A Real-time Scheme for Validation of an Auto-regressive Time Series Model for Power System Ambient Inter-area Mode Estimation

被引：5
|
作者：
Pai, Gurudatha K. [1 ]
Pierre, John W. [1 ]
机构：
[1] Univ Wyoming, Dept Elect & Comp Engn, Laramie, WY 82072 USA
来源：
2014 47TH HAWAII INTERNATIONAL CONFERENCE ON SYSTEM SCIENCES (HICSS) | 2014年
关键词：
ELECTROMECHANICAL MODES; ONLINE ESTIMATION; PRONY ANALYSIS; IDENTIFICATION; PERFORMANCE;
D O I：
10.1109/HICSS.2014.306
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
A lattice time series model may be used to estimate the inter-area electromechanical modes of a power system from measured synchrophasor data. The accuracy of these estimates is sensitive to the order of the model. This paper describes a methodology for real-time, order-recursive whiteness testing of the prediction errors. This hypothesis testing methodology may be used in conjunction with the lattice mode-meter algorithm to validate the model in real-time. Results are presented on a model of the western North American power system.
引用
收藏
页码：2436 / 2443
页数：8
相关论文
共 50 条
  • [31] Real-time Power System Electromechanical Mode Estimation Implementation and Visualization Utilizing Synchrophasor Data
    Guo, Jiahui
    Liu, Hesen
    Zhou, Dao
    Chai, Jidong
    Zhang, Ye
    Liu, Yilu
    2016 IEEE/PES TRANSMISSION AND DISTRIBUTION CONFERENCE AND EXPOSITION (T&D), 2016,
  • [32] 'In Silico' Testing of a Real-Time PMU-Based Tool for Power System Mode Estimation
    Baudette, Maxime
    Singh, Ravi Shankar
    Peric, Vedran S.
    Lovlund, Stig
    Vanfretti, Luigi
    2016 IEEE POWER AND ENERGY SOCIETY GENERAL MEETING (PESGM), 2016,
  • [33] Real-time estimation of power system frequency by neural network
    Bertoluzzo, M
    Buja, G
    Castellan, S
    Fiorentin, P
    IEEE INTERNATIONAL SYMPOSIUM ON DIAGNOSTICS FOR ELECTRIC MACHINES, POWER ELECTRONICS AND DRIVES, PROCEEDINGS, 2003, : 87 - 92
  • [34] Implementation and Testing of a Real-Time Mode Estimation Algorithm using Ambient PMU Data
    Peric, Vedran S.
    Baudette, Maxime
    Vanfretti, Luigi
    Gjerde, Jan O.
    Lovlund, Stig
    2014 CLEMSON UNIVERSITY POWER SYSTEMS CONFERENCE (PSC), 2014,
  • [35] Wind Power Real-time Prediction Based on Improved Time Series and Grey Model
    Chen, Lanlan
    Pei, Zlxia
    Mao, Anjia
    Liu, Yan
    Wu, Zhuohang
    Hou, Yuzheng
    PROCEEDINGS OF THE 3RD INTERNATIONAL CONFERENCE ON ADVANCES IN ENERGY AND ENVIRONMENTAL SCIENCE 2015, 2015, 31 : 433 - 438
  • [36] An Adaptive Wide-Area Load Shedding Scheme Incorporating Power System Real-Time Limitations
    Shekari, Tohid
    Gholami, Amin
    Aminifar, Farrokh
    Sanaye-Pasand, Majid
    IEEE SYSTEMS JOURNAL, 2018, 12 (01): : 759 - 767
  • [37] Robust Self-tuning Control Based on Discrete-time Sliding Mode for Auto-regressive Mathematical Model in the Presence of Unmodelled Dynamics
    Nabiha Touijer
    Samira Kamoun
    International Journal of Automation and Computing, 2016, (03) : 277 - 284
  • [38] Validation of Distributed Real-Time Simulations for Decoupled Power System Models
    Shen, Zhiwei
    Arrano-Vargas, Felipe
    Konstantinou, Georgios
    2022 IEEE 20TH INTERNATIONAL CONFERENCE ON INDUSTRIAL INFORMATICS (INDIN), 2022, : 170 - 174
  • [39] A real-time estimation and control scheme for induction motors based on sliding mode theory
    Barambones, Oscar
    Alkorta, Patxi
    Gonzalez de Durana, Jose Maria
    JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS, 2014, 351 (08): : 4251 - 4270
  • [40] Time series analysis and prediction of COVID-19 patients using discrete wavelet transform and auto-regressive integrated moving average model
    Ilu, Saratu Yusuf
    Prasad, Rajesh
    MULTIMEDIA TOOLS AND APPLICATIONS, 2024, 83 (29) : 72391 - 72409
12345