發(fā)布時(shí)間：2019-01-24 21:53

【摘要】：能源不僅是當(dāng)今社會(huì)發(fā)展的基礎(chǔ),也是世界各國(guó)經(jīng)濟(jì)建設(shè)的動(dòng)力。太陽能特有的零污染和取之不盡的特點(diǎn)使之成為世界公認(rèn)的最理想的清潔能源,而太陽能被用來發(fā)電是目前最主要的用途之一。開展對(duì)光伏發(fā)電系統(tǒng)的研究不僅對(duì)能源的可持續(xù)利用、環(huán)境保護(hù)提供了有效解決方案,而且對(duì)社會(huì)的穩(wěn)定發(fā)展具有重要的戰(zhàn)略意義。利用最大功率跟蹤控制技術(shù)是提高光伏發(fā)電系統(tǒng)效率的有效方式,對(duì)光伏發(fā)電行業(yè)有重要影響,同時(shí)也是這一領(lǐng)域的研究熱點(diǎn)。本文以光伏發(fā)電系統(tǒng)為研究對(duì)象,針對(duì)最大功率跟蹤問題展開研究,主要內(nèi)容有:1.根據(jù)光伏電池的等效電路推導(dǎo)了其數(shù)學(xué)模型,利用Matlab/Simulink搭建光伏電池模型以及整個(gè)光伏發(fā)電系統(tǒng)的仿真平臺(tái)。利用搭建的仿真平臺(tái)對(duì)光伏電池的V-I輸出特性進(jìn)行仿真研究,并對(duì)傳統(tǒng)的占空比擾動(dòng)觀察法,進(jìn)行了分析與仿真研究。2.本文對(duì)利用BP神經(jīng)網(wǎng)絡(luò)進(jìn)行最大功率點(diǎn)跟蹤時(shí),訓(xùn)練數(shù)據(jù)存在測(cè)量誤差的情況進(jìn)行了分析,指出了基于最小二乘的BP神經(jīng)網(wǎng)絡(luò)(LS-NN) MPPT算法預(yù)測(cè)結(jié)果的精準(zhǔn)度嚴(yán)重依賴訓(xùn)練數(shù)據(jù)的準(zhǔn)確性,提出了基于準(zhǔn)最小二乘的BP神經(jīng)網(wǎng)絡(luò)(QLS-NN) MPPT算法。通過仿真分析與實(shí)驗(yàn)測(cè)試對(duì)比了兩種不同算法的預(yù)測(cè)結(jié)果。3.本文對(duì)利用阻抗匹配原理進(jìn)行最大功率跟蹤的方法也做了重點(diǎn)研究。通過對(duì)傳統(tǒng)阻抗匹配MPPT算法的理論分析,發(fā)現(xiàn)傳統(tǒng)阻抗匹配MPPT算法對(duì)系統(tǒng)參數(shù)設(shè)置比較敏感,提出了改進(jìn)型動(dòng)態(tài)阻抗匹配MPPT算法。并通過仿真和實(shí)驗(yàn)對(duì)比,本文提出的改進(jìn)型動(dòng)態(tài)阻抗匹配MPPT算法能夠有效的改善光伏發(fā)電系統(tǒng)的性能。本研究采用基于準(zhǔn)最小二乘的BP神經(jīng)網(wǎng)絡(luò)MPPT算法減小了含有測(cè)量誤差的訓(xùn)練樣本對(duì)利用神經(jīng)網(wǎng)進(jìn)行預(yù)測(cè)最大功率的影響,提高了系統(tǒng)的魯棒性;基于改進(jìn)型動(dòng)態(tài)阻抗匹配的MPPT算法針對(duì)傳統(tǒng)阻抗匹配MPPT算法能夠有效的解決系統(tǒng)參數(shù)設(shè)定較多以及輸出功率受負(fù)載影響較大的問題,為提高光伏發(fā)電系統(tǒng)的整體效率和穩(wěn)定性提供了參考依據(jù)。
[Abstract]:Energy is not only the foundation of social development, but also the driving force of economic construction. The unique zero pollution and inexhaustible characteristics of solar energy make it the most ideal clean energy in the world, and solar energy is used to generate electricity is one of the most important uses. The research on photovoltaic power generation system not only provides an effective solution for the sustainable use of energy and environmental protection, but also has an important strategic significance for the stable development of society. The use of maximum power tracking control technology is an effective way to improve the efficiency of photovoltaic power generation system, which has an important impact on photovoltaic power generation industry, and is also a research hotspot in this field. In this paper, photovoltaic power generation system as the research object, aiming at the maximum power tracking problem, the main contents are: 1. According to the equivalent circuit of photovoltaic cell, the mathematical model of photovoltaic cell is deduced, and the model of photovoltaic cell and the simulation platform of the whole photovoltaic system are built by using Matlab/Simulink. The V-I output characteristics of photovoltaic cells are simulated by using the built simulation platform, and the traditional duty cycle disturbance observation method is analyzed and simulated. 2. In this paper, the measurement error of the training data in maximum power point tracking using BP neural network is analyzed. It is pointed out that the accuracy of BP neural network (LS-NN) MPPT algorithm based on least squares depends heavily on the accuracy of training data, and a BP neural network (QLS-NN) MPPT algorithm based on quasi least squares neural network is proposed. The prediction results of two different algorithms are compared by simulation analysis and experimental test. 3. 3. The method of maximum power tracking based on impedance matching principle is also studied in this paper. Based on the theoretical analysis of the traditional impedance matching MPPT algorithm, it is found that the traditional impedance matching MPPT algorithm is sensitive to the system parameters, and an improved dynamic impedance matching MPPT algorithm is proposed. The improved dynamic impedance matching MPPT algorithm proposed in this paper can effectively improve the performance of photovoltaic power generation system through the comparison of simulation and experiment. In this study, the BP neural network MPPT algorithm based on quasi least squares is used to reduce the influence of training samples with measurement error on the prediction of maximum power using neural network, and the robustness of the system is improved. The MPPT algorithm based on improved dynamic impedance matching can effectively solve the problem that the system parameters are set more and the output power is greatly affected by the load for the traditional impedance matching MPPT algorithm. It provides a reference for improving the overall efficiency and stability of photovoltaic power generation system.
【學(xué)位授予單位】：溫州大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TM615;TP183

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