基於深度學習之合成配電網絡建置研究

Synthetic Power Distribution Network Construction Based on Deep Learning Algorithm

林岳鴻(Yue-Hung Lin) ； 林其穎(Chi-Ying Lin)

《結構工程》 37卷4期 (2022/12) Pp. 108-126

https://doi.org/10.6849/SE.202212_37(4).0006

摘要

現今時代處於能源高消耗的處境且加上地球暖化帶來的災害規模日益嚴重，使人們對於公共資產管理、減少能源消耗、預測災害損失更加重視。本研究提出利用深度學習建置合成電力網路，以便未來使用其擬真模型進行電網可靠度分析。本研究利用街景圖配合物體識別及計算交集的方式定位出電線桿之地理資訊，並利用此地理資訊建立其合成電力網路。研究首先使用Mask R-CNN與YOLOv4物體識別模型，進行時長之條件控制訓練，並比較兩種模型的精確度得出適合此研究之方法。接續調整其參數配置，比較不同參數下對於此研究的最佳模型設置。對於區域的研究進行每條街的個別物體識別，並設計兩種排序方式進行電線桿排序，分別為經緯度以及最短路徑，利用兩種排序方式進行基於馬爾科夫隨機場（Markov random field, MRF）和基於方位線（line of bearing, LOB）與基於密度之聚類演算法（Density-based spatial clustering of application with noise, DBCSCAN）兩種方式計算交集，接著進行電線桿之補遺，使其更符合真實電線桿最大容許間距。最後，利用距離迭代的方式合併所有街道並刪除重複預測的電線桿點位，得出四種結果。使用四種結果與真實電線桿地理資訊進行精度比對，選擇出最適者並利用最小生成樹（minimum spanning tree, MST）建立電力網路，建立後分析並比較其性質與真實電力網絡的區別。未來可使用此模型進行改善，使其更符合真實電力網路，並使用其合成電力網絡進行電網可靠度分析、公共資產管理、災害分析等分析。

關鍵字

合成配電網絡 ； 深度學習 ； 物體識別 ； 地理標籤

並列摘要

Global warming has caused high energy consumption and an increasing scale of disasters, which make people draw more attention to public asset management to reduce energy consumption and predict losses caused by disasters. Based on a deep learning based object detection approach, this study develops a synthetic power distribution network that can serve as an alternative to the real power distribution net-work and be used to analyze its reliability. This research uses the street view images to detect utility poles and conduct geo-positioning to locate utility poles on the map. For object detection, the Mask R-CNN and YOLOv4 are trained with controlled du-ration, and then the accuracy of the two models is compared to determine which method is suitable for this research. Second, the model's hyperparameters are adjusted and compared to determine the best model setting for the object detection task in this study. Then the selected model is used to perform the object detection task for each street in the research region. Two sorting methods, namely, the latitude and longitude sorting method and the shortest path sorting method, are proposed to sort the poles for pole geo-positioning and supplementation. With two sorting methods, pole geo-positioning is conducted based on two approaches: The first is the Markov random field (MRF) approach, and the second is the line of bearing (LOB) with density-based spatial clustering of application with noise (DBSCAN). After determining the detected pole location, pole supplementation is conducted to ensure the maximum allowable distance between poles. Third, four sets of results are obtained by merging all streets and removing duplicate poles by means of distance iteration. Finally, four results are compared with the coordinates of real utility poles. The most suitable method for study region is selected to establish the synthetic power distribution network using the minimum spanning tree (MST). In the future, this model can be improved to make it more in line with the real power distribution network, and the synthetic power distribution network can be used for power grid reliability analysis, public asset management, disaster analysis, power demand-supply analysis, etc.

並列關鍵字

synthetic power distribution network ； deep learning ； object detection ； geo-positioning

參考文獻

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