基于图自编码器的输电网单线图布局自适应生成方法  

An Adaptive Generation Method for Transmission Network One-line Diagram Layouts Based on Graph Autoencoder

作　　者：盛志文 王镜毓 石东源[1] SHENG Zhiwen;WANG Jingyu;SHI Dongyuan(State Key Laboratory of Advanced Electromagnetic Engineering and Technology(Huazhong University of Science and Technology),Wuhan 430074,Hubei Province,China)

机构地区：[1]强电磁工程与新技术国家重点实验室(华中科技大学),湖北省武汉市430074

出　　处：《中国电机工程学报》2022年第6期2133-2144,共12页Proceedings of the CSEE

摘　　要：输电网单线图被广泛应用于电力系统各类信息的可视化展示,然而针对不同的展示需求往往需要设计专有算法来生成相应风格的单线图,其主要原因是现有自动生成方法难以快速适应场景需求的变化。该文旨在提出一种输电网单线图布局的自动生成方法,可适应较为广泛的布局要求,并实现需求变化后布局的快速重新生成。首先,建立输电网单线图布局的数学模型;其次,分析影响布局易读性的因素,并探讨几种场景下布局质量的量化评价指标;再次,构建一种基于图自编码器的深度学习模型,学习输电网单线图布局的生成模式,并利用学习到的布局特征生成适应给定评价指标要求的优化布局;最后,将该文所提方法用于某省级实际输电网单线图的生成,实验结果表明其生成布局符合所设场景预期,并在生成时间和布局质量等指标上均优于对比算法,验证了该方法快速适应场景需求变化的能力。Transmission grid one-line diagrams are widely used to visualize the information of power systems.However,it is often necessary to design proprietary algorithms to generate one-line diagrams of different styles to meet various presentation demands.The main reason was that existing automatic generation methods could not efficiently adapt to the change in the application scenario.This paper proposed an automated approach to generating transmission grid one-line diagrams.It could adapt to a broader range of layout requirements and allowed rapid regeneration of layouts after changes in demand.To begin with,a mathematical model of the layout of transmission grid one-line diagrams was established in this paper.The factors that affect legibility and the quantitative metrics for evaluating the layout quality were then analyzed.After that,a deep learning model based on graph autoencoders was built to learn the patterns of transmission grid one-line diagrams.The learned patterns were used to generate an optimized layout that could meet the given requirements of the evaluation metrics.Finally,the proposed method was implemented on an actual provincial-level transmission grid.The experimental results show that the generated layout can meet the expectation of the selected scenarios,and is superior to previous algorithms in terms of generation time and layout quality.Thus,the capability of the method in quickly adapting to different application demands is verified.

关 键 词：输电网单线图 图神经网络 电力系统可视化 地理信息系统 自动布局 

分 类 号：TM76[电气工程—电力系统及自动化]