Research on power dispatching model based on knowledge graph entity extraction task
Abstract This paper proposes an integrated knowledge graph-based power dispatching model for emergency response, combining Markov chain-based text preprocessing, entity-extracted knowledge graph construction, and case-based reasoning optimization - a novel approach that enhances both real-time decis...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2025-08-01
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| Series: | Energy Informatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s42162-025-00559-9 |
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| Summary: | Abstract This paper proposes an integrated knowledge graph-based power dispatching model for emergency response, combining Markov chain-based text preprocessing, entity-extracted knowledge graph construction, and case-based reasoning optimization - a novel approach that enhances both real-time decision-making and system security. First, a Markov chain-based method effectively removes redundant information from power anomaly event texts, improving entity extraction accuracy. Subsequently, a knowledge graph is constructed to precisely identify key entities, enabling the creation of a structured power emergency plan database. Finally, case-based reasoning matches real-time anomalies with historical cases, facilitating the rapid generation of optimal dispatching schemes. The experiments demonstrate that the proposed model achieves high efficiency (with an average dispatching time < 50 s) and reliability (exhibiting a failure blowout rate below 0.1%), thereby significantly improving power grid safety. The proposed framework advances intelligent power system dispatching by integrating text analytics, knowledge representation, and adaptive reasoning. |
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| ISSN: | 2520-8942 |