Robust Unscented Kalman Filter for Power System Dynamic State Estimation With Unknown Noise Statistics

Zhao, Junbo; Mili, Lamine

doi:10.1109/tsg.2017.2761452

Cited by 179 publications

(81 citation statements)

References 21 publications

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“…where (25a) and (25b) represents all matrix equality and inequality constraints respectively. As (24) is less constrained than (12) due to relaxation, then λ + ν 3 ≤ µ * 2 where µ * = ν * 1 ν * 2 + ν * 3 is obtained from solving (12). This ends the proof.…”

Section: Conclusion Paper's Limitations Future Workmentioning

confidence: 66%

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Robust Dynamic State Estimation of Synchronous Machines With Asymptotic State Estimation Error Performance Guarantees

Nugroho

Taha

2020

IEEE Trans. Power Syst.

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A robust observer for performing power system dynamic state estimation (DSE) of a synchronous generator is proposed. The observer is developed using the concept of L∞ stability for uncertain, nonlinear dynamic generator models. We use this concept to (i) design a simple, scalable, and robust dynamic state estimator and (ii) obtain a performance guarantee on the state estimation error norm relative to the magnitude of uncertainty from unknown generator inputs, and process and measurement noises. Theoretical methods to obtain upper and lower bounds on the estimation error are also provided. Numerical tests validate the performance of the L∞-based estimator in performing DSE under various scenarios. The case studies reveal that the derived theoretical bounds are valid for a variety of case studies and operating conditions, while yielding better performance than existing power system DSE methods.

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Section: Conclusion Paper's Limitations Future Workmentioning

confidence: 66%

“…where Q = A P +P A−C Y −Y C +Ξ. The constraints in (24) can be represented by E(P , ν, Ξ, Ψ i,j , Φ, Θ, λ, σ) = 0 (25a) L(P , Y , ν, Ξ, Ψ i,j , Φ, Θ, λ, σ) 0,…”

Section: Conclusion Paper's Limitations Future Workmentioning

confidence: 99%

Robust Dynamic State Estimation of Synchronous Machines With Asymptotic State Estimation Error Performance Guarantees

Nugroho

Taha

2020

IEEE Trans. Power Syst.

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“…Therefore, there is sustainable motivation for developing a robust filter that can work well in non-Gaussian environments and in the presence of outliers. In order to achieve such a goal, the work in [30] proposes a Generalized-Maximum Likelihood (GM)-UKF, in which a batch-mode regressing form is obtained via the statistical linearization to enhance the data redundancy, and thereby the form enables the GM-estimatorto identify bad data and filter out unknown noises. However, when using the UKF, it is an essential but challenging taskto generate Sigma points by using a scaled symmetric sampling strategy in case the state vector dimension is greater than 3,since there are three mutually influential parameters needed to be tuned in this step, while there is currently no consensus about the corresponding parameter selection principles.…”

Section: Limitations and Contributionsmentioning

confidence: 99%

“…In this work, a robust CKF (RCKF) based distributed DSE approach is developed to estimate the machine dynamic states by integrating the Huber's M-estimation theory with the CKF. Different from the GM-type estimator in [30], the proposed RCKF uses the robust M-estimation to detect outliers in measurements and then eliminates them by revising measurement noise variance matrix.…”

Section: Limitations and Contributionsmentioning

confidence: 99%

Robust Cubature Kalman Filter for Dynamic State Estimation of Synchronous Machines Under Unknown Measurement Noise Statistics

et al. 2019

IEEE Access

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Kalman-type filtering techniques including cubature Kalman filter (CKF) does not work well in non-Gaussian environments, especially in the presence of outliers. To solve this problem, Huber's M-estimation based robust CKF (RCKF) is proposed for synchronous machines by combining the Huber's M-estimation theorywith the classical CKF,which is capable of coping with the deterioration in performance and discretization of tracking curves when measurement noise statistics deviatefrom the prior noise statistics. The proposed RCKF algorithm has good adaptability to unknown measurement noise statistics characteristics including non-Gaussian measurement noise and outliers. The simulation results on the WSCC 3-machine 9-bus system and New England 16-machine 68-bus system verify the effectiveness of the proposed method and its advantage over the classical CKF.INDEX TERMSDynamic state estimation, cubature Kalman filter, synchronous machines,M-estimation theory,unknown noise statistics, non-Gaussian noise, outliers, PMU data.

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“…Consequently, when using the metaheuristics optimization algorithms, it is necessary to have appropriate knowledge about the model of a power system, the permissible range of parameters, and the parameters whose values are known 10 . In References 11–14, various types of Kalman filters are used for the identification process. Kalman filters have recursive nature, and they can be easily employed in real‐time identification methods.…”

Section: Introductionmentioning

confidence: 99%

Data mining based excitation system parameters estimation considering DCS and PMU measurements using cubature Kalman filter

Mohseni

Doroudi

Karrari

2020

Int Trans Electr Energ Syst

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Summary The excitation system is an important system of power plants, which has an effective role in power system dynamic and stability. Consequently, developing an approach for the excitation system's model and parameter estimation is a necessary research goal. In real situations, it is often needed to identify and estimate unknown parameters of the excitation system by field recorded signals. The recorded signal can be internal from the distributed control system (DCS) or external from the phasor measurement unit (PMU). In this article, a novel method based on cubature Kalman filter and data mining is proposed to identify the parameters of a standard type excitation system. Firstly, the best available signals for parameters estimation are chosen. Secondly, a method is proposed to parameters estimation of excitation systems efficiently, when both the DCS and PMU with different sample rates are employed to record the measurement data. Data mining is performed at intervals that DCS data is missing (or unavailable), while PMU data is available. Experimental data are used for validation of the proposed approach.

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Robust Unscented Kalman Filter for Power System Dynamic State Estimation With Unknown Noise Statistics

Cited by 179 publications

References 21 publications

Robust Dynamic State Estimation of Synchronous Machines With Asymptotic State Estimation Error Performance Guarantees

Robust Dynamic State Estimation of Synchronous Machines With Asymptotic State Estimation Error Performance Guarantees

Robust Cubature Kalman Filter for Dynamic State Estimation of Synchronous Machines Under Unknown Measurement Noise Statistics

Data mining based excitation system parameters estimation considering DCS and PMU measurements using cubature Kalman filter

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