基于节点日负荷曲线的深度嵌入式聚类及其改进方法对比研究
作者:
作者单位:

(河海大学能源与电气学院,江苏 南京 211100)

作者简介:

通讯作者:

陈嘉雯(1997—),女,硕士研究生,主要从事人工智能算法在负荷建模中的应用研究;E?mail:506137005@qq.com

中图分类号:

TM712

基金项目:

国家自然科学基金(51837004)


Comparative study on deep embedded clustering and its improved methods based on node daily load curve
Author:
Affiliation:

(College of Energy and Elctrical Engineering, Hohai University, Nanjing 211100,China)

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    基于日负荷曲线的负荷节点分类是负荷建模的重要环节,详略得当的分类结果保留了负荷节点的内在特性,可提升电力系统仿真计算的效率。当前基于人工智能的节点聚类方法进展迅速,然而总体上针对数据深层特征提取的适应性仍存在不足。采用了基于改进的深度嵌入式算法的日负荷曲线聚类方法,利用神经网络可有效提取数据的深层特征的能力。进而,提出一种先升维后聚类的改进方法,通过算例对比分析,验证了本文所提算法的可行性,以及所提升维—重构聚类方法的正确性。

    Abstract:

    Load node classification based on daily load curve is an important part of load modeling. The detailed and appropriate classification results retain the internal characteristics of load nodes and can improve the efficiency of power system simulation calculation. At present, the node clustering method based on artificial intelligence has made rapid progress. However, the overall adaptability to data deep feature extraction is still insufficient. This paper presents the daily load curve clustering method based on the improved deep embedded algorithm, which uses the ability of neural network to effectively extract the deep features of the data. Then, an improved method of increasing the dimension first and then clustering is proposed. Through the comparative analysis of numerical examples, the feasibility of the proposed algorithm and the correctness of the improved dimension reconstruction clustering method are verified.

    参考文献
    相似文献
    引证文献
引用本文

陈 谦,陈嘉雯,王苏颖,史 锐.基于节点日负荷曲线的深度嵌入式聚类及其改进方法对比研究[J].电力科学与技术学报,2023,(1):130-137. CHEN Qian, CHEN Jiawen, WANG Suying, SHI Rui. Comparative study on deep embedded clustering and its improved methods based on node daily load curve[J]. Journal of Electric Power Science and Technology,2023,(1):130-137.

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:
  • 最后修改日期:
  • 录用日期:
  • 在线发布日期: 2023-04-10
  • 出版日期: