A Hybrid-Learning Algorithm for Online Dynamic State Estimation in Multimachine Power Systems

Tian, Guanyu; Zhou, Qun; Birari, Rahul; Qi, Junjian; Qu, Zhihua

doi:10.1109/tnnls.2020.2968486

Title: A Hybrid-Learning Algorithm for Online Dynamic State Estimation in Multimachine Power Systems

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Abstract

With the increasing penetration of distributed generators in the smart grids, having knowledge of rapid real-time electromechanical dynamic states has become crucial to system stability control. Conventional Supervisory Control and Data Acquisition (SCADA)-based dynamic state estimation (DSE) techniques are limited by the slow sampling rates, while the emerging phasor measurement units (PMUs) technology enables rapid real-time measurements at network nodes. Using generator bus terminal voltages, we propose a hybrid-learning DSE (HL-DSE) algorithm to estimate the synchronous machine rotor angle and speed in real time. The HL-DSE takes the power system model into account and trains neuroestimators with real-time data in an online manner. Compared with traditional DSE methods, the HL-DSE overcomes limitations by using a data-driven approach in conjunction with the physical power system model. The time efficiency, accuracy, convergence, and robustness of the proposed algorithm are tested under noises and fault conditions in both small- and large-scale test systems. Simulation results show that the proposed HL-DSE is much more computationally efficient than widely used Kalman filter (KF)-based methods while maintaining comparable accuracy and robustness. In particular, HL-DSE is over 100 times faster than square-root unscented KF (SR-UKF) and 80 times faster than extended KF (EKF). The advantages and challengesmore » of the HL-DSE are also discussed.« less

Authors:

^[1];

^[1]; Birari, Rahul ^[2];

^[1];

^[1]

Univ. of Central Florida, Orlando, FL (United States)
Software Motor Company, Sunnyvale, CA (United States)

Publication Date:: Thu Feb 13 00:00:00 EST 2020

Research Org.:: Univ. of Central Florida, Orlando, FL (United States)

Sponsoring Org.:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office

OSTI Identifier:: 1820630

Grant/Contract Number:: EE0007998

Resource Type:: Accepted Manuscript

Journal Name:: IEEE Transactions on Neural Networks and Learning Systems

Additional Journal Information:: Journal Volume: 31; Journal Issue: 12; Journal ID: ISSN 2162-237X

Publisher:: IEEE Computational Intelligence Society

Country of Publication:: United States

Language:: English

Subject:: 24 POWER TRANSMISSION AND DISTRIBUTION; Dynamic state estimation (DSE); hybrid learning; neural networks; phasor measurement units (PMUs); synchronous generators

Citation Formats


                    Tian, Guanyu, Zhou, Qun, Birari, Rahul, Qi, Junjian, and Qu, Zhihua. A Hybrid-Learning Algorithm for Online Dynamic State Estimation in Multimachine Power Systems.  United States: N. p., 2020. 
Web.  doi:10.1109/tnnls.2020.2968486.

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                    Tian, Guanyu, Zhou, Qun, Birari, Rahul, Qi, Junjian, & Qu, Zhihua. A Hybrid-Learning Algorithm for Online Dynamic State Estimation in Multimachine Power Systems.  United States.  https://doi.org/10.1109/tnnls.2020.2968486

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                    Tian, Guanyu, Zhou, Qun, Birari, Rahul, Qi, Junjian, and Qu, Zhihua. Thu .  
"A Hybrid-Learning Algorithm for Online Dynamic State Estimation in Multimachine Power Systems".  United States.  https://doi.org/10.1109/tnnls.2020.2968486.  https://www.osti.gov/servlets/purl/1820630.

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@article{osti_1820630,

  title        = {A Hybrid-Learning Algorithm for Online Dynamic State Estimation in Multimachine Power Systems},

  author       = {Tian, Guanyu and Zhou, Qun and Birari, Rahul and Qi, Junjian and Qu, Zhihua},

  abstractNote = {With the increasing penetration of distributed generators in the smart grids, having knowledge of rapid real-time electromechanical dynamic states has become crucial to system stability control. Conventional Supervisory Control and Data Acquisition (SCADA)-based dynamic state estimation (DSE) techniques are limited by the slow sampling rates, while the emerging phasor measurement units (PMUs) technology enables rapid real-time measurements at network nodes. Using generator bus terminal voltages, we propose a hybrid-learning DSE (HL-DSE) algorithm to estimate the synchronous machine rotor angle and speed in real time. The HL-DSE takes the power system model into account and trains neuroestimators with real-time data in an online manner. Compared with traditional DSE methods, the HL-DSE overcomes limitations by using a data-driven approach in conjunction with the physical power system model. The time efficiency, accuracy, convergence, and robustness of the proposed algorithm are tested under noises and fault conditions in both small- and large-scale test systems. Simulation results show that the proposed HL-DSE is much more computationally efficient than widely used Kalman filter (KF)-based methods while maintaining comparable accuracy and robustness. In particular, HL-DSE is over 100 times faster than square-root unscented KF (SR-UKF) and 80 times faster than extended KF (EKF). The advantages and challenges of the HL-DSE are also discussed.},

  doi          = {10.1109/tnnls.2020.2968486},

  journal      = {IEEE Transactions on Neural Networks and Learning Systems},

  number       = 12,

  volume       = 31,

  place        = {United States},

  year         = {Thu Feb 13 00:00:00 EST 2020},

  month        = {Thu Feb 13 00:00:00 EST 2020}

}

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