Abstract
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.
Highlights
Transients are the special type of power quality disturbances that persist for short duration, degrade the voltage and current waveforms and have a strong impact on both the grid and customers [1]
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 fading factor (FF) into the squareroot cubature Kalman filter (SCKF), and 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
A novel constrained symmetric strong tracking SCKF (CSSTSCKF) has been developed for transient detection and parameters estimation where (i) the constraints on the state variables are encountered in dynamic state estimation by means of logarithmic barrier function; (ii) at the same time, nonlinear symmetric strong tracking filter (STF) framework has been exploited in the SCKF structure to track the sudden changes quickly while maintaining the residual sequences to be orthogonal to each other; (iii) Cholesky triangular decomposition has been utilized so that symmetric suboptimal multiple FFs in the error covariance matrix can be relocated that confirms the symmetry of the error covariance matrix during every iteration
Summary
Transients are the special type of power quality disturbances that persist for short duration, degrade the voltage and current waveforms and have a strong impact on both the grid and customers [1]. 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 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.
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