臺灣博碩士論文加值系統

在太陽能系統中，其中最常見的問題就是部分遮蔭的情形，這造成了太陽能系統的功率輸出有大幅的減少。元啟發式演算法可以在多峰的功率-電壓曲線中追蹤到最大功率點，常見的元啟發式演算法都已經應用在太陽能最大功率的追蹤上，有粒子群演算法、人工蜂群演算法、蟻群演算法和布穀鳥搜索演算法。
灰狼演算法是一個新的元啟發式演算法，它可以解決許多應用中的優化問題，包括太陽能的最大功率追蹤。然而在照度快速變化的情況下，原始灰狼演算法的準度和追蹤時間仍可以被改善，因此已經提出了一些改良型灰狼演算法來改善，但是這些只有小部分地改進。
因此，本文提出了一個增強型改良灰狼演算法，本文方法在灰狼演算法中加入了非線性的收斂因子、加權平均和狼撲的行為。透過模擬和實驗測試，本文提出的增強型改良灰狼演算法大幅地縮短了追蹤時間(約為灰狼演算法的60%)且達到最高的最大功率點。

Partial shading condition (PSC) is one of the most common problems in the
photovoltaic (PV) system. It causes the output power of a PV system drastically
decrease. Meta-heuristic algorithms (MHA) can track the maximum power point in
a multiple-peak power-voltage curve. The common MHAs, such as particle swarm
optimization (PSO), artificial bee colony (ABC), ant colony optimization (ACO) and
cuckoo search (CS) have been used for maximum power point tracking (MPPT).
Grey wolf optimization (GWO) algorithm is a new optimization algorithm based
on MHA. It has been used to solve optimization problems in many applications including MPPT for a PV system. However, the accuracy and tracking time in the
conventional GWO can still be improved for the condition of rapidly changing irradiance. Therefore, there have been some modified grey wolf optimization (MGWO) algorithms proposed to improve the GWO. Nevertheless, only incremental improvement has been made.
Therefore, an enhanced modified grey wolf optimization (EMGWO) is proposed
in this thesis. The proposed method adds the weighting average, the pouncing behavior and nonlinear convergence factor in the GWO. As will be shown via simulation and experiment, the proposed EMGWO can drastically reduce the tracking
time (about 60% of the GWO) and achieve the highest maximum power point.

1 INTRODUCTION
1.1 Background & Motivation
1.2 Outline
2 PV MODEL and PARTIAL SHADING PROBLEM
2.1 PV Model
2.2 Partial Shading Problem
3 GREY WOLF ALGORITHM
3.1 Overview
3.2 Mathematical Model
3.3 Review of Other Modified Grey Wolf Optimizer
3.3.1 Mittal's MGWO Algorithm
3.3.2 Ma's MGWO Algorithm
4 PROPOSED EMGWO ALGORITHM and EVALUATION RESULT
4.1 Proposed EMGWO Algorithm
4.2 Simulation Result
4.3 Experimental Set up
4.4 Experimental Result
4.4.1 Static Case
4.4.2 Dynamic Case
5 CONCLUSION & FUTURE WORK
5.1 Conclusion
5.2 Future Work
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