Enhancement of Power Equipment Management Using Knowledge Graph

Abstract

Accurate retrieval of the power equipment information plays an important role in guiding the full-lifecycle management of power system assets. Because of data duplication, database decentralization, weak data relations, and sluggish data updates, the power asset management system eager to adopt a new strategy to avoid the information losses, bias, and improve the data storage efficiency and extraction process. Knowledge graph has been widely developed in large part owing to its schema-less nature. It enables the knowledge graph to grow seamlessly and allows new relations addition and entities insertion when needed. This study proposes an approach for constructing power equipment knowledge graph by merging existing multisource heterogeneous power equipment related data. A graphsearch method to illustrate exhaustive results to the desired information based on the constructed knowledge graph is proposed. A case of a 500 kV station example is then demonstrated to show relevant search results and to explain that the knowledge graph can improve the efficiency of power equipment management.

A survey on the development status and application prospects of knowledge graph in smart grids

“…Wang and Liu [173] utilised KG technology to construct an error recognition model of power equipment defect records. Moreover, Tang et al [11] applied KG into merging heterogeneous data of all the power equipment to enhance the management of power equipment, which could query the basic information, classify relevant information of products, maintain the integrity of information, demonstrate the relationship between products, and achieve real-time updates on the content of any product. Cui [174] introduced KG to integrate the data of the IoT system for detecting the abnormal state of electric device.…”

“…To the best of our knowledge, the KGs provide a flexible way to establish semantic connections and obtain unified semantic-level data service, known as an ontology. The KG4SG is a huge semantic network that merges the multi-source heterogeneous big data, which leverages a new data integration paradigm that is applicable to the next generation of electric power artificial intelligence [11]. Moreover, KG4SG can be widely applied in equipment failure analysis, consumer service, fraud data analysis and other fields.…”

“…To the best of our knowledge, Tang et al. [11] merged existing multi‐source heterogeneous data of power equipment to construct a KG aiming at power asset management. Chun et al.…”

“…The knowledge graph (KG) [10] provides us an effective solution to achieve this goal, which is crucial for facilitating the intelligentization of power grids as well. Taking power equipment management as an example, the KG can improve planning, traceability, production scheduling, order execution, and supplier management of power equipment [11].…”

How to Construct a Power Knowledge Graph with Dispatching Data?

“…ere is little research on knowledge graphs in the field of power grids. Tang [14] merged existing multisource heterogeneous power equipment related data to construct a power equipment knowledge graph to improve the data storage efficiency and extraction process. Given the problem of flattening and efficient utilization of power asset information, Yang [15] proposed the general process of constructing professional knowledge graphs in the power field and proposed a multisource heterogeneous power asset information fusion method based on knowledge fusion.…”