Abstract
Defects in main electrical equipment can potentially cause power grid collapses, leading to catastrophic risks and immense pressure on dispatch operations. We adopt a Knowledge Graph Completion (KGC) approach to diagnose defects in main electrical equipment. The conventional approach for knowledge graph completion involves randomly initializing the feature representation of nodes and edges. In the context of main electrical equipment, we propose an innovative method based on ChatGPT to generate a corpus for fine-tuning the pre-trained model. We then employ the pre-trained model as a feature extractor for nodes and edges, enhancing the relationships between nodes and improving the initial embedding quality of both nodes and edges. Our pre-trained model fine-tuning process can be efficiently executed on a CPU with less than 10% of the parameters required for fine-tuning the entire pre-trained model. Our experimental results show that our model effectively improves the performance of KGC in diagnosing defects in main electrical equipment.
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This work is supported by the Research Funds from State Grid Fujian (SGFJDK00SZJS2200162).
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Huang, J. et al. (2023). Improve Knowledge Graph Completion for Diagnosing Defects in Main Electrical Equipment. In: Huang, DS., Premaratne, P., Jin, B., Qu, B., Jo, KH., Hussain, A. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2023. Lecture Notes in Computer Science(), vol 14090. Springer, Singapore. https://doi.org/10.1007/978-981-99-4761-4_62
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DOI: https://doi.org/10.1007/978-981-99-4761-4_62
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