基于深度学习的配电网安全态势感知研究
Research on Distribution Network Security Situation Awareness Based on Deep Learning
管委玲 1何金波 1陈潞 1倪烨 1杨军 1裘炜望 1李赟2
作者信息
- 1. 国网浙江省电力有限公司 台州市路桥区供电公司,浙江 台州 318050
- 2. 浙江省台州市宏泰供电服务有限公司路桥分公司,浙江 台州 318050
- 折叠
摘要
配电网作为电力系统的关键环节,有必要识别配电网潜在危害,避免失稳.为了解决数据中噪声干扰的问题并提高态势预测准确性,提出了一种基于深度学习的配电网安全态势感知方法.首先,采集配电网运行量,利用奇异值分解(singular value decomposition,SVD)对运行量进行降噪;其次,分析运行量与安全态势的关系,采用评估值指标评估配电网态势;最后,利用注意力时域卷积网络(temporal convolution network-attention mechanism,TCN-AM)对降噪后的输入数据预测得出态势评估值,预测配电网潜在危害,若失稳,则发出预警信号.通过对IEEE 33节点系统和实际配电网系统仿真可知,TCN-AM预测效果好,且进行降噪处理后预测准确性有所提高,能够在满足预警条件后,发出相应的预警信号.所提方法在降噪处理后能够更准确地实现配电网的安全态势感知.
Abstract
As the key link of the power system,it is necessary to identify the potential hazards of the distribution network and avoid the instability.To solve the problem of noise interference and improve the accuracy of situa-tion prediction,a security situation awareness method of the distribution network based on deep learning is stud-ied.Firstly,The operating quantity of the distribution network is collected and denoised by singular value de-composition(SVD).Secondly,the relationship between operation volume and security situation is analyzed,and the situation of the distribution network is evaluated by evaluating the value index.Finally,the temporal convolution network-attention mechanism(TCN-AM)is used to predict the input data after noise reduction to obtain the situation assessment value,predict the potential hazards of the distribution Network,and send an ear-ly warning signal if it is unstable.Through the simulation of the IEEE 33-bus system and actual distribution net-work system,TCN-AM has a good prediction effect,and the prediction accuracy is improved after noise reduc-tion processing,and the corresponding early warning signal can be issued after meeting the early warning condi-tions.The proposed method can realize the security situation awareness of the distribution network more accu-rately after noise reduction.
关键词
配电网/安全态势感知/注意力时域卷积网络/噪声Key words
distribution network/security situation awareness/TCN-AM/noise引用本文复制引用
基金项目
国家自然科学基金(52007103)
国家电网浙江省电力公司台州市路桥区供电公司项目(5211T223000M)
出版年
2024
国家科技期刊平台
NSTL
万方数据