臺灣博碩士論文加值系統

當太陽能模組部分遮蔽的情況發生，因電壓與功率特性曲線不同
而產生多峰的情形且使輸出功率大幅降低。因此需使用最大功率追蹤
(Maximum Power Tracking, MPPT)控制器，以利因不同遮陰因素下太
陽能電池依然能保持最大功率輸出。
混合演算法為近年來，學者們所研究的方向，目的是利用兩種互
補的方式以改善單一演算法的缺點。本論文提出一個結合改良型螢火
蟲演算法(Improved Firefly Algorithm, IFA)與粒子群最佳化法(Particle
Swarm Optimization, PSO)之混合法。本文利用太陽能模擬機模擬出三
條不同照度且有部分遮陰現象之特性曲線，比較本文所提出的混合法、
多粒子擾動觀察法Multiple Perturb-and-Observe(MP&O)、擾動觀察法
結合粒子群最佳化法的演算法Perturb-and-Observe+ Particle Swarm
Optimization(P&O+P.S.O)、差分演化法結合粒子群最佳化法的演算法
Different Evolution+ Particle Swarm Optimization(D.E+P.S.O)與原有粒
子群最佳化法之追蹤時間和追蹤效率。實驗結果證明本文所提出混合
法所得之效率較高，追蹤時間較短。

When partial shading conditions occur, the P-V characteristics
curves of the photovoltaic string may exhibit multiple peaks and the
operating point may not be at the global maximum power (GMP) point,
leading to low efficiency. Therefore, maximum power point tracking
(MPPT) control strategy is needed in a photovoltaic system to output the
power at the maximum.
Combing two different MPPT strategies has gained its popularity
because strategies in complementary can improve the performance of a
single algorithm. This thesis proposes a hybrid method, combing
improved firefly algorithm (IFA) and particle swarm optimization (PSO)
to capture the great attributes from both methods, and yield a better
MPPT control strategy. In order to justify the proposed method, we
compared it with same of the state of the art such as Multiple
Perturb-and-Observe, Perturb-and-Observe+Particle Swarm Optimization,
Different Evolution+ Particle Swarm Optimization and PSO. All the
verifications were done experimentally and the results showed that the
proposed hybrid method yields the highest efficiency and the shortest
tracking time.

摘要
ABSTRACT
目錄
圖目錄
表目錄
第一章　緒論
1.1 前言
1.2 研究動機與背景
1.3 系統架構與描述
1.4 內容大綱
第二章　部分遮蔭與太陽能最大功率追蹤
2.1 前言
2.2 太陽能電池簡介
2.2.1 太陽能電池模型
2.2.2 太陽能電池於遮蔭下之特性
2.3 太陽能最大功率追蹤演算法
2.3.1 擾動觀察法(Perturbation and Observation, P&O)
2.3.2 差分演化演算法 (Different Evolution, DE)
2.3.3 粒子群最佳化法(Particle Swarm Optimization, PSO)
2.3.4 螢火蟲演算法(Firefly Algorithm, FA)
2.3.5 改良型螢火蟲演算法(Improved F.A)
2.3.6 多粒子擾動觀察法(MP&O)
2.3.7 擾動觀察法結合粒子群最佳化法的演算法(P&O+P.S.O)
2.3.8 差分演化法結合粒子群最佳化法的演算法( D.E+P.S.O)
2.3.9 本文提出之混合法
第三章　軟硬體規劃
3.1 前言
3.2 數位信號處理器功能介紹
3.3 數位信號處理器周邊電路
3.3.1 電壓與電流感測電路
3.3.2 開關驅動電路
3.3.3 串列輸出命令電路
3.4 演算法動作流程
3.5 交錯式直流升壓轉換器
3.6 建立實驗曲線
第四章　實驗結果與討論
4.1 前言
4.2 測試之太陽能特性曲線及硬體規格
4.3 最大功率點追蹤實驗
4.3.1 多粒子擾動觀察法實驗結果
4.3.2 擾動觀察法結合粒子群最佳化法之混合法實驗結果
4.3.3 粒子群最佳化法實驗結果
4.3.4 差分演化法結合粒子群最佳化法之混合法實驗結果
4.3.5 本文提出改良型螢火蟲演結合粒子群最佳化之混合法實驗結果
4.3.6 動態曲線變化之實驗結果
4.4 最大功率追蹤實驗之討論與效率比較
第五章　結論與未來展望
5.1 結論
5.2 未來展望
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