Construction and optimization technology of power grid dispatching knowledge graph based on multi-modal data fusion
DOI:
https://doi.org/10.4108/ew.11262Keywords:
Multi-modal data fusion, Power grid dispatching, Knowledge graph, Distributed storage, Knowledge mappingAbstract
The data in the field of power grid dispatching has multi-modal characteristics such as text, images, and time series. How to deeply integrate these heterogeneous data is a key challenge in building an intelligent knowledge graph. The research aims to construct and optimize a power grid dispatching knowledge graph based on multi-modal data fusion. To this end, a unified framework integrating text, images, and time series data is proposed. This framework first uses joint extraction technology to extract entity relationships from text; subsequently, an improved RESCAL model (fusing L2 regularization and data augmentation) is introduced for knowledge embedding to enhance generalization ability; for the multi-modal association problem, a cross-modal transformation network (CMTN) is designed to map different modal data to a shared semantic space to achieve precise retrieval. At the system level, the perceptual hashing algorithm is integrated for fast similarity matching, and a distributed storage architecture is adopted to ensure the efficient processing and dynamic update of massive multi-modal data. Experimental results show that the joint extraction technique achieves high accuracy and recall in entity recognition and relationship extraction tasks, with F1-scores of 0.82 and 0.86 on the PowerGraph and OmniCorpus datasets, respectively. The CMTN exhibits superior performance in cross-modal retrieval, with mean inter-modal similarities of 0.72 and 0.75 and Top-1 alignment accuracies of 0.85 and 0.88 on the two datasets. The constructed knowledge graph effectively supports intelligent power grid dispatching by accurately representing and managing complex multi-modal data. The research provides an effective solution for enhancing the representation, association, and update capabilities of grid dispatching knowledge through multi-modal data fusion technology. At the same time, it can focus on the lightweighting and real-time optimization of the model to promote the integration of this technology into online intelligent dispatching systems.
Downloads
References
[1.] Sahu A, Davis K. Inferring adversarial behaviour in cyber‐physical power systems using a Bayes-ian attack graph approach[J]. IET Cyber‐Physical Systems: Theory & Applications, 2023, 8(2): 91-108.
[2.] Long X M, Chen Y J, Zhou J. Development of AR Experiment on Electric-Thermal Effect by Open Framework with Simulation-Based Asset and User-Defined Input. Artificial Intelligence and Applications. 2023, 1(1): 52-57.
[3.] Kosasih E E, Margaroli F, Gelli S. Towards knowledge graph reasoning for supply chain risk management using graph neural networks. International Journal of Production Research, 2024, 62(15): 5596-5612.
[4.] Alqahtani H, Kumar G. Deep learning-based in-trusion detection system for in-vehicle networks with knowledge graph and statistical methods. International Journal of Machine Learning and Cybernetics, 2025, 16(5): 3539-3555.
[5.] Xiao N, Peng B, Li X. Research on the construc-tion and implementation of power grid fault handling knowledge graphs. Energy Reports, 2023, 9(4): 182-189.
[6.] Ji Z, Wang X, Zhang J. Construction and appli-cation of knowledge graph for grid dispatch fault handling based on pre-trained model. Global Energy Interconnection, 2023, 6(4): 493-504.
[7.] Liu Z, Yao N, Fan Q. Reasoning simulation of substation power grid fault events based on knowledge map technology. 5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023). SPIE, 2023, 12748(4): 918-923.
[8.] Wang Q, Mao Z, Wang B. Knowledge graph em-bedding: A survey of approaches and applica-tions. IEEE transactions on knowledge and data engineering, 2017, 29(12): 2724-2743.
[9.] Verma S, Bhatia R, Harit S. Scholarly knowledge graphs through structuring scholarly communi-cation: a review. Complex & intelligent systems, 2023, 9(1): 1059-1095.
[10.] Fujita T. Hypergraph and superhypergraph ap-proaches in electronics: A hierarchical frame-work for modeling power-grid hypernetworks and superhypernetworks. Journal of Energy Re-search and Reviews, 2025, 17(6): 102-136.
[11.] Li M, Yang M, Yu Y. Short-term wind power forecast based on continuous conditional ran-dom field. IEEE Transactions on Power Systems, 2023, 39(1): 2185-2197.
[12.] Takiddin A, Atat R, Ismail M. Generalized graph neural network-based detection of false data injection attacks in smart grids. IEEE Transactions on Emerging Topics in Computa-tional Intelligence, 2023, 7(3): 618-630.
[13.] Subagdja B, Shanthoshigaa D, Wang Z. Ma-chine learning for refining knowledge graphs: A survey. ACM Computing Surveys, 2024, 56(6): 1-38.
[14.] Madabhushi S, Dewri R. A survey of anomaly detection methods for power grids. International Journal of Information Security, 2023, 22(6): 1799-1832.
[15.] Marzbani F, Abdelfatah A. Economic dispatch optimization strategies and problem formula-tion: A comprehensive review. Energies, 2024, 17(3): 550-562.
[16.] Tian Y, Wu Y, Lu J. Improved static capacity configuration for hybrid power supply scheme with energy storage based on NSGA in Tokamak. IEEE Transactions on Industry Applications, 2023, 60(2): 2914-2924.
[17.] Contreras-Rodríguez J A, Có rdova-Esparza D M, Saavedra-Leos M Z. Machine learning and miRNAs as potential biomarkers of breast can-cer: a systematic review of classification meth-ods. Applied Sciences, 2023, 13(14): 8257-8273.
[18.] Yang J, Liu J, Qiu G. A spatio-temporality-enabled parallel multi-agent-based real-time dy-namic dispatch for hydro-PV-PHS integrated power system. Energy, 2023, 278(1): 127915-12730.
[19.] Liu P, Qian L, Zhao X. The construction of knowledge graphs in the aviation assembly do-main based on a joint knowledge extraction model. IEEE Access, 2023, 11: 26483-26495.
[20.] Shang Y, Fu K, Zhang Z. MERGE: A Modal Equilibrium Relational Graph Framework for Multi-Modal Knowledge Graph Completion Sensors, 2024, 24(23): 7605-7619.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Wei Li, Chao Hu, Siqi Shen, Zhangguo Chen
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This is an open-access article distributed under the terms of the Creative Commons Attribution CC BY 4.0 license, which permits unlimited use, distribution, and reproduction in any medium so long as the original work is properly cited.
