【摘要】：對(duì)化石能源的不斷開(kāi)采和使用,對(duì)生態(tài)環(huán)境造成了嚴(yán)重的影響,為了更好地保護(hù)地球的生態(tài)環(huán)境,利用綠色能源和可再生能源以實(shí)現(xiàn)可持續(xù)發(fā)展迫在眉睫。太陽(yáng)能是一種備受人們親睞的清潔能源,有著廣闊的發(fā)展前景。光伏發(fā)電作為目前開(kāi)發(fā)太陽(yáng)能的主要方式,具有安全可靠、應(yīng)用形式靈活、安裝維護(hù)簡(jiǎn)單等優(yōu)勢(shì)。但是光伏發(fā)電存在電能轉(zhuǎn)化率低、成本高等問(wèn)題,目前廣泛使用的最大功率跟蹤控制算法(MPPT),是提高其轉(zhuǎn)換效率的重要手段。首先介紹了光伏發(fā)電輸出預(yù)測(cè)的研究背景、意義以及預(yù)測(cè)中所采用的方法和技術(shù)。然后分析了光伏電池輸出特性,引出MPPT的必要性和重要性。接著介紹了一種新興群智能算法-智能水滴算法(Intelligent Water Drops,IWD),該算法最初是針對(duì)離散優(yōu)化問(wèn)題提出來(lái)的,而對(duì)SVM參數(shù)的優(yōu)化是連續(xù)優(yōu)化問(wèn)題,同時(shí)傳統(tǒng)智能水滴算法在結(jié)點(diǎn)的選擇上易出現(xiàn)停滯和阻塞現(xiàn)象。針對(duì)以上問(wèn)題對(duì)傳統(tǒng)智能水滴算法提出改進(jìn),然后利用傳統(tǒng)標(biāo)準(zhǔn)算法優(yōu)化測(cè)試函數(shù)對(duì)改進(jìn)的智能水滴進(jìn)行測(cè)試,并將結(jié)果同傳統(tǒng)蟻群算法、標(biāo)準(zhǔn)智能水滴算法進(jìn)行對(duì)比,實(shí)驗(yàn)結(jié)果證明,本文提出的改進(jìn)智能算法用于函數(shù)優(yōu)化問(wèn)題,具有良好的收斂性和準(zhǔn)確性。其次介紹了支持向量機(jī)算法(SVM)基本原理,提出利用智能水滴算法優(yōu)化支持向量機(jī)參數(shù)。然后利用UCI網(wǎng)站提供的標(biāo)準(zhǔn)測(cè)試集進(jìn)行相應(yīng)性能測(cè)試,并將實(shí)驗(yàn)結(jié)果與傳統(tǒng)優(yōu)化算法尋優(yōu)結(jié)果進(jìn)行比較,實(shí)驗(yàn)結(jié)果證明利用改進(jìn)的智能水滴算法優(yōu)化支持向量機(jī)能獲得更高的回歸預(yù)測(cè)精度。最后以某光伏電站為研究對(duì)象,利用光伏電站歷史數(shù)據(jù)進(jìn)行實(shí)驗(yàn),對(duì)遺傳算法、粒子群算法和智能水滴算法的參數(shù)尋優(yōu)結(jié)果進(jìn)行了對(duì)比分析,給出了在matlab/simulink環(huán)境下搭建的基于IWD-SVM的光伏MPPT的仿真模型。結(jié)果表明了本文給出的利用智能水滴優(yōu)化SVM模型在光伏系統(tǒng)最大功率跟蹤中的可行性。
文內(nèi)圖片：
圖片說(shuō)明：光伏電池仿真模型
[Abstract]:The continuous exploitation and use of fossil energy has a serious impact on the ecological environment. In order to better protect the ecological environment of the earth, it is urgent to use green energy and renewable energy to achieve sustainable development. Solar energy is a kind of clean energy which is favored by people and has a broad development prospect. Photovoltaic power generation, as the main way to develop solar energy, has the advantages of safety and reliability, flexible application form, simple installation and maintenance, and so on. However, there are some problems in photovoltaic power generation, such as low power conversion rate and high cost. The maximum power tracking control algorithm (MPPT), which is widely used at present, is an important means to improve its conversion efficiency. Firstly, the research background, significance and methods and techniques of photovoltaic power generation output prediction are introduced. Then the output characteristics of photovoltaic cells are analyzed, and the necessity and importance of MPPT are introduced. Then, a new swarm intelligent algorithm, intelligent water drop algorithm (Intelligent Water Drops,IWD), is introduced, which is originally proposed for discrete optimization problem, and the optimization of SVM parameters is a continuous optimization problem. At the same time, the traditional intelligent water drop algorithm is prone to stagnation and blocking in node selection. In view of the above problems, the traditional intelligent water drop algorithm is improved, and then the improved intelligent water droplet algorithm is tested by using the traditional standard algorithm to optimize the test function, and the results are compared with the traditional ant colony algorithm and the standard intelligent water drop algorithm. The experimental results show that the improved intelligent algorithm proposed in this paper has good convergence and accuracy in the function optimization problem. Secondly, the basic principle of support vector machine (SVM) algorithm is introduced, and the intelligent water drop algorithm is proposed to optimize the parameters of support vector machine (SVM). Then the corresponding performance test is carried out by using the standard test set provided by UCI website, and the experimental results are compared with those of the traditional optimization algorithm. The experimental results show that the improved intelligent water drop algorithm is used to optimize the support vector function to obtain higher regression prediction accuracy. Finally, taking a photovoltaic power station as the research object, the parameter optimization results of genetic algorithm, particle swarm optimization algorithm and intelligent water drop algorithm are compared and analyzed by using the historical data of photovoltaic power station, and the simulation model of photovoltaic MPPT based on IWD-SVM in matlab/simulink environment is given. The results show the feasibility of using intelligent water droplets to optimize SVM model in maximum power tracking of photovoltaic system.
【學(xué)位授予單位】：華北電力大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP18;TM615

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