臺灣博碩士論文加值系統

本研究探討太陽電池模組缺陷造成品質影響的關聯機制，進行品質評價分級研究。首先建立太陽電池模組缺陷因子與影響品質評價分類制定，使用類神經網路技術進行模型建立，自動調配缺陷因子間權重值。輸入參數依據矽晶模組國際驗證規範(IEC61215)，所測得試驗前後之最大輸出功率及EL影像量化指標的衰減比率、後測絕緣電阻及濕漏電阻正規化指標，輸出參數評價分為3級(A、B、C)。類神經網路學習採用30組訓練資料，採用4個神經元之模型訓練完成，並以30組測試資料驗證其評價結果，評價測試結果判別成功率為93.3%。

This study investigates the correlation mechanism between the quality and defect in solar cell modules. First, defect factors for photovoltaic modules and classifications for effects on quality evaluations were developed. Models were then constructed using artificial neural network technology and the weight values between defect factors were automatically allocated by giving several learning data. The input parameters were adopted based on internationally verified standards for silicon modules (International Electrotechnical Commission, IEC61215), including the attenuation ratio of maximum output power and the EL image quantitative indicators, insulation resistance and wet leakage resistance regularization indicators. The output parameters were assigned evaluation classifications of A, B, and C. By using 30 set of training data, a best neural network model with 4 internal nodes is obtained. The minimum mean square error is 2.98*10-14. From validation evaluation results for another 30 set of test data, the successful rate is 93.3%.

目錄
摘 要 I
Abstract II
致謝 III
目錄 IV
圖目錄 VI
表目錄 X
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機與目的 2
1.3 研究方法 2
1.4 本文架構 3
第二章、相關文獻與技術探討 4
2.1 太陽模組失效分析 4
2.2 高效率太陽電池模組 5
2.3 太陽模組測試規範(IEC61215) 10
2.4 電致發光(Electroluminescence, EL)影像檢測 15
2.5 類神經網路(Artificial Neural network , ANN) 17
2.5.1 類神經網路品質評價文獻回顧 17
2.5.2 類神經網路 18
2.5.3 倒傳遞類神經網路 20
2.5.4 倒傳遞類神經網路架構 23
2.6 MATLAB使用者介面 25
第三章 類神經模型與使用者介面 26
3.1 類神經網路模型建立 26
3.2 MATLAB類神經網路架構 39
3.3 倒傳遞類神經網路的訓練流程 41
3.4 類神經網路模型匯出 44
第四章 電池模組品質評價系統建立與驗證 49
4.1 太陽模組評價因子決定 49
4.2 品質分級範圍與品質定義 53
4.3 類神經網路品質評價模型建立 58
4.4 類神經網路訓練資料 62
4.5 太陽模組缺陷評價系統使用者介面操作流程 72
4.6 太陽模組品質評價系統實際應用情況 90
第五章 結論與未來研究方向 93
5.1 結論 93
5.2 未來研究方向 94
參考文獻 95
附件 電池模組品質評價系統程式碼 100

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