Topological Relationship Detection of Substation Primary Wiring Diagram Based on Deep Learning
At present,the substation wiring diagram in the dispatching and control system is drawn and input man-ually.Due to the complex graphics style and numerous equipment types,it is very easy to have problems such as miss-ing components,association errors,virtual connection of connecting lines,etc.To address these problems,a recognition algorithm based on deep learning and improved progressive probabilistic Hough transform(PPHT)is proposed in this paper.Firstly,the object detection method based on improvedFaster-RCNN was used to identify the electrical compo-nents and text box to obtain their location information.Then,the improved PPHT was adopted to detect busbar and connecting line based.Finally,the mutual relationship of all elements was ascertained after determination of the dis-tance threshold and graph structure.The experimental results show that the proposed method has achieved 100%ac-curacy in busbar detection.Besides,the accuracy,recall and comprehensive F1 values of the topology relationship de-tection of substation primary wiring diagram are 89.8%,88.6%and 89.2%,respectively.Compared with the Faster-RCNN and YOLOv5 methods,the detection performance is improved significantly,which indicates that the proposed method can satisfy the requirement of automatic identification of substation primary wiring diagram.
NETL
NSTL
万方数据
维普
