Extract-Transform-Load Process Leveraging Computer Vision to Enable Computer Modelling of Smart Grids

Abstract

A novel extract-transform-load methodology applying computer vision to build electrically equivalent models of electricity grids (i.e., digital twins of smart grids) is proposed. Here, we standardise information from electricity grids in various formats into a single visual input for object and text recognition, transformation and loading into a computer simulator. The proposed method is tested using state of the art computer vision algorithms: YOLOv8 for object detection (re-trained using two generic datasets of geometric figures) and PaddleOCR for text recognition. Validation is performed using two real electricity distribution networks in the centre-south region of Colombia, with a global object detection accuracy of up to 80.4%. Initial results suggest that the proposed method significantly reduces human effort compared to two classic approaches. The benefits and shortcomings of the proposed method are discussed together with future work opportunities.Index Terms-smart grids, computer vision, extract transform load, digital twins, distribution networks• Presenting and testing the application of state of the art computer vision algorithms for object detection and text recognition to build computer models of electrical distribution grids from archived electrical design plans/documents.