臺灣博碩士論文加值系統

本論文的主要目的是開發光伏系統在局部遮陰條件下的最大功率追蹤器。由
於天氣是不可預測的，所以系統有可能存在局部和全局的最大功率點。因此，我
們必須在遮陰條件下追蹤到全局最大功率點，為了使我們的光伏系統能提供有效
的最大功率輸出。
首先，我們建立光伏陣列系統數學模型，並且調查分析在部分遮陰以及沒有
遮陰條件下的電壓和功率的輸出。然而，在遮陰條件下的光伏陣列輸出會產生多
個最大功率點，所以我們必須找出合適的全局最大功率點追蹤技術。
我們提出一個新的概念來改良傳統的粒子群優化法並加強算法開發能力和提
高系統的性能。我們除了使用線性遞減慣性權重外，還應用非線性適應學習因子
增強追蹤能力。它能夠避免陷入局部最佳解，並提供系統具有更精確的收斂。實
驗結果表明，改良後的粒子群算法在遮陰條件下的全局最大功率點追蹤具有較高
的精確度與收斂性。

The major target of this thesis is to develop the maximum power tracker of
photovoltaic (PV) systems under the partial-shading conditions. Since the weather is
unpredictable, there might exist local and global maximum power points (MPP) in the
systems. Therefore, we must be able to track the global MPP under the partial-shading
conditions in order to make our PV systems offer effective maximum power output for
obtaining optimal system performance.
First of all, the mathematical model is established for a PV array system to
investigate and analyze the voltage and power output under partial-shade and
non-partial-shade conditioning. However, the output power of PV systems could have
various MPP under partial-shading conditions, so we have to determine an appropriate
technology for the tracking control of global MPP.
A novel concept is presented to modify the traditional particle swarm optimization
method for strengthening algorithm capability and improving the system performance.
In addition to using linear decreasing inertia weight, we apply nonlinear adapting
learning factors for enhancing the tracking ability. It can avoid falling into local
maximum solutions and provide the system to have more accurate convergence. As a
result, the simulation results show that the modified particle swarm optimization has the
potentials to track the global MPP with accurate rate of convergence under
partial-shading conditions.

致謝辭. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . I
摘要. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . II
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . III
Table of Contents .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV
List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . VI
List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . VII
Chapter 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 Objective and background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Control method and research status of MPPT . . . . . . . . . . . . . . . . . . . . . 3
1.3 Organization of thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
Chapter 2 Solar Photovoltaic Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.1 Principle of the PV cell . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.2 PV cell model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.3 PV module and array model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
Chapter 3 Maximum Power Point Tracking. . . . . . . . . . . . . . . . . . . . . . . . . . 13
3.1 Output characteristics of partial-shading conditions . . . . . . . . . . . . . . . . . 13
3.2 MPPT control method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
3.2.1 Perturbation and observation method . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
3.2.2 Incremental conductance algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
3.2.3 Fuzzy logic control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
3.3 Comparison of MPPT methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
Chapter 4 Particle Swarm Optimization Algorithm. . . . . . . . . . . . . . . . . . . . 25
4.1 The principle of particle swarm optimization . . . . . . . . . . . . . . . . . . . . . . 25
4.2 Advantages and disadvantages of particle swarm optimization . . . . . . . . 28
4.3 Parameter design of particle swarm optimization . . . . . . . . . . . . . . . . . . . 29
4.4 Initial parameters of particle swarm optimization . . . . . . . . . . . . . . . . . . . 30
4.5 Linear decreasing inertia weight . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
4.6 Linear learning factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
4.7 Nonlinear adapting learning factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
Chapter 5 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
Chapter 6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
Reference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
List of Figures
2.1 Operation of PV cell . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.2 PV cell equivalent circuit . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.3 The composition of the PV array . . . . . . . . . . . . . . . . . . . . 9
2.4 Equivalent circuit models of generalized PV module . . . . . . . . . . 10
2.5 P-V characteristics of PV cell model . . . . . . . . . . . . . . . . . . 11
2.6 P-V characteristics of PV module model . . . . . . . . . . . . . . . . 12
2.7 P-V characteristics of PV array model . . . . . . . . . . . . . . . . . 12
3.1 PV array without shading conditions . . . . . . . . . . . . . . . . . . 15
3.2 P-V characteristics of PV array in Fig 3.1 . . . . . . . . . . . . . . . 15
3.3 PV array under partial-shading conditions . . . . . . . . . . . . . . . 16
3.4 P-V characteristics of PV array in Fig 3.3 . . . . . . . . . . . . . . . 16
3.5 Moving of perturbation and observation . . . . . . . . . . . . . . . . 17
3.6 Flow chart of perturbation and observation . . . . . . . . . . . . . . . 18
3.7 Flow chart of incremental conductance algorithm . . . . . . . . . . . 20
3.8 Flow chart of fuzzy logic control . . . . . . . . . . . . . . . . . . . . . 21
3.9 No partial-shading conditions with perturbation and observation . . . 23
3.10 Partial-shading conditions with perturbation and observation . . . . . 23
3.11 Persistent disturbances . . . . . . . . . . . . . . . . . . . . . . . . . . 24
4.1 Moving of particles . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
4.2 Moving schematic view . . . . . . . . . . . . . . . . . . . . . . . . . . 27
4.3 Procedure of tracking . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
4.4 Nonlinear adapting learning factors of c1(k) . . . . . . . . . . . . . . 35
4.5 Nonlinear adapting learning factors of c2(k) . . . . . . . . . . . . . . 35
5.1 Global MPP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
5.2 Local MPP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
5.3 Fixed !, c1; c2 PSO algorithm . . . . . . . . . . . . . . . . . . . . . . 39
5.4 Using linear decreasing inertia weight and xed c1; c2 . . . . . . . . . 39
5.5 Using linear learning factors and xed ! . . . . . . . . . . . . . . . . 40
5.6 Using nonlinear adapting learning factors and xed ! . . . . . . . . . 40
5.7 Using linear decreasing inertia weight and nonlinear adapting learning
factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
5.8 Successful rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
List of Tables
3.1 Comparison of various MPPT methods . . . . . . . . . . . . . . . . . 22
5.1 Parameters of PV module . . . . . . . . . . . . . . . . . . . . . . . . 37
5.2 Parameters for simulation . . . . . . . . . . . . . . . . . . . . . . . . 37

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