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 quality-based equipment status assessment.
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