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Construction Method of Knowledge Base for Power Grid-Aided Decision Based on Knowledge Graph

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Recent Developments in Intelligent Computing, Communication and Devices (ICCD 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1185))

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Abstract

Smart grid has become an important direction for the development of the current power industry, and risk management is an important research content of the smart grid. However, the current solutions to power failure still rely on dispatchers with their knowledge and experience. It is of great significance to construct a knowledge base of power dispatching operation for intelligent decision-making support. In this paper, we propose a method of constructing dispatching operation knowledge based on knowledge graph technology to assist the dispatcher in making decisions when encountering the risk of power failure. We use it to analyze the power dispatching knowledge. By knowledge extraction and building knowledge relationship network, we constructed the knowledge graph of power dispatching operation. It provides knowledge support for intelligent power dispatching systems to assist dispatchers in making decisions when power failures occur. It can help dispatchers reduce workload and various work errors while improving quality and efficiency.

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Acknowledgments

This research was funded by Application Research of Artificial Intelligence Technology in Real-time Balance and Risk Analysis of Large Power Grid, the project of Hunan Electric Power Company, State Grid. We would like to thank the referees for their valuable comments and suggestions.

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Correspondence to Bin Chen .

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Chen, Y., Liao, Z., Chen, B., Shi, H., Chen, H. (2021). Construction Method of Knowledge Base for Power Grid-Aided Decision Based on Knowledge Graph. In: WU, C.H., PATNAIK, S., POPENTIU VLÃDICESCU, F., NAKAMATSU, K. (eds) Recent Developments in Intelligent Computing, Communication and Devices. ICCD 2019. Advances in Intelligent Systems and Computing, vol 1185. Springer, Singapore. https://doi.org/10.1007/978-981-15-5887-0_51

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