基于循环神经网络的配网用户侧负荷态势感知方法
Recurrent Neural Net works-based User Side Load Situation Perception of Distribution Networks
李扬帆1
作者信息
- 1. 国网绵阳供电公司,四川 绵阳 621000
- 折叠
摘要
针对配网用户侧负荷分析中的MAPE误差问题,提出基于循环神经网络的负荷态势感知方法.利用降噪自编码器处理历史负荷数据;通过奇异谱分析获取低频和高频分量序列,描述负荷态势特点;以循环神经网络为核心,结合长短期记忆网络构建负荷态势感知模型,分别预测低频和高频分量序列;利用改进鲸鱼算法优化模型参数,以获得更准确的预测结果.实验结果表明,新方法得出的感知结果的MAPE值低于5％,满足配网用户侧负荷态势感知要求.
Abstract
This paper proposes a load situation awareness method based on recurrent neural networks to address the issue of MAPE errors in user side load analysis of distribution networks.The method uses denoising autoencoder to process his-torical load data,obtains low-frequency and high-frequency component sequences through singular spectrum analysis to describe the characteristics of load situation,constructs load situation perception model by using recurrent neural networks as the core and long short-term memory networks as assistance to predict independently low-frequency and high-frequency component sequences,and utilizes modified whale algorithm to optimize model parameters and improve prediction accura-cy.The experimental results show that the MAPE value of the perception results obtained by the new method is less than 5％,which meets the requirements of load situation perception for distribution network users.
关键词
循环神经网络/配网/用户侧/电力负荷/态势感知Key words
recurrent neural network/distribution network/user side/electricity load/situation awareness引用本文复制引用
出版年
2024
NETL
NSTL
万方数据