本文關(guān)鍵詞： 光伏 最大功率點(diǎn)跟蹤 RBF神經(jīng)網(wǎng)絡(luò) 遺傳算法　出處：《湖南工業(yè)大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著傳統(tǒng)燃料的日漸消耗與能源需求量的不斷提升,可再生能源逐漸受到關(guān)注。由于太陽(yáng)能具有綠色、安全、可再生等特點(diǎn),近年來,太陽(yáng)能光伏發(fā)電已經(jīng)在我國(guó)得到了飛速發(fā)展。但光伏電池具有生產(chǎn)成本高、光電轉(zhuǎn)換效率低的缺點(diǎn),因此如何使光伏電池持續(xù)有效地輸出最大功率以提高發(fā)電效率和降低發(fā)電成本則成為了當(dāng)下研究的重點(diǎn)。針對(duì)該問題,本文利用神經(jīng)網(wǎng)絡(luò)的非線性擬合能力以及遺傳算法突出的尋優(yōu)特點(diǎn),提出了遺傳算法優(yōu)化的RBF神經(jīng)網(wǎng)絡(luò)對(duì)光伏發(fā)電系統(tǒng)最大功率點(diǎn)進(jìn)行預(yù)測(cè)控制。首先,本文對(duì)光伏發(fā)電的研究背景及國(guó)內(nèi)外研究現(xiàn)狀進(jìn)行了綜述,介紹了目前光伏發(fā)電MPPT技術(shù)的判斷標(biāo)準(zhǔn)及不足。詳細(xì)說明了光伏電池的工作原理,通過MATLAB搭建光伏電池模型獲得了U-I及P-V動(dòng)態(tài)變化曲線,并在此基礎(chǔ)上得出光照強(qiáng)度和溫度為影響最大功率點(diǎn)輸出的主要因素。接著,闡述了光伏發(fā)電最大功率點(diǎn)跟蹤的原理,分析了傳統(tǒng)跟蹤方法及其改進(jìn)方法的優(yōu)缺點(diǎn)。針對(duì)傳統(tǒng)方法的不足,介紹了基于現(xiàn)代控制理論的神經(jīng)網(wǎng)絡(luò)控制法,通過RBF神經(jīng)網(wǎng)絡(luò)函數(shù)逼近能力的分析,選擇其對(duì)光伏發(fā)電最大功率點(diǎn)進(jìn)行預(yù)測(cè)控制。然后,對(duì)于RBF神經(jīng)網(wǎng)絡(luò)中存在的不足,本文使用了遺傳算法對(duì)其數(shù)據(jù)中心、擴(kuò)展常數(shù)及權(quán)值進(jìn)行優(yōu)化。通過將RBF神經(jīng)網(wǎng)絡(luò)的數(shù)據(jù)中心和其對(duì)應(yīng)的擴(kuò)展常數(shù)以及權(quán)值統(tǒng)一編碼,加強(qiáng)了隱含層和輸出層的合作關(guān)系,并利用遺傳算法全局搜索的功能特性,使得整個(gè)網(wǎng)絡(luò)模型達(dá)到全局最優(yōu)。此外,對(duì)遺傳算法本身的機(jī)制作出相應(yīng)的改進(jìn),使遺傳操作更加完善。最后,將遺傳算法優(yōu)化后的RBF神經(jīng)網(wǎng)絡(luò)與優(yōu)化前的網(wǎng)絡(luò)對(duì)同一光伏發(fā)電系統(tǒng)最大功率點(diǎn)進(jìn)行預(yù)測(cè),結(jié)果顯示優(yōu)化后的RBF神經(jīng)網(wǎng)絡(luò)達(dá)到目標(biāo)誤差的訓(xùn)練次數(shù)較優(yōu)化前明顯減少,平均誤差降低了3.7%,結(jié)果證明遺傳算法優(yōu)化后的RBF神經(jīng)網(wǎng)絡(luò)不僅提高了預(yù)測(cè)速度,還提高了預(yù)測(cè)精確度,從而能更好地實(shí)現(xiàn)光伏發(fā)電最大功率點(diǎn)跟蹤控制。
[Abstract]:With the increasing consumption of traditional fuels and increasing energy demand, renewable energy has attracted more and more attention. Due to the green, safe, renewable and other characteristics of solar energy, in recent years, Solar photovoltaic power generation has been developing rapidly in China, but photovoltaic cells have the disadvantages of high production cost and low photoelectric conversion efficiency. Therefore, how to make photovoltaic cells output maximum power continuously and effectively to improve generation efficiency and reduce generation cost has become the focus of current research. In this paper, based on the nonlinear fitting ability of neural network and the outstanding optimization characteristics of genetic algorithm, a genetic algorithm optimized RBF neural network is proposed to predict and control the maximum power point of photovoltaic power generation system. This paper summarizes the research background of photovoltaic power generation and the current research situation at home and abroad, introduces the judging standard and deficiency of photovoltaic generation MPPT technology at present, and explains the working principle of photovoltaic cell in detail. The dynamic curves of U-I and P-V are obtained by building photovoltaic cell model by MATLAB. On the basis of this, the main factors affecting the output of maximum power point are light intensity and temperature. Then, the principle of maximum power point tracking for photovoltaic generation is described. This paper analyzes the advantages and disadvantages of the traditional tracking method and its improvement, and introduces the neural network control method based on the modern control theory, which is based on the RBF neural network function approximation ability. Then, for the shortcomings of RBF neural network, the genetic algorithm is used to control the data center of photovoltaic power generation. By coding the data center of RBF neural network, its corresponding expansion constant and weights, the cooperative relationship between the hidden layer and the output layer is strengthened, and the global search function of genetic algorithm is used. In addition, the mechanism of genetic algorithm itself is improved accordingly to make genetic operation more perfect. Finally, The RBF neural network optimized by genetic algorithm and the network before optimization are used to predict the maximum power point of the same photovoltaic system. The results show that the training times of the optimized RBF neural network to achieve the target error are obviously reduced compared with those before optimization. The results show that the RBF neural network optimized by genetic algorithm not only improves the prediction speed, but also improves the accuracy of prediction, so that the maximum power point tracking control of photovoltaic generation can be realized better.
【學(xué)位授予單位】：湖南工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TM615;TP183

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本文編號(hào)：1494914