# Integrating Knowledge Graphs into Distribution Grid Decision Support Systems +
## 2 Related Work  
A major challenge associated with the operation and maintenance of power grids is keeping track of its components, as it requires the collection, management and analysis of data in hundreds of thousands of power devices [2]. Given the network nature of the grid, it can be expected that the use of KGs would enhance the efficiency of knowledge retrieval and greatly improve the quality and accuracy of the search results. However, to date, there have been only a few studies that describe domain-specific applications of KGs in power grids. Selected articles published in this area are summarized bellow.  
The use of KGs improved workflow performance, improved data accessibility, and became an essential step towards intelligent engineering and production at Siemens [3]. It helped overcome traditional database challenges, provided an integrated view of data, and improved search functionality and data control.  
KGs have been pivotal in advancing power system management, analysis, and smart grid development. The application of KGs in power system management, such as equipment management, [PERSON] et al. [4], and fault diagnosis and disposal, [PERSON] et al. [5], has been noted for its efficiency in information retrieval, updating, and decision making. For instance, an enterprise-level KG enhanced the management of power equipment assets, studied by [PERSON] et al. [6], while a designed architecture was applied to analyze the energization of the IEEE 118-bus system [7]. All of this demonstrated the effectiveness of graph databases over relational ones. Furthermore, recent studies, like [PERSON] et al. [8], have focused on automatic ontology extraction to build power terminal KGs, facilitating data exchange across various sources. In similar efforts, [PERSON] et al. [9] used ontologies to develop a knowledge management system that can model interactions across multiple domains, which proved beneficial in the building of decision support tools. [PERSON] et al. [2] developed an AI-enhanced labeling method for creating KGs to group power grid equipment with similar characteristics. [PERSON] et al. [10] demonstrated the adaptability of KGs to business needs, enhancing database response time and expansion process. The expansion of smart grids, coupled with the deployment of advanced metering infrastructure and sensors, has led to the generation of large amounts of heterogeneous data. [PERSON] et al. [11] noted that KGs help incorporate additional information and connectivity across devices in smart grids. Addressing the issue of information islands, [PERSON] et al. [12] used KGs for grid equipment considering the multifaceted nature of the equipment and inter-equipment relationships. [PERSON] et al. [13] proposed a multisource information fusion method to efficiently use data from power equipment. Similarly, [PERSON] [14] used KGs to integrate power grid and environmental information for anomaly detection, and [PERSON] et al. [15] developed a dispatch KG for the power grid using NLP and machine learning techniques.  
[PERSON] et al. [16] and [PERSON] et al. [17] also demonstrated innovative KG applications in nuclear power plant fault analysis. [PERSON] et al. [18] and [PERSON] and [PERSON] [19] applied graph-based learning for fault detection in photovoltaic arrays and wind farms, while [PERSON] et al. [20] and [PERSON] and [PERSON] [21] developed KGs for specific fault location and transformer maintenance, respectively. [PERSON] et al. [22] extended the application of KG by creating an electrical equipment fault KG based on operational and maintenance records.  
This contribution introduces an ontology for concepts and relations required to represent a grid topology, its electrical elements, and information describing them. The ontology is then used to construct a KG of a real-world distribution grid. We also show that such a graph can be used to understand the interconnections of the grid and to analyze their changes.