Ensemble Kalman Filter for Dynamic State Estimation of Power Grids Stochastically Driven by Time-correlated Mechanical Input Power

Rosenthal, William Steven; Tartakovsky, Alex; Huang, Zhenyu

doi:10.1109/TPWRS.2017.2764492

Title: Ensemble Kalman Filter for Dynamic State Estimation of Power Grids Stochastically Driven by Time-correlated Mechanical Input Power

Abstract

State and parameter estimation of power transmission networks is important for monitoring power grid operating conditions and analyzing transient stability. Wind power generation depends on fluctuating input power levels, which are correlated in time and contribute to uncertainty in turbine dynamical models. The ensemble Kalman filter (EnKF), a standard state estimation technique, uses a deterministic forecast and does not explicitly model time-correlated noise in parameters such as mechanical input power. However, this uncertainty affects the probability of fault-induced transient instability and increased prediction bias. Here a novel approach is to model input power noise with time-correlated stochastic fluctuations, and integrate them with the network dynamics during the forecast. While the EnKF has been used to calibrate constant parameters in turbine dynamical models, the calibration of a statistical model for a time-correlated parameter has not been investigated. In this study, twin experiments on a standard transmission network test case are used to validate our time-correlated noise model framework for state estimation of unsteady operating conditions and transient stability analysis, and a methodology is proposed for the inference of the mechanical input power time-correlation length parameter using time-series data from PMUs monitoring power dynamics at generator buses.

Authors:

Rosenthal, William Steven ^[1]; Tartakovsky, Alex ^[1]; Huang, Zhenyu ^[1]

Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

Publication Date:: Tue Oct 31 00:00:00 EDT 2017

Research Org.:: Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

Sponsoring Org.:: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)

OSTI Identifier:: 1438238

Report Number(s):: PNNL-SA-123139
Journal ID: ISSN 0885-8950

Grant/Contract Number:: AC05-76RL01830

Resource Type:: Accepted Manuscript

Journal Name:: IEEE Transactions on Power Systems

Additional Journal Information:: Journal Volume: 33; Journal Issue: 4; Journal ID: ISSN 0885-8950

Publisher:: IEEE

Country of Publication:: United States

Language:: English

Subject:: 24 POWER TRANSMISSION AND DISTRIBUTION; Power system estimation; power system dynamics; wind power generation; time-correlation; stochastic processes; stochastic resonance; ensemble Kalman filter; filter divergence; parameter estimation

Citation Formats


                    Rosenthal, William Steven, Tartakovsky, Alex, and Huang, Zhenyu. Ensemble Kalman Filter for Dynamic State Estimation of Power Grids Stochastically Driven by Time-correlated Mechanical Input Power.  United States: N. p., 2017. 
Web.  doi:10.1109/TPWRS.2017.2764492.

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                    Rosenthal, William Steven, Tartakovsky, Alex, & Huang, Zhenyu. Ensemble Kalman Filter for Dynamic State Estimation of Power Grids Stochastically Driven by Time-correlated Mechanical Input Power.  United States.  https://doi.org/10.1109/TPWRS.2017.2764492

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                    Rosenthal, William Steven, Tartakovsky, Alex, and Huang, Zhenyu. Tue .  
"Ensemble Kalman Filter for Dynamic State Estimation of Power Grids Stochastically Driven by Time-correlated Mechanical Input Power".  United States.  https://doi.org/10.1109/TPWRS.2017.2764492.  https://www.osti.gov/servlets/purl/1438238.

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@article{osti_1438238,

  title        = {Ensemble Kalman Filter for Dynamic State Estimation of Power Grids Stochastically Driven by Time-correlated Mechanical Input Power},

  author       = {Rosenthal, William Steven and Tartakovsky, Alex and Huang, Zhenyu},

  abstractNote = {State and parameter estimation of power transmission networks is important for monitoring power grid operating conditions and analyzing transient stability. Wind power generation depends on fluctuating input power levels, which are correlated in time and contribute to uncertainty in turbine dynamical models. The ensemble Kalman filter (EnKF), a standard state estimation technique, uses a deterministic forecast and does not explicitly model time-correlated noise in parameters such as mechanical input power. However, this uncertainty affects the probability of fault-induced transient instability and increased prediction bias. Here a novel approach is to model input power noise with time-correlated stochastic fluctuations, and integrate them with the network dynamics during the forecast. While the EnKF has been used to calibrate constant parameters in turbine dynamical models, the calibration of a statistical model for a time-correlated parameter has not been investigated. In this study, twin experiments on a standard transmission network test case are used to validate our time-correlated noise model framework for state estimation of unsteady operating conditions and transient stability analysis, and a methodology is proposed for the inference of the mechanical input power time-correlation length parameter using time-series data from PMUs monitoring power dynamics at generator buses.},

  doi          = {10.1109/TPWRS.2017.2764492},

  journal      = {IEEE Transactions on Power Systems},

  number       = 4,

  volume       = 33,

  place        = {United States},

  year         = {Tue Oct 31 00:00:00 EDT 2017},

  month        = {Tue Oct 31 00:00:00 EDT 2017}

}

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Cited by: 11 works

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Figure 1: Schematic of the WECC 3-generator, 9 buses power system [18].

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