVD-QR algorithm is used to reduce the rules to a certain extent. The multistep prediction model of the second type non-single-valued interval second-type FLS is established. The feasibility and effectiveness of the method are proved by the wind power prediction of 20 ~ 40 and 60 minutes in advance. The "regular explosion" problem of type 2 FLS is considered, and then the study of PCA method is carried out. In order to verify the effectiveness of the proposed method, a prediction method based on the combination of PCA method and non-single-valued interval second-type FLS and PCA method and second-type non-single-valued interval second-type FLS is proposed. The different methods in this paper are applied to short-term wind power examples in different regions. Under the same conditions, it can be seen that the prediction accuracy of this method is higher than that of support vector machine and fuzzy logic method. At the same time, there are few fuzzy rules in the model. The problem of regular "explosion" of fuzzy model is solved well, which makes PCA-interval type 2 FLS method have good application potential in wind power prediction field.
【學(xué)位授予單位】：蘭州交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TM614

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