Dynamic Phasor Estimation Through DSTKF Under Transient Conditions

Huang, Chun; Xie, Xing; Jiang, Hui

doi:10.1109/tim.2017.2713018

Cited by 38 publications

(28 citation statements)

References 21 publications

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“…The filter outperforms when λ k is less than one and no correction is required [39]. Henceforth, from (50), we can get (31) as…”

Section: ) Ff and Sstsckf Algorithmmentioning

confidence: 99%

“…Further, Yin et al [30] have proposed the symmetric strong tracking EKF (SSTEKF) to avert the stability related issues commence in the STEKF. Huang et al [31] have developed double suboptimal scaling factor based adaptive STKF (DSTKF) algorithm for phasor measurement to adjust the process noise covariance matrix and the error covariance matrix in real-time, thus mitigating the shortcomings of the STKF. Li et al [32] have proposed the strong tracking CKF (STCKF) for real-time orbit determination, which adjusts the gain matrix online and forces the residual errors to be mutually orthogonal, thus allowing more accurate performance than the CKF.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, systematically no such consistency analysis has been evaluated in the reported algorithms. Besides, in [29], [30], and [31] the stability and divergence related problems have not been discussed so far, though these two are the important factors in the transient analysis. To resolve the issues mentioned above, in this paper, we have developed the constrained symmetric strong tracking square-root cubature Kalman filtering (CSSTSCKF) algorithm, in which the referred problems have been addressed as follows: (i) the stability related issues are alleviated by introducing the symmetric suboptimal multiple FFs into the SCKF, and hence making the algorithm to be stable, (ii) the divergence related problem is solved by imposing the nonlinear state constraints into the state vectors, (iii) noise sensitivity is controlled by tuning the soften factor (SF) and making the algorithm to be robust against the model uncertainty and environmental change.…”

Section: Introductionmentioning

confidence: 99%

“…-Further, constraints are imposed on the state vectors to prevent the filter from providing wayward estimates by converting constrained optimization problem into an unconstrained one by means of the log-barrier method, which formulates a novel transient state estimation algorithm i.e., the constrained SSTSCKF (CSSTSCKF). -The proposed method has been benchmarked by comparing the state estimation results obtained using the CSSTSCKF with the results obtained using four other well-known reported state-of-art algorithms i.e., the STCKF [21], STKF [1], DSTKF [31], and SSTEKF [30]. -Various statistical tests have been performed for consistency analysis.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Accurate Real-Time Estimation of Power System Transients Using Constrained Symmetric Strong Tracking Square-Root Cubature Kalman Filter

2019

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The paper presents a fast and accurate algorithm for estimating four significant parameters (i.e., amplitude, frequency, phase angle, and damping factor) of a typical transient signal. The method can be connoted as the constrained symmetric strong tracking square-root cubature Kalman filter (CSSTSCKF). The important aspects of the proposed algorithm are: 1) constraints are imposed on the state vectors by way of a logarithmic barrier function that is either ignored or handled heuristically; 2) symmetric sub-optimal multiple fading factors (FFs) are augmented into the predicted covariance matrix to capture sudden changes and to tune the gain matrix in real-time; moreover, symmetry of the covariance matrix is guaranteed by the influence of Cholesky triangular decomposition; 3) effect of noise can be adjusted by tuning the soften factor. Several case studies have been simulated to evaluate the proposed algorithm with respect to some of the well-known state-of-the-art methods. The real-time performance has been evaluated by flashing the filter codes into an ARM Cortex-M7 processor board and tracking the real-time signal from the experimental test bench. The results, presented herein, indicate that the CSSTSCKF remarkably outperforms all other considered techniques. Furthermore, the stability analysis of the nonlinear filter has been proved based on the constructor expression considering the boundedness of the estimation errors and other sub-items. INDEX TERMS Cholesky triangular decomposition, fading factor (FF), inequality constraints, logarithmic barrier function, power system transient, strong tracking square-root cubature Kalman filter (STSCKF).

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“…The filter outperforms when λ k is less than one and no correction is required [39]. Henceforth, from (50), we can get (31) as…”

Section: ) Ff and Sstsckf Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Accurate Real-Time Estimation of Power System Transients Using Constrained Symmetric Strong Tracking Square-Root Cubature Kalman Filter

2019

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“…Dynamic phasor is estimated using the Taylor expansion, where frequency deviation is derived directly from fitting parameters to avoid magnification of fitting errors. A double suboptimal-scaling factor-adaptive strongtracking Kalman filter (DSTKF)-based phasor measurement unit algorithm which can meet the accuracy requirement of the IEEE standard C37.118.1 under the dynamic condition was proposed in [26]. This method uses a kth Taylor polynomial to linearize the complex exponential of the signal model and estimates the dynamic phasor using DSTKF.…”

Section: Introductionsmentioning

confidence: 99%

Dynamic Phasors Estimation Based on Taylor-Fourier Expansion and Gram Matrix Representation

Petrović

Damljanović

2018

Mathematical Problems in Engineering

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The paper presents a new approach to estimation of the dynamic power phasors parameters. The observed system is modelled in algebra of matrices related to its Taylor-Fourier-trigonometric series representation. The proposed algorithm for determination of the unknown phasors parameters is based on the analytical expressions for elements of the Gram’s matrix associated with this system. The numerical complexity and algorithm time are determined and it is shown that new strategy for calculation of Gram’s matrix increases the accuracy of estimation, as well as the speed of the algorithm with respect to the classical way of introducing the Gram’s matrix. Several simulation examples of power system signals with a time-varying amplitude and phase parameters are given by which the robustness and accuracy of the new algorithm are confirmed.

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Dynamic Phasors Estimation Based on Taylor-Fourier Expansion and Gram Matrix Representation

Petrović

2023

Studies in Systems, Decision and Control

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The paper presents a new approach to estimation of the dynamic power phasors parameters. The observed system is modelled in algebra of matrices related to its Taylor-Fourier-trigonometric series representation. The proposed algorithm for determination of the unknown phasors parameters is based on the analytical expressions for elements of the Gram's matrix associated with this system. The numerical complexity and algorithm time are determined and it is shown that new strategy for calculation of Gram's matrix increases the accuracy of estimation, as well as the speed of the algorithm with respect to the classical way of introducing the Gram's matrix. Several simulation examples of power system signals with a time-varying amplitude and phase parameters are given by which the robustness and accuracy of the new algorithm are confirmed.

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Dynamic Phasor Estimation Through DSTKF Under Transient Conditions

Cited by 38 publications

References 21 publications

Accurate Real-Time Estimation of Power System Transients Using Constrained Symmetric Strong Tracking Square-Root Cubature Kalman Filter

Accurate Real-Time Estimation of Power System Transients Using Constrained Symmetric Strong Tracking Square-Root Cubature Kalman Filter

Dynamic Phasors Estimation Based on Taylor-Fourier Expansion and Gram Matrix Representation

Dynamic Phasors Estimation Based on Taylor-Fourier Expansion and Gram Matrix Representation

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