Finding and diagnosing problems in the operation of substation equipment in time is one of the key means to ensure the safe operation of power grids. On the basis of the research of deep network and knowledge graph, this paper proposes an intelligent fault diagnosis method that correlates multi-modal information of substation equipment. Deep learning technology and knowledge graph method are used to extract and fuse knowledge from the collected multi-modal data, and a semantic knowledge graph of multi-modal information fusion is constructed. YOLOv4 algorithm is used to cluster fault samples and extract prior bounding box parameters. The multi-modal knowledge map and YOLOv4 visual inspection are combined together and applied to the substation scene to realize the autonomous early warning and diagnosis of the substation equipment. Experiments show that the model can achieve the goal of intelligent decision-making of fault diagnosis, thereby improving the efficiency of daily operation, maintenance and management of power grid.

Based on an in-depth analysis of the digital design needs of power grid enterprises and power design enterprises, a digital design technological system was established for promoting the development of digital design technology in China. Rules were formulated for guiding the direction of information construction of power design enterprises so as to realize sharing of design information in the whole life cycle of the power projects and to ensure the certainty, integrity and uniqueness of information. The digital design technology system can be used as platform to improve the labor productivity of design enterprises, ensure the design quality of the finished products, and promote the coordinated development of power transmission & transformation design, power grid construction and management for achieving a win-win situation.