Calibrating Parameters of Power System Stability Models Using Advanced Ensemble Kalman Filter

Huang, Renke; Diao, Ruisheng; Li, Yuanyuan; Sanchez-Gasca, J.J.; Huang, Zhenyu; Thomas, Brian; Etingov, Pavel; Kincic, Slaven; Wang, Shaobu; Fan, Rui; Matthews, Gordon; Kosterev, Dmitry; Yang, Shaolin; Zhao, Junbo

doi:10.1109/tpwrs.2017.2760163

Cited by 104 publications

(57 citation statements)

References 12 publications

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“…Therefore, sensitivity analysis is carried out to narrow down the number of candidate parameters for calibration. This strategy is widely used in the model validation and calibration, such as [26], [100], [101].…”

Section: Parameter Estimation and Calibration Using Dsementioning

confidence: 99%

Power System Dynamic State Estimation: Motivations, Definitions, Methodologies, and Future Work

Zhao

Gómez-Expósito

Netto

et al. 2019

IEEE Trans. Power Syst.

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553

257

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This paper summarizes the technical activities of the Task Force on Power System Dynamic State and Parameter Estimation. This Task Force was established by the IEEE Working Group on State Estimation Algorithms to investigate the added benefit of dynamic state and parameter estimation for the enhancement of the reliability, security, and resilience of electric power systems. The motivations and engineering values of dynamic state estimation (DSE) are discussed in detail. Then, a set of potential applications that will rely on DSE is presented and discussed. Furthermore, a unifie framework is proposed to clarify the important concepts related to DSE, forecasting-aided state estimation, tracking state estimation and static state estimation. An overview of the current progress in DSE and dynamic parameter estimation is provided. The paper also provides future research needs and directions for the power engineering community.

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Section: Parameter Estimation and Calibration Using Dsementioning

confidence: 99%

Power System Dynamic State Estimation: Motivations, Definitions, Methodologies, and Future Work

Zhao

Gómez-Expósito

Netto

et al. 2019

IEEE Trans. Power Syst.

Self Cite

553

257

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“…According to [16,17], the measurement error is generally between 1% and 2%. Considering the influence, the process noise variance matrix is set as…”

Section: Equation Of Measurement and State Estimationmentioning

confidence: 99%

Dynamic State Estimation for Synchronous Machines Based on Adaptive Ensemble Square Root Kalman Filter

Nan

Wang

et al. 2019

Applied Sciences

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Dynamic state estimation (DSE) for generators plays an important role in power system monitoring and control. Phasor measurement unit (PMU) has been widely utilized in DSE since it can acquire real-time synchronous data with high sampling frequency. However, random noise is unavoidable in PMU data, which cannot be directly used as the reference data for power grid dispatching and control. Therefore, the data measured by PMU need to be processed. In this paper, an adaptive ensemble square root Kalman filter (AEnSRF) is proposed, in which the ensemble square root filter (EnSRF) and Sage–Husa algorithm are utilized to estimate measurement noise online. Simulation results obtained by applying the proposed method show that the estimation accuracy of AEnSRF is better than that of ensemble Kalman filter (EnKF), and AEnSRF can track the measurement noise when the measurement noise changes.

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“…In [23], EnKF was used for the DSE of power systems and addressed from the accuracy of the initial value, model error, and the sensitivity to sampling frequency. EnKF was also used to estimate the power system harmonic state in [25] and to calibrate the generator parameters to reduce the mismatch between PMU measurement and the generator state in [27].…”

Section: Introductionmentioning

confidence: 99%

Comparisons on Kalman-Filter-Based Dynamic State Estimation Algorithms of Power Systems

Liu

et al. 2020

IEEE Access

104

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The Kalman-filter-based algorithms as the mainstream algorithms of dynamic state estimation of power systems have been extensively used to provide accurate data for power system applications. However, few comparisons are made to show their advantages and disadvantages. In this paper, four Kalmanfilter-based algorithms (i.e., extended Kalman filter, unscented Kalman filter, cubature Kalman filter, and ensemble Kalman filter) are compared to show their differences from implementation complexity, estimation accuracy and calculation efficiency, the resistance to measurement errors, and the sensitivity to system scales. Finally, the simulation results on the 3-machine, 10-machine, and 48-machine power systems show their advantages and disadvantages. INDEX TERMS Cubature Kalman filter, dynamic state estimation, ensemble Kalman filter, extended Kalman filter, unscented Kalman filter. I. NOMENCLATURE a Admittance angle matrix C Equal weight cubature points vector D Damping coefficient e Unit column vector E EnKF sample set f (•) State transition function F Jacobian matrix of state transition function h(•) Measurement function H Jacobian matrix of measurement function I Unit matrix k Filtering gain p e Electromagnetic power of the generator p m Mechanical power of the generator P k Estimated error covariance P zz Innovation covariance matrix P xz The cross-covariance matrix Q System noise variance R Measurement noise variance The associate editor coordinating the review of this manuscript and approving it for publication was Wuhui Chen. S Weighted Sigma points vector T Inertia constant of the generator u Constant control vector v Measurement noise vector V Voltage amplitude w System noise vector W c Weight vectors of covariance W m Weight vectors of mean x State vector Y Reduced node admittance matrix z Measurement vector δ Generator power angle ω Generator speed ω s Generator reference speed ω δ System noise vector of generator power angle ω ω System noise vector of generator speed h Difference step size i, j The ith/jth generator k Current time n Number of generators n en Number of samples N Number of times α, β, η, λ, l Setting parameters of UKF

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Calibrating Parameters of Power System Stability Models Using Advanced Ensemble Kalman Filter

Cited by 104 publications

References 12 publications

Power System Dynamic State Estimation: Motivations, Definitions, Methodologies, and Future Work

Power System Dynamic State Estimation: Motivations, Definitions, Methodologies, and Future Work

Dynamic State Estimation for Synchronous Machines Based on Adaptive Ensemble Square Root Kalman Filter

Comparisons on Kalman-Filter-Based Dynamic State Estimation Algorithms of Power Systems

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