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.
Against the backdrop of the widespread application of advanced metering systems and power Internet of Things technology, the rapid growth of electricity big data and the integration of diverse heterogeneous characteristics from various sources present unprecedented complexity. A precise understanding of user electricity consumption behavior and load characteristics holds significant importance for achieving energy efficiency improvements and personalized services. This paper aims to construct user electricity load classification profiles through multidimensional feature analysis using a non-intrusive load disaggregation method. It utilizes daily average power consumption (P), daily average operating duration (T), and daily average activation count (0) as key feature dimensions to establish the PTO model. By comprehensive assessment, the PTO model reveals characteristics such as energy consumption, operating duration, and frequency of user electricity loads. Experimental validation is conducted using the publicly available REDD dataset.
Power quality data mining is of great potential in both supply-side and demand-side energy management system. In recent decades, with the wide application of flexible AC/DC power grid and grid-connected renewable energy generation, power quality data has been unified as a generalized model for improving power quality. Meanwhile, power quality monitoring system has also been deployed on a large scale. In order to further highlight the availability and usability of power quality data, the paper integrates various types of information to support power quality analysis. A multimodal data system is constructed to process information collected in different forms into a multi-dimensional data model, which can be pretrained to provide integrated features for various power quality analysis tasks. Firstly, the three data types of voltage waveforms, texts and images are embedded through feature extraction, low-dimensional spatial representation and CNNbased representation, respectively. Then all information is fused with the interaction model based on Attention mechanism. The output of the data model can be sent to networks specific to certain downstream tasks.
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