Intelligent Filling Method of Power Grid Working Ticket Based on Historical Ticket Knowledge Base

An, Zhongfu; Yi, Mancheng; Liu, Jing; Li, Ying; Zheng, Peng; Yu, Sifan; Liu, Jianxin; Huang, Wei-Rong; Fang, Chunhua

doi:10.3389/fenrg.2021.813855

Cited by 1 publication

(1 citation statement)

References 14 publications

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“…Liu (Liu et al, 2023) and others proposed a prediction method based on Adaboost ensemble convolutional neural network and bidirectional long short-term memory. In addition, there are many rule-based generation methods, such as that of An et al (2021), who used association rule algorithms to mine historical ticket information and proposed a method to establish a knowledge base of historical tickets. Overall, this rule-based generation method achieves some effectiveness in the region where it is initially constructed, but its cumbersome rule revision process, as well as strong specialization, often necessitates changing the use of the rules when they are migrated elsewhere.…”

Section: Introductionmentioning

confidence: 99%

A fusion topology method for generating new equipment startup schemes for power grids

Meng,

Lu,

Wang

et al. 2024

Front. Energy Res.

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New grid equipment startup programs are widely used in various countries to regulate the commissioning of new equipment; these programs have unique differences in terms of strictness, information asymmetry, and complexity relative to other types of startup programs. With respect to rule-based generation methods, because the method of revising the rules weakens their migration ability, it is difficult to adapt these methods to the status quo of high-speed power grid construction; moreover, most of the current generation methods based on deep learning improve upon the rule-based methods but do not eliminate the rules of the constraints. Therefore, this paper presents a fusion topology for generating a new grid equipment startup scheme, which generates the scheme from end to end. The method utilizes the powerful processing capabilities of the GATv2 model and the ERNIE-GEN model for topology and text, respectively. The device type-based coding strategy and the scheme complexity-based self-attention layer selection strategy are used in the GATv2-based device identification model to address information asymmetry and complexity variability, and the device information modification strategy is applied to solve the strictness variability problem in the ERNIE-GEN-based scheme generation model. Finally, through the testing and verification of field data from four types of new equipment startup schemes in real power grids, it is verified that the method can effectively generate new equipment startup schemes for power grids, and the reasonableness and efficiency of the three strategies are verified through ablation experiments, which verify that the method can effectively generate new equipment startup schemes for power grids that meet the requirements of real power grids.

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Section: Introductionmentioning

confidence: 99%