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研究生: 林建璋
Chang-Chung Lin
論文名稱: 基於軟計算之太陽能發電系統全域最大功率追蹤法則研究
Study on Global Maximum Power Point Tracking Algorithm for Photovoltaic Generation Systems Based on Soft Computing
指導教授: 劉益華
Yi-Hua Liu
口試委員: 劉益華
Yi-Hua Liu
邱煌仁
Huang-Jen Chiu
王順忠
Shun-Chung Wang
鄧人豪
Jen-Hao Teng
學位類別: 碩士
Master
系所名稱: 電資學院 - 電機工程系
Department of Electrical Engineering
論文出版年: 2018
畢業學年度: 106
語文別: 中文
論文頁數: 154
中文關鍵詞: 太陽能發電系統部分遮蔭全域最大功率追蹤軟計算法
外文關鍵詞: photovoltaic systems, partially shaded condition, global maximum power point tracking, soft computing
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  • 在大型太陽能發電系統中部分遮蔭情況常常發生,且因太陽能發電系統是由多個太陽能電池模組串並聯組成,導致功率-電壓特性曲線產生出多個峰值,因此全域最大功率追蹤變得相當重要。本文研究13種可用於部分遮蔭情況下的全域最大功率追蹤之軟計算方法,方法包含粒子群優化法、亂數搜尋法、雙載子亂數搜尋法、灰狼群優化法、螢火蟲演算法、改良型螢火蟲、人工蜂群演算法、改良型人工蜂群、杜鵑鳥搜尋法、退火演算法、差異進化演算法、改良型差異進化演算法及人工螞蟻群演算法等。在模擬樣式部分,本文提供252種不同照度樣式及均勻照度之模擬結果,而在模擬數據方面,由於軟計算法本身含有亂數成分,因此各方法對於每種照度樣式皆執行100次的模擬。最後本文將模擬出來的結果進行整理及比較並初步的分析結果,希望能讓使用者在做這領域的研究時能有個參考的依據。


    In large photovoltaic generation systems (PGSs), partially shaded conditions (PSCs) often happen. PCGs result in multiple peak values in power–voltage characteristic curve; hence, global maximum power point tracking becomes essential. In this thesis, thirteen soft computing (SC)-based global maximum power point tracking (GMPPT) techniques for PGSs operating under PSCs are investigated. The compared methods include particle swarm optimization (PSO), random search method (RSM), dual carrier chaos search method (DCCSM), gray wolf optimization (GWO), firefly algorithm (FA), modified firefly algorithm (MFA), artificial bee colony algorithm (ABC), modified artificial bee colony algorithm (MABC), cuckoo search (CS), simulated annealing (SA), differential evolution (DE), modified differential evolution (MDE), and ant colony optimization (ACO). Simulated results for 252 different shading patterns and one uniform irradiance condition will be provided. Due to their stochastic nature, 100 simulations for each shading pattern will be performed for each GMPPT technique. After all the simulations are performed, the obtained results will be summarized and some conclusion will be made accordingly. This study hopes to presents reference information for scholars planning to carry out research in this field.

    摘要 I Abstract II 誌謝 III 目錄 V 圖目錄 VIII 表目錄 XII 第一章 緒論 1 1.1 研究背景與動機 1 1.2 研究目的 2 1.3 文獻探討 3 1.4 論文大綱 4 第二章 太陽能電池介紹 5 2.1 太陽能電池簡介 5 2.2 太陽能電池原理 6 2.3 太陽能電池種類簡介 7 2.4 太陽能電池電器特性 9 第三章 太陽能最大功率追蹤技術 16 3.1 最大功率追蹤技術簡介 16 3.2 本文比較之全域最大功率追蹤技術 16 3.2.1 粒子群優化法 16 3.2.2 亂數搜尋法 20 3.2.3 雙載子混沌搜尋法 23 3.2.4 灰狼群優化法 28 3.2.5 螢火蟲搜尋法 32 3.2.6 改良型螢火蟲搜尋法 36 3.2.7 人工蜂群演算法 39 3.2.8 改良型人工蜂群演算法 43 3.2.9 杜鵑鳥搜尋法 46 3.2.10 模擬退火演算法 51 3.2.11 差異進化演算法 54 3.2.12 改良型差異演算法 58 3.2.13 人工螞蟻群演算法 61 第四章 模擬結果與分析 65 4.1 測試環境與方法介紹 65 4.2 測試項目之定義 68 4.3 模擬結果 69 4.3.1 PSO性能表現 71 4.3.2 RSM性能表現 76 4.3.3 DCCSM性能表現 80 4.3.4 GWO性能表現 84 4.3.5 FA性能表現 88 4.3.6 MFA性能表現 92 4.3.7 ABC性能表現 96 4.3.8 MABC性能表現 100 4.3.9 CS性能表現 104 4.3.10 SA性能表現 108 4.3.11 DE性能表現 112 4.3.12 MDE性能表現 116 4.3.13 ACO性能表現 120 4.4 模擬結果分析與比較 124 第五章 結論與未來展望 130 5.1 結論 130 5.2 未來展望 130 參考文獻 131

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