臺灣博碩士論文加值系統

本論文主要目的在於開發一兼具物聯網功能之太陽光電模組陣列故障診斷系統。首先，將模組接成四串三併之「全部跨接連接」方式，以提升整體太陽光電發電系統之發電量，並考慮太陽光電模組陣列發生部份模組遮蔭情況下將使P-V特性曲線產生多峰現象，故本論文提出一權重最佳化粒子群演算法進行全域最大功率追蹤，以確保該模組陣列能輸出最大功率。然後，在太陽光電模組陣列中之跨接連接線段裝置一電流感測器，使得太陽光電模組陣列發生模組遮蔭或故障時，可透過感測橫跨於模組間之線段電流的方向，判斷當前模組遮蔭或故障之正確位置，並將其資訊透過LoRa無線傳輸模組上傳至雲端伺服器，並轉發至專業人員通訊界面上，以便維修人員進行故障排除。為使本系統之演算法有良好的運算速度，本論文以數位信號處理器實現權重最佳化粒子群演算法。最後，並以實測結果驗證所提具物聯網功能之太陽光電模組陣列故障診斷系統的可行性。

The main purpose of this thesis is to develop a photovoltaic module array fault diagnosis system with IOT function. First, the modules are connected into a four series three paralles configuration with total-cross-tied construction to increase the power generation of the overall photovoltaic system, and to consider the P-V characteristic curve produces multi peaks when partial modules of PV array are shaded. Therefore, this paper proposes a weight optimized particle swarm optimization algorithm for global maximum power tracking to ensure that the PV module array can output maximum power. Then, in the photovoltaic module array, a current sensor is connected across the connecting line segment, so that when the photovoltaic module array is shaded or faulty, the current of the line segment across the module can be sensed. Through the direction of the line segment, the correct position of the shading or faulty module can be detected. Then, the information is uploaded to the cloud server through the LoRa wireless transmission module, and forward it with the professional communication interface for maintenance personnel to troubleshoot. In order to make the algorithm of this system have a good computing speed, the weight optimization particle swarm optimization algorithm is implemented with digital signal processor. Finally, the feasibility of the photovoltaic module array fault diagnosis system with IOT function is verified by the measured results.

中文摘要 I
英文摘要 II
誌謝 IV
目錄 V
圖目錄 VII
表目錄 X
第一章 緒論 1
1.1 研究動機及目的 1
1.2 研究方法 2
1.3 論文大綱 3
第二章 太陽光電模組陣列 4
2.1 前言 4
2.2 太陽光電模組之工作原理 4
2.3 太陽光電模組陣列在遮蔭及故障下之輸出特性 11
第三章 太陽光電模組陣列在遮蔭及故障下之最大功率追蹤 15
3.1 前言 15
3.2 暨有太陽光電模組陣列之智慧型最大功率追蹤法 16
3.2.1差分進化法 16
3.2.2蟻群優化法 17
3.2.3人工蜂群法 19
3.2.4類神經演算法 21
3.2.5教與學演算法 22
3.3 基於權重最佳化之粒子群演算法 23
3.3.1傳統粒子群演算法 24
3.3.2粒子群演算法之權重最佳化 27
3.4 改良型粒子群演算法之最大功率追蹤控制器架構 28
3.4.1升壓型轉換器之設計 29
3.4.2數位信號處理器 36
3.5 基於粒子群演算法之最大功率追蹤模擬實測結果 38
3.5.1模擬結果 38
3.5.2實測結果 45
第四章 物聯網之雲端平台 52
4.1 前言 52
4.2 基於物聯網之太陽光電模組陣列故障診斷系統架構 53
4.2.1應用LoRa實現資料傳輸 53
4.2.2微軟Power BI軟體開發平台 54
4.3 太陽光電模組建立故障診斷策略 55
第五章 結合物聯網之太陽光電模組陣列故障診斷系統實測 56
5.1 前言 56
5.2 太陽光電模組陣列之故障檢測方法 56
5.2.1太陽光電模組陣列之故障模擬分析 56
5.2.2太陽光電模組陣列之故障檢測分析 60
5.3 故障診斷器電路 63
5.4 實測結果 64
第六章 結論 70
6.1 總結 70
6.2 未來展望 70
參考文獻 72

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