Linearizing Power Flow Model: A Hybrid Physical Model-Driven and Data-Driven Approach

被引:44
|
作者
Tan, Yi [1 ]
Chen, Yuanyang [1 ]
Li, Yong [1 ]
Cao, Yijia [1 ]
机构
[1] Hunan Univ, Coll Elect & Informat Engn, Changsha 410082, Hunan, Peoples R China
基金
中国国家自然科学基金;
关键词
Load flow; Reactive power; Mathematical model; Analytical models; Hybrid power systems; Data models; Linear model; data-driven modelling; hybrid modelling; power flow; physical model-driven approach;
D O I
10.1109/TPWRS.2020.2975455
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Linear power flow model is advantageous for the fast operational analysis and the efficient optimization of the power systems. In this letter, we propose a hybrid physical model-driven and data-driven approach for linearizing power flow model. In this proposed approach, the linear power flow model contains two parts, i.e., the existing physical-equation-based linear power flow model and the linearized error model. The linearized errors are obtained by the partial least squares regression based data-driven approach. The proposed linear power flow model can retain the useful inherent information from the physical model and utilize the ability of data analysis to extract the inexplicit linear relationship. Simulations on the four test systems have validated that the proposed hybrid linear model exhibits a much better performance on the branch power flow calculation than other linear power flow models.
引用
收藏
页码:2475 / 2478
页数:4
相关论文
共 50 条
  • [1] A Hybrid Model-Driven and Data-Driven Approach for Saturation Correction of Current Transformer
    Zhang, Yubo
    Yang, Songhao
    Hao, Zhiguo
    Lin, Zexuan
    Liu, Zhiyuan
    [J]. 2021 IEEE POWER & ENERGY SOCIETY GENERAL MEETING (PESGM), 2021,
  • [2] Hybrid model-driven and data-driven approach for the health assessment of axial piston pumps
    Chao, Qun
    Xu, Zi
    Shao, Yuechen
    Tao, Jianfeng
    Liu, Chengliang
    Ding, Shuo
    [J]. INTERNATIONAL JOURNAL OF HYDROMECHATRONICS, 2023, 6 (01) : 76 - 92
  • [3] Hybrid Model-driven and Data-driven Approach to Price Forecasting in Bilateral Contract Electricity Markets
    Li, Yapeng
    Han, Xu
    Yu, Xuguang
    Cheng, Chuntian
    Liu, Benxi
    Cai, Huaxiang
    [J]. Dianli Xitong Zidonghua/Automation of Electric Power Systems, 2022, 46 (18): : 179 - 189
  • [4] An Overview of Data-Driven and Model-Driven Based Prognostics Techniques for Power Modules
    Halim, M. H. Abdul
    Buniyamin, N.
    Naoe, N.
    Rosman, M. S.
    [J]. 2018 4TH INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONICS AND SYSTEM ENGINEERING (ICEESE), 2018, : 34 - 39
  • [5] Data-Driven vs Model-Driven Imitative Learning
    Tembine, Hamidou
    [J]. 2017 6TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS (DDCLS), 2017, : 22 - 29
  • [6] Hyperspectral Image Denoising: From Model-Driven, Data-Driven, to Model-Data-Driven
    Zhang, Qiang
    Zheng, Yaming
    Yuan, Qiangqiang
    Song, Meiping
    Yu, Haoyang
    Xiao, Yi
    [J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2023, : 1 - 21
  • [7] Steady-state Security Region Boundary Modification Model: A Hybrid Physical Model-Driven and Data -Driven Approach
    Ren, Junzhi
    Zeng, Yuan
    Qin, Chao
    Li, Bao
    Wang, Ziqiang
    Yuan, Quan
    [J]. 2023 IEEE POWER & ENERGY SOCIETY GENERAL MEETING, PESGM, 2023,
  • [8] Physical-Model-Aided Data-Driven Linear Power Flow Model: An Approach to Address Missing Training Data
    Shao, Zhentong
    Zhai, Qiaozhu
    Guan, Xiaohong
    [J]. IEEE TRANSACTIONS ON POWER SYSTEMS, 2023, 38 (03) : 2970 - 2973
  • [9] Lateral Maneuver Discrimination for Hypersonic Glide Vehicles: A Hybrid Approach Combining Model-Driven and Data-Driven Methods
    Xu, Hong
    Liu, Yajian
    Xing, Yizhou
    Ren, Aifeng
    Quan, Yinghui
    [J]. IEEE SENSORS JOURNAL, 2024, 24 (07) : 11425 - 11437
  • [10] An investigation on the coupling of data-driven computing and model-driven computing
    Yang, Jie
    Huang, Wei
    Huang, Qun
    Hu, Heng
    [J]. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, 2022, 393