Construction of a power quality and electrical equipment knowledge graph for equipment status assessment

Abstract

In modern power systems, the condition of electrical equipment is vital for maintaining power grid stability and efficiency. This paper presents the construction of a comprehensive knowledge graph for power quality and electrical equipment, derived from Chinese web sources. This knowledge graph facilitates real-time monitoring, predictive maintenance, and informed decision-making for electrical equipment management. The study covers core technologies, including data acquisition through web crawling using Selenium, entity recognition using BiLSTM-CRF, and relation extraction using R-BERT. The practical architecture includes ontology design, data pre-processing, entity recognition, relation extraction, and knowledge graph construction. SPARQL queries enable complex analysis, and applications support power quality monitoring and equipment health assessment. The knowledge graph serves as a foundation for understanding power quality and equipment status in a Chinese context, emphasizing the relationship between power quality and equipment failure. Ongoing efforts will expand and refine the graph to enhance its value for power qualitybased equipment status assessment.