本研究致力於太陽能光電(Photovoltaic, PV)廠維運系統的建立，使用無人機搭載熱成像儀拍攝影像，應用紅外線熱(IR)影像檢測PV 模組熱瑕疵、RGB影像檢測模組表面瑕疵，兩者交叉驗證模組瑕疵原因。
　　第一部建立太陽能光電廠資訊圖，以臺灣太陽能光電廠(PV模組1482片，410kW)為例，應用尺度不變特徵點偵測法，偵測太陽能光電廠影像特徵點，解決影像亮度、旋轉、縮放等特徵變化問題，將多張局部電廠影像相同特徵點進行匹配，透過平面投影矩陣轉換與隨機抽樣一致化計算最佳特徵點數量，將影像拼接形成太陽能光電廠全景圖，再藉由影像色度去除電廠全景圖背景雜訊，並利用影像亮度對PV系統進行模組分割、形態學對PV 模組進行幾何重建，使用拉普拉斯運算子，提取PV 模組邊緣輪廓，得到周長、面積、形心特徵，辨識計算PV 模組數量與位置，形成太陽能光電廠資訊圖。
　　第二部分建立PV模組瑕疵辨識分類系統，收集臺灣7座太陽能光電廠瑕疵，使用卷積神經網路(CNN)，除卷積層擷取影樣特徵外，透過最大池化與局部響應正規化函數強化影像特徵，並透過色彩空間轉換強化色彩特徵，提高分類模型準確率，辨識定位PV 模組瑕疵。對IR 影像熱點，辨識準確率為100%。對正常模組及7種瑕疵共八類進行分類，準確率97.52%。對RGB 影像模組外觀正常及5種瑕疵共六類進行分類，準確率99.17%。同時對IR 影像與RGB 影像14種瑕疵進行分類，準確率97.52%。將IR影像與RGB影像交叉驗證瑕疵產生的原因，本研究使用K-Fold交叉驗證選擇出最佳模型，辨識一張影像小於0.02秒，低於相機時間常數，可應用在即時偵測。
　　第三部分建立太陽能光電廠瑕疵資訊圖，在檢測時，將具有瑕疵之PV模組標記，得知電廠PV 模組的瑕疵與其位置，利於電廠維護。

This study aims to build a photovoltaic (PV) plant maintenance and operation system, using an unmanned aerial vehicle (UAV) carrying a thermal imager to take images. In the proposed system, the infrared (IR) image was used for detecting PV module thermal defects, and the RGB image was used for detecting module surface defects. The two images were employed to cross validate the causes for module defects.
In Part I, the PV plant information pattern was created, and the Taiwan PV plant (1,482 PV modules, 410 kW) was taken as an example. The PV system image feature points were detected by using the Scale Invariant Feature Transform (SIFT), in order to solve the feature variation problems, such as image luminance, rotation, and zoom in/out. The same feature points of multiple local power plant images were matched. Afterwards, the optimal number of feature points was calculated by homography transformation and random sample consensus (RANSAC) to form the PV plant panorama by image stitching. The power plant panorama background noise was removed by image hue. The module segmentation of PV systems was performed by using image luminance, and the PV module was geometrically reconstructed by using morphology. The PV module edge contour was extracted by the Laplace operator to obtain the perimeter, area, and centroid features. The quantity and positions of PV modules were recognized and calculated to form the PV plant information pattern.
In Part II, the PV module defect recognition and classification system was built. The defects in seven PV plants in Taiwan were collected, and the image features were enhanced using a convolutional neural network (CNN). Besides using the convolution layer to capture the image features, Max Pooling and local response normalization were used to enhance the image features. Color space transform was used to intensify the color features, increase the accuracy of the classification modules, and recognize and position the PV module defects. The IR image hot spot recognition accuracy was 100%. The classification accuracy of eight modules, including one normal module and seven defect modules, is 97.52%. The classification accuracy of six modules, including the appearances of one normal module and five defects in RGB images, is 99.17%. The classification accuracy of 14 defects in IR thermal images and RGB images is 97.52%. The causes of defects were cross validated by IR thermal image and RGB image. This study applied the K-fold cross validation to select the optimal model, and the recognition time of one image was shorter than 0.02 sec, which is lower than the camera time constant. The results show that the system is applicable to real-time detections.
In Part III, the PV plant defect information pattern was created. The PV module with defects was labeled during detection, and the defects in the power plant PV module and the positions thereof were obtained, which would be favorable for PV plant maintenance.

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