Off-line tracking of series parameters in distribution systems using AMI data

被引:5
|
作者
Williams, Tess L. [1 ]
Sun, Yannan [1 ]
Schneider, Kevin [1 ]
机构
[1] Pacific NW Natl Lab, Richland, WA 99352 USA
关键词
Distribution system analysis; Parameter estimation; State estimation; Change detection; STATE ESTIMATION; IDENTIFICATION; ERRORS;
D O I
10.1016/j.epsr.2015.12.036
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
In the past, electric distribution systems have lacked measurement points, and equipment is often operated to its failure point, resulting in customer outages. The widespread deployment of sensors improves distribution level observability. This paper presents an off-line parameter tracking procedure that leverages the increased deployment of distribution level measurement devices to estimate changes in impedance parameters over time. Parameter tracking enables the discovery of non-diurnal and non-seasonal changes, which can be flagged for investigation. The presented method uses an unbalanced distribution-system state-estimator and a measurement-residual based parameter-estimation procedure. Measurement residuals from multiple measurement snapshots are combined to increase effective local redundancy and improve robustness to measurement noise. The input data used in the experiments Consists of data from devices on the primary distribution system and from customer meters, via an AMI system. Results of simulations on the IEEE 13-Node Test Feeder with 307 measurements and 246 parameters are presented to illustrate the proposed approach applied to changes in series impedance parameters. The proposed approach can detect a 5% change in series resistance elements with 2% measurement error using less than 1 day of measurement snapshots for a single estimate. (C) 2016 Elsevier B.V. All rights reserved.
引用
收藏
页码:205 / 212
页数:8
相关论文
共 50 条
  • [1] Estimation of hydrodynamic parameters for underwater systems using a simple off-line regression method: a case study
    Ranganathan, Thiyagarajan
    Singh, Vijendra
    Thondiyath, Asokan
    JOURNAL OF MARINE SCIENCE AND TECHNOLOGY, 2019, 24 (03) : 968 - 983
  • [2] Estimation of hydrodynamic parameters for underwater systems using a simple off-line regression method: a case study
    Thiyagarajan Ranganathan
    Vijendra Singh
    Asokan Thondiyath
    Journal of Marine Science and Technology, 2019, 24 : 968 - 983
  • [3] Augmented State Estimation of Line Parameters in Active Power Distribution Systems With Phasor Measurement Units
    Wang, Yubin
    Xia, Mingchao
    Yang, Qiang
    Song, Yuguang
    Chen, Qifang
    Chen, Yuanyi
    IEEE TRANSACTIONS ON POWER DELIVERY, 2022, 37 (05) : 3835 - 3845
  • [4] Selection of model parameters for off-line parameter estimation
    Li, RJ
    Henson, MA
    Kurtz, MJ
    IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, 2004, 12 (03) : 402 - 412
  • [5] State Estimation for Unbalanced Electric Power Distribution Systems Using AMI Data
    Gao, Yuanqi
    Yu, Nanpeng
    2017 IEEE POWER & ENERGY SOCIETY INNOVATIVE SMART GRID TECHNOLOGIES CONFERENCE (ISGT), 2017,
  • [6] Distribution System State Estimation Using AMI Data
    Baran, Mesut
    McDermott, T. E.
    2009 IEEE/PES POWER SYSTEMS CONFERENCE AND EXPOSITION, VOLS 1-3, 2009, : 486 - +
  • [7] Data-driven Robust State Estimation Through Off-line Learning and On-line Matching
    Chen, Yanbo
    Chen, Hao
    Jiao, Yang
    Ma, Jin
    Lin, Yuzhang
    JOURNAL OF MODERN POWER SYSTEMS AND CLEAN ENERGY, 2021, 9 (04) : 897 - 909
  • [8] Generation of Duplicated Off-Line Signature Images for Verification Systems
    Diaz, Moises
    Ferrer, Miguel A.
    Eskander, George S.
    Sabourin, Robert
    IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2017, 39 (05) : 951 - 964
  • [9] Distribution line parameter estimation driven by probabilistic data fusion of D-PMU and AMI
    Xiao, Mengmeng
    Xie, Wei
    Fang, Chen
    Wang, Shaorong
    Li, Yan
    Liu, Shu
    Ullah, Zia
    Zheng, Xuejun
    Arghandeh, Reza
    IET GENERATION TRANSMISSION & DISTRIBUTION, 2021, 15 (20) : 2883 - 2892
  • [10] Estimating Power Flow Directions Using Off-Line PF Analysis and Artificial Neural Networks
    Al-Roomi, Ali R.
    El-Hawary, Mohamed E.
    2019 IEEE ELECTRICAL POWER AND ENERGY CONFERENCE (EPEC), 2019,