發(fā)布時(shí)間：2021-11-04 16:39

　　全球變暖的加劇、化石燃料的枯竭以及成本效益高的制造技術(shù)的進(jìn)步,使可再生能源成為一種可靠的能源。燃料電池、地?zé)�、風(fēng)能、水力、生物質(zhì)量和太陽能是領(lǐng)先的可再生能源。其中最有希望的是太陽能。太陽能利用光伏技術(shù)直接轉(zhuǎn)化為電能。太陽能電池板與房屋、汽車、充電站和移動(dòng)平臺(tái)、抽水站等的集成在現(xiàn)實(shí)世界中提供了廣泛的應(yīng)用。太陽能的主要優(yōu)點(diǎn)是成本低、可擴(kuò)展性好、碳足跡小、維護(hù)少、機(jī)械疲勞小、安裝快捷、無噪音、無污染。雖然光伏系統(tǒng)具有廣闊的發(fā)展前景,但其固有的非線性特性、對(duì)運(yùn)行條件的敏感性、不同的光照強(qiáng)度和溫度使得這項(xiàng)任務(wù)具有挑戰(zhàn)性。部分陰影（PS）會(huì)導(dǎo)致可用功率的嚴(yán)重?fù)p失。提高光伏系統(tǒng)生產(chǎn)率的最有效途徑是引入控制系統(tǒng),迫使光伏系統(tǒng)在最大功率點(diǎn)上運(yùn)行。這種技術(shù)被稱為最大功率點(diǎn)跟蹤（MPPT）。MPPT技術(shù)被分為許多種類,有自己的優(yōu)點(diǎn)和缺點(diǎn)。MPPT控制器的優(yōu)點(diǎn)是MPP的快速跟蹤、全局最大值（GM）檢測(cè)和魯棒性。本文提出了兩種高效的基于生物激勵(lì)的MPPT控制技術(shù)。本研究的主要目的是發(fā)展光伏系統(tǒng)的控制策略,以克服現(xiàn)有多點(diǎn)跟蹤控制技術(shù)的缺點(diǎn)。首先,針對(duì)多點(diǎn)跟蹤問題,在多點(diǎn)跟蹤控制器上實(shí)現(xiàn)了一種基于群體智能的蝗蟲優(yōu)化... 

【文章來源】：中國科學(xué)技術(shù)大學(xué)安徽省 211工程院校 985工程院校

【文章頁數(shù)】：126 頁

【學(xué)位級(jí)別】：碩士

【文章目錄】：
摘要
ABSTRACT
List of Abbreviations
Chapter 1. Introduction
    1.1 Energy
        1.1.1 Renewable energy
        1.1.2 The future of renewables
    1.2 Leading renewable energy resources
        1.2.1 Hydroelectric energy
        1.2.2 Wind Power
        1.2.3 Biomass
        1.2.4 Geothermal power
        1.2.5 Solar
        1.2.6 Ocean tidal energy
        1.2.7 Hydrogen and fuel cells
        1.2.8 Thermoelectric generators (TEG)
        1.2.9 Other renewable energy sources
    1.3 Trends in PV technologies and effectiveness
        1.3.1 Global technology trends and prices
        1.3.2 PV cell efficiency
        1.3.3 PV system efficiency
    1.4 Motivation
    1.5 Research Contents
    1.6 Innovation
    1.7 Chapter Layout
Chapter 2. PV System Modeling and Characteristics
    2.1 Mathematical modeling of PV cell
    2.2 PV cell modeling
        2.1.1 Single diode model
        2.1.2 Double diode model
    2.3 PV model characteristics
        2.3.1 Solar Cell I-V Characteristic Curves
        2.3.2 Solar Array series-parallel combination
        2.3.3 PV Parameters description
        2.3.4 Effects of temperature condition
        2.3.5 Effects of Uniform irradiance condition
        2.3.6 Partial shading condition
    2.4 The components of the PV system
        2.4.1 DC converter
            2.4.1.1 Boost converter
            2.4.1.2 Buck converter
            2.4.1.3 Buck-Boost converter
            2.4.1.4 Cuk converter
        2.4.2 MPPT controller
        2.4.3 Inverters
            2.4.3.2 Types of inverters for PV systems
        2.4.4 Load management and grid connectivity
Chapter 3. Soft Computing based MPPT techniques
    3.1. Introduction
        3.1.1 Literature review
        3.1.2 Artificial Neural Networks (ANN)
        3.1.3 Fuzzy logic controller (FLC)
    3.2 The Proposed GHO MPPT
        3.2.1 The mathematical model of GHO
        3.2.2 GHO for MPPT of PV systems
        3.2.3 Tracking mechanism of GHO
        3.2.4 GHO under Complex Partial Shading
        3.2.5 Advantages of GHO on MPPT
    3.3 Results and discussion
        3.3.1 Case 1: Fast varying irradiance
        3.3.2 Case 2 partial shading
        3.3.3 Case 3: Partial shading
        3.3.5 Case 4 Complex partial shading
        3.3.6 Case 5: Complex Partial Shading
        3.3.7 Efficiency and performance evaluation
    3.4 Conclusion
Chapter 4. Swarm Intelligence based MPPT Techniques
    4.1 Some conventional swarm intelligence based MPPT techniques
        4.1.1 Particle swarm optimization (PSO)
        4.1.2 Grey Wolf Optimization (GWO)
        4.1.3 Artificial bee colony (ABC)
        4.1.4 ABC Application for MPPT control
        4.1.5 Cuckoo Search (CS)
            4.1.5.1 Main features and sequence of CS
            4.1.5.2 Cuckoo search for MPPT in PV system
        4.1.6 Adoptive cuckoo search algorithm
    4.2 The proposed HHO based MPPT technique
        4.2.1 The HHO model
        4.2.2 Soft besiege
        4.2.3 Hard besiege
        4.2.4 Soft besiege with progressive rapid dives
        4.2.5 Hard besiege with progressive rapid dives
        4.2.6 Working methodology of HHO
        4.2.7 Advantages of HHO on MPPT
        4.2.8 Case partial shading
        4.2.9 Results of Quantitative and statistical results
    4.3 Case field atmospheric data
        4.3.1 Weather conditions
        4.3.2 Spring results
        4.3.3 Summer results
    4.4 Hardware setup
    4.5 Conclusion
Chapter 5. Conclusion and Future Work
    5.1 Contributions
    5.2 Future Work
References
List of Publications



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