Analysis and Design of Power System Transformer Standard Based on Knowledge Graph

Zhou, Yang; Lin, Zhengping; La, Yuan; Huang, Junkai; Wang, Xin

doi:10.4108/eetsis.v9i6.2642

Cited by 6 publications

(4 citation statements)

References 30 publications

(25 reference statements)

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“…Graph data, utilizing nodes and edges to represent entities and their relationships, is a powerful structure for modeling complex real-world data [1,2], including social networks [3] and academic citation networks [4,5]. Computing the embedding vectors of the nodes of the graph in the low-dimensional space is an important work.…”

Section: Introductionmentioning

confidence: 99%

MIED : An Improved Graph Neural Network for Node Embedding in Heterogeneous Graphs

Ni,

Song,

Wang

et al. 2023

ICST Transactions on Scalable Information Systems

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This paper proposes a Metapath-Infused Exponential Decay graph neural network (MIED) approach for node embedding in heterogeneous graphs. It is designed to address limitations in existing methods, which usually lose the graph information during feature alignment and ignore the different importance of nodes during metapath aggregation. Firstly, graph convolutional network (GCN) is applied on the subgraphs, which is derived from the original graph with given metapaths to transform node features. Secondly, an exponential decay encoder (EDE) is designed, in which the influence of nodes on starting point decays exponentially with a fixed parameter as they move farther away from it. Thirdly, a set of experiments is conducted on two selected datasets of heterogeneous graphs, i.e., IMDb and DBLP, for comparison purposes. The results show that MIED outperforms selected approaches, e.g., GAT, HAN, MAGNN, etc. Thus, our approach is proven to be able to take full advantage of graph information considering node weights based on distance aspects. Finally, relevant parameters are analyzed and the recommended hyperparameter setting is given.

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Section: Introductionmentioning

confidence: 99%

MIED : An Improved Graph Neural Network for Node Embedding in Heterogeneous Graphs

Ni,

Song,

Wang

et al. 2023

ICST Transactions on Scalable Information Systems

View full text Add to dashboard Cite

show abstract

“…Motivated by the development of IoT networks, the use of digital technologies has enabled the smart grid to become a modernized electricity grid that enhances reliability, efficiency, and flexibility [12][13][14]. It comprises an interconnected network of electricity generation, transmission, and distribution systems that communicate with each other and electricity consumers in real-time.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis and Research of Knowledge Sharing System for Power Grid Networks

Zhou

Shi

Yang

et al. 2023

EAI Endorsed Scal Inf Syst

Self Cite

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Knowledge sharing is a critical aspect of machine learning and knowledge management, which also plays an important role in regulating the power grid networks. Hence, it is important to investigate the performance of knowledge sharing in the power grid networks. Motivated by this, we firstly investigate a typical power grid network with a knowledge sharing node, where the transmit power of users is constrained by the knowledge sharing node. We then measure the system performance by evaluating the system outage probability (OP), where the analytical expression of OP is derived in detail. Finally, we present some simulation and numerical results on the OP for the considered power grid networks with knowledge sharing, in order to verify the proposed studies on the OP. In particular, these results show that the knowledge sharing can help enhance the system OP performance efficiently.

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“…Real-time voltage computing systems are systems that are used to monitor and analyze voltage data in real-time. The combination of these two technologies has enabled the development of efficient and effective real-time voltage computing systems [24][25][26]. One study explored the use of flink in a realtime voltage computing system for the purpose of voltage quality assessment, where an architecture that utilized flink to process and analyze voltage data was designed from multiple sources in real-time.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Flink Framework Aided Real-Time Voltage Computing Systems in Autonomous and Controllable Environments

Yang

Huangfu

Wang

et al. 2023

EAI Endorsed Scal Inf Syst

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Motivated by the progress in artificial intelligence such as deep learning and IoT networks, this paper presents an intelligent flink framework for real-time voltage computing systems in autonomous and controllable environments. The proposed framework employs machine learning algorithms to predict voltage values and adjust them in real-time to ensure the optimal performance of the power grid. The system is designed to be autonomous and controllable, enabling it to adapt to changing conditions and optimize its operation without human intervention. The paper also presents experimental results that demonstrate the effectiveness of the proposed framework in improving the accuracy and efficiency of voltage computing systems. Simulation results are provided to verify that the proposed intelligent flink framework can work well for real-time voltage computing systems in autonomous and controllable environments, compared with the conventional DRL and cross-entropy methods, in terms of convergence rate and estimation result. Overall, the intelligent flink framework presented in this paper has the potential to significantly improve the performance and reliability of power grids, leading to more efficient and sustainable energy systems.

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Analysis and Design of Power System Transformer Standard Based on Knowledge Graph

Cited by 6 publications

References 30 publications

MIED : An Improved Graph Neural Network for Node Embedding in Heterogeneous Graphs

MIED : An Improved Graph Neural Network for Node Embedding in Heterogeneous Graphs

Performance Analysis and Research of Knowledge Sharing System for Power Grid Networks

Intelligent Flink Framework Aided Real-Time Voltage Computing Systems in Autonomous and Controllable Environments

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