再生能源需求上升，提高建置大型太陽能發電廠數量及面積，其管理與診斷技術就必須更加提升檢測速度與準確度。目前針對太陽能板常見的熱斑故障檢測方式為透過熱影像儀拍攝，由於太陽能電廠面積很大，不易透過人工方式檢測，因此採用具備熱影像相機的無人機進行拍攝，以監測太陽能板的健康狀態。
本研究提出一種方法由無人機收集影像資料，並利用深度學習方法進行熱斑故障診斷，且結合地理位置資訊系統提供故障定位服務。首先，透過無人機的可見光及熱影像相機採集整個太陽能電廠之影像資料，藉由正射影像技術及影像處理將太陽能面板從影像分割作為資料集。再來，利用提出之故障疊加法及生成對抗網路ProGAN來增強深度學習所需的訓練影像資料，改善訓練集資料不平衡問題。再者，透過實際案場資料作為測試集，以捲積神經網路Resnet-50進行熱斑故障之熱特徵訓練及測試分析，驗證本研究提出的方法能有效解決訓練數據不足及可實際應用之可行性。最後，透過圖資系統將故障面板標示在電廠可見光俯視圖之上。
本研究於實際太陽能電廠進行實測，達到由無人機採集影像資料後，透過系統平台上傳至異地伺服器資料庫，再經由伺服器進行影像處理及深度學習之辨識，並將結果顯示在太陽能電廠監控系統平台，以便尋找故障面板做更換。該方法可對於大型太陽能電廠在故障診斷與電廠效能評估上，提供一個具有價值的參考依據。

As the demand for renewable energy rises, the number and area of large-scale solar power plants will increase, and their management and diagnostic technologies must further improve detection speed and accuracy. Currently, the common method for hot spot fault detection of solar panels is to take pictures with a thermal imager. Due to the large area of the solar power station, manual inspection is not easy. Therefore, UAVs(unmanned aerial vehicles) equipped with thermal imaging cameras will be used to photograph the health of the solar panels.
This study proposes a method for diagnosing hotspot faults using image data collected by UAVs using deep learning methods and combined with geographic information system to provide fault location services. First, the image data of the entire solar power plant is collected through the visible light and thermal imager of the UAVs, and the solar panels are segmented from the image as a data set through orthophoto technology and image processing. In addition, the proposed Fault Overlay Method and the generated adversarial network ProGAN are used to enhance the training image data required for deep learning and solve the data imbalance problem in the training set. Furthermore, the actual case field data is used as the test set. The thermal feature training and test analysis of hot spot faults are carried out using the convolutional neural network Resnet-50. To verify the feasibility of the method proposed in this study to effectively solve the shortage of training data and be practically applicable. Finally, the fault panel is marked on the perspective view of the plant through the geographic information system.
In this study, field measurements were carried out in real solar power plants. After the UAV collects the data, it is uploaded to the remote server database through the system platform. The server performs image processing and deep learning recognition. Display the results in the solar plant monitoring system platform to find faulty panels for replacement. This research can provide a valuable reference for large-scale solar power plants in fault diagnosis and plant performance evaluation.

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