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%.

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