Transformers are key hub equipment in the power system, and their operation reliability is directly related to the stability of the power grid. In order to ensure the safe and stable operation of the transformer, moni...
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The development of new types of power systems poses several key challenges for super-large-scale online analysis and optimization. These challenges include data merging, computation performance, and operation optimiza...
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Federated learning is a promising paradigm that utilizes widely distributed devices to jointly train a machine learning model while maintaining privacy. However, when oriented to distributed resource-constrained edge ...
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In the operational landscape of the electric power industry, a wealth of events, operations, and fault reports generates substantial textual data. The application of frame semantic parsing to the power grid system pre...
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Digital empowerment of China's power energy sector is a key factor in increasing its economic and social benefits, and named entity recognition technology is the most fundamental and core task of information extra...
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Risk management and control of on-site power operations is an important link in the power production process. Traditional methods that rely on manual execution of safety rules lack proactive prevention and control cap...
ISBN:
(纸本)9798400716775
Risk management and control of on-site power operations is an important link in the power production process. Traditional methods that rely on manual execution of safety rules lack proactive prevention and control capabilities, and insufficient efficiency leads to an increasing frequency of safety issues in power production. Therefore, it is urgent to adopt intelligent methods to improve the level of safety risk management and control. In response to this issue, this paper proposes a multimodal causal knowledge graph based auxiliary decision-making method for safety risk management in power field operations. Based on the characteristics and requirements of safety risk management for on-site power operations, first obtain multimodal data related to safety risk management for on-site power operations, including video, photo, and text information collected by end devices, as well as historical archive data in various system databases. Then, based on historical risk management rules, the causal relationships of the core elements of on-site work safety issues are sorted out, and the data involved in the multiple types of antecedent influencing factors of historical faults and safety accidents are taken as the causes, and the faults and safety issues are taken as the results, forming a complete causal knowledge sample and constructing a causal effect estimation learning model. This model can output the safety risks of on-site power operations, forming a causal knowledge graph to provide auxiliary support for early warning and pre control decision-making of safety risks in on-site power operations.
In recent years, with the rapid development of the Internet of Things, the Internet, and social networks, the storage of data in the network is growing at an explosive rate and is becoming more and more closely relate...
ISBN:
(纸本)9798400716669
In recent years, with the rapid development of the Internet of Things, the Internet, and social networks, the storage of data in the network is growing at an explosive rate and is becoming more and more closely related to the real natural world. Driven by large-scale data mining and machine learning applications, distributed graph computing models that use graph data structures to describe data and relationships between data have been increasingly widely used. Therefore, this paper studies the optimization of communication mechanisms based on a distributed graph computing environment. Firstly, a BSP model based on a pure message-passing communication mechanism of a distributed graph computing system is established. Secondly, the optimization model is evaluated from two aspects: data communication and convergence condition judgment. Finally, large-scale data sets are used to test and evaluate performance optimization. The results show that this method can greatly improve the efficiency of graph parallel computing.
Transformer is the key apparatus of the power system, and its operational reliability is directly related to the stability of the power grid. Accurately predicting the future health state of transformers can reserve e...
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The rapid integration of distributed renewable energy sources and the diversification of loads have precipitated profound changes in the configuration and operation of power distribution networks in the 21st century, ...
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ISBN:
(数字)9798350375855
ISBN:
(纸本)9798350375862
The rapid integration of distributed renewable energy sources and the diversification of loads have precipitated profound changes in the configuration and operation of power distribution networks in the 21st century, underscoring the necessity for accurate and standardized power grid topology model data. This manuscript introduces a novel graph contrastive learning-based approach for the representation of node features in distribution networks, specifically targeting the alignment of identical nodes across different Common Information Model (CIM) documents. By combining self-supervised learning with graph neural networks, a feature extraction framework that integrates topological structural features and attribute features of the distribution network is proposed in the paper. Besides, the study designs a graph view generation strategy considering distribution network characteristics. The proposed method is validated by an 11-feeder distribution network dataset, and the results show the effectivity of the graph contrastive learning.
Reliable short-term net load forecasting (STNLF) is important for the stable operation of power systems and assists utilities in unit commitment and demand side management. With the increasing penetration of invisible...
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