摘要
　太陽光伏發電(Photovoltaic Generation, PVG)輸出之電壓與電流隨著環境條件變動而改變。為了使太陽光伏發電在不同環境條件下皆能維持最大輸出，最大功率追蹤(Maximum Power Point Tracking, MPPT)技術一直以來都是太陽能產業重點研究課題。時至今日已有許多最大功率追蹤技術被提出。
　　本文將兩種最常見的最大功率追蹤技術擾動觀察法(Perturb and Observe, P&O)與粒子群演算法(Particle Swarm Optimization, PSO)做出比較與分析。將中央氣象局所提供之一整日之照度及溫度資料利用Matrix Laboratory (MATLAB) 軟體分別模擬一塊額定輸出功率為200W之太陽能板在採取擾動觀察法與粒子群演算法時之差異，並比對兩者整日運作時在暫態追蹤時間與功率上的差異，並比較兩者間因為在暫態追蹤時所損失的能量(unharvested energy)。

ABSTRACT
Maximum Power Point Tracking (MPPT) plays an important role in Photovoltaic Generation (PVG) systems because it maximizes the power output from a PVG system. Thus, an MPPT can minimize the overall system cost. MPPT operates a PVG system under different solar irradiances and temperatures. Many such algorithms have been proposed. This thesis provides a comparison between Perturb and Observe (P&O) method, and Particle Swarm Optimization (PSO) method with the weather data from Taiwan Weather Bureau. Matrix Laboratory (MATLAB) programming by using real data is implemented for a PVG system with a rated output of 200 W energy to obtain the curve performance and the unharvest energy for different the MPPT algorithms.

TABLE OF CONTENTS
ACKNOWLEDGEMENT ii
摘要 iii
TABLE OF CONTENTS v
LIST OF FIGURES vii
LIST OF TABLES ix
ACRONYMS AND ABBREVIATIONS x
CHAPTER 1 INTRODUCTION 1
1.1 Background 1
1.2 Study Objective 3
1.3 Thesis Outline 3
CHAPTER 2 PHOTOVOLTAIC GENERATION 5
2.1 Photovoltaic Introduction 5
2.1.1 Silicon 5
2.1.2 P-N Junction 7
2.1.3The Electric Current 8
2.2 Photovoltaic Classification 9
2.2.1 Non-Organic and Organic Cell 9
2.2.2 Silicon and Non-Silicon Cell 10
2.3 Photovoltaic Electrical Characteristic 12
CHAPTER 3 MAXIMUM POWER POINT TRACKING TECHNOLOGY OVERVIEW 19
3.1 Maximum Power Point Technology 19
3.1.1 Perturbation and Observe (P & O) 19
3.1.2 Feedback Voltage 23
3.1.3 Feedback Power 24
3.1.4 Incremental Conductance (IncCond) 24
3.1.5 Particle Swarm Opimization (PSO) 28
3.1.6 Parabolic Prediction 32
3.1.7 Fuzzy Logic Control (FLC) 33
3.1.8 Neural Network (NN) 36
3.1.9 Fractional Open Circuit Voltage (FOCV) 39
3.1.10 Fractional Short Circuit Current (FSCI) 39
3.1.11 Adaptive Neuro Fuzzy Inference System (ANFIS) 40
3.2 Comparison of Various Algorithm 42
CHAPTER 4 MAXIMUM POWER POINT TRACKING SOFTWARE ARCHITECTURE AND TECHNICAL ANALYSIS 45
4.1 Solar Irradiance Characteristic Simulation Analysis 45
4.2 Software Program Process 46
CHAPTER 5 SIMULATION TEST RESULTS 50
5.1 Simulation Specification 50
5.2 Simulation Results of Comparison Methods 51
5.2.1 Comparison Single Irradiance Simulation Result 52
5.2.2 Comparison Various Irradiances Simulation Result 54
5.2.3 Simulation Results for Whole Day of Comparison Methods 60
CHAPTER 6 CONCLUSION AND FUTURE RESEARCH 63
6.1 Conclusion 63
6.2 Future Research 65
REFERENCES 66

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