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研究生:李柏宏
研究生(外文):Bo-Hong Li
論文名稱:應用基因演算法結合K階均值分類演算法於輻狀基底函數網路之太陽能最大功率點預測
論文名稱(外文):Genetic K-Means Algorithm Based RBF Network for PV Generation Maximum Power Point Prediction
指導教授:廖烔州
指導教授(外文):Chiung-Chou Liao
學位類別:碩士
校院名稱:清雲科技大學
系所名稱:電子工程研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2008
畢業學年度:95
語文別:中文
論文頁數:74
中文關鍵詞:太陽能發電最大功率點基因演算法輻狀基底函數網路
外文關鍵詞:PhotovoltaicMPPTGenetic AlgorithmRadial Basis Function Network
相關次數:
  • 被引用被引用:2
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  • 評分評分:
  • 下載下載:44
  • 收藏至我的研究室書目清單書目收藏:0
太陽能是再生能源設備中最廣泛使用的能源之一。而在太陽能轉換系統中,最重要的是如何增加最大功率轉換的效率,因此有許多相關文獻探討如何提高最大功率的轉換效率。與其他學習網路相比,輻狀基底函數網路因為學習速度較快,因此被認為是較好的學習方法之一。傳統的,輻狀基底函數網路在第一階段通常使用k階均值分類演算法將輸入資料進行分類。而在第二階段使用梯度坡降法降低最小標準平方差,調整每個節點之參數。

雖然k階均值分類演算法能將資料快速的進行分類,但可能因為隨機挑選初始中心點而陷入局部最佳解。為了解決這個問題,因此本論文提出結合k階均值分類演算法和基因演算法的混合演算法,簡稱為GKA。此方法使用基因演算法之特性,可降低原有方法因隨機挑選初始中心點,而對網路收斂結果產生的不穩定性,並且可以藉著基因演算法採取多點搜尋之特性,能具備跳脫局部最佳分類之特性,解決對網路訓練結果產生的不確定性,並將其應用在追蹤太陽能發電最大功率點上,使其能更準確的預測太陽能的最大功率點。本文將利用Matlab套裝軟體模擬所提出之方法,並以HIP-190BA3太陽能板之實際量測資料做訓練,進行太陽能最大功率點追蹤之測試,並將結果比較於輻狀基底函數網路,來驗證此法的可行性。
Photovoltaic (PV) is one of the most widely used devices among the renewable energies. It is important to operate PV energy conversion systems near the maximum power point (MPP) to increase the output efficiency of PV arrays. The radial basis function (RBF) network, which is considered as a good candidate for approximating problems for its faster learning capability compared with other networks. In traditional RBF networks, the k-means algorithm (KMA) is one of the most popular methods to classify the input patterns in the first stage of RBF network. In the second stage, a network adjusts iteratively parameters of each node by minimizing the least squares criterion according to gradient descent algorithm.
Although the KMA has an ability to classify the training patterns rapidly, it usually converges to a local minimum and can be oversensitive of randomly initial partitions. To solve these significant problems, a hybrid algorithm with KMA and Genetic Algorithm (GA) called GKA is proposed to improve the effectiveness of the clusters from the training patterns by avoiding being trapped in a local minimum solution during the k-means searching process and being taken a large amount of time to converge the global minimum solution with GA. Besides, the proposed GKA based clustering approach can overcome the problem of oversensitivity of randomly initial partitions with the existing KMA. By precise clustering of the training patterns, the aims at approximating the MPP of PV system can be accurately and rapidly reached with the least squares criterion in RBF network. Also, this thesis employed the actual data obtained from the practical PV energy conversion systems and the developed MPP prediction method was proven to be effective.
中文摘要 i
英文摘要 ii
誌謝 iii
目錄 iv
表目錄 vi
圖目錄 vii
符號說明 ix
第一章 緒論 1
1.1研究背景 1
1.2文獻回顧 2
1.3研究動機與方法 4
1.5論文組織架構 5
第二章 太陽能板基本理論 7
2.1太陽能板原理 7
2.2 國內太陽能產業趨勢 11
2.3 太陽能板之電氣特性 15
2.4 本章結論 23
第三章 太陽能最大功率點預測方法 24
3.1 輻狀基底函數網路(Radial Basis Function Network) 24
3.2 k階均值分類演算法(K-Means Algorithms) 28
3.3基因演算法之k階均值分類演算法(Genetic K-Means Algorithm) 30
3.3.1 編碼 32
3.3.2 選擇複製 32
3.3.3 突變 33
3.3.4 單步k-means 34
3.3.5 終止條件 34
3.4本章結論 34
第四章 實驗架構與數值驗證 35
4.1 參數設定 35
4.1.1 頻寬調整 36
4.1.2 學習速率 37
4.1.3 平均百分比絕對誤差(Average Percentage Absolute Error, APAE) 38
4.2 實驗模擬與分析 38
4.2.1 隨機選取樣本訓練結果 40
4.2.2 隨機選取測試結果 45
4.2.3 春季 50
4.2.4 夏季 55
4.2.5 秋季 59
4.2.6 冬季 64
4.3 本章結論 68
第五章 結論與未來研究方向 69
5.1 結論 69
5.2 未來研究方向 70
參考文獻 71
簡歷 74
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