Robust Estimation and Tracking of Power System Harmonics Using an Optimal Finite Impulse Response Filter

Kwon, Bo-Kyu; Han, Soohee; Lee, Kwang Y.

doi:10.3390/en11071811

Cited by 6 publications

(2 citation statements)

References 25 publications

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“…The disadvantage of this method is the lack of proof of its convergence. This paper deals with finite impulse response (FIR) filters for state estimation of linear discrete systems which are extensively employed in a variety of applications see for instance [30][31][32][33][34][35][36][37][38][39][40]. Unlike the KF, they allow to avoid the divergence and unsatisfactory object tracking connected with temporary perturbations, errors in the noise statistics setting, abrupt object changes [1,14,15].…”

Section: Introductionmentioning

confidence: 99%

Finite Impulse Response Filtering Algorithm with Adaptive Horizon Size Selection and Its Applications

Skorohod

2023

Journal of Robotics and Control

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It is known, that unlike the Kalman filter (KF) finite impulse response (FIR) filters allow to avoid the divergence and unsatisfactory object tracking connected with temporary perturbations and abrupt object changes. The main challenge is to provide the appropriate choice of a sliding window size for them. In this paper, the new finite impulse response (FIR) filtering algorithm with the adaptive horizon size selection is proposed. The algorithm uses the receding horizon optimal (RHOFIR) filter which receives estimates, an abrupt change detector and an adaptive recurrent mechanism for choosing the window size. Monotonicity and asymptotic properties of the estimation error covariance matrix and the RHOFIR filter gain are established. These results form a solid foundation for justifying the principal possibility to tune the filter gain using them and the developed adaptation mechanism. The proposed algorithm (the ARHOFIR filter) allows reducing the impact of disturbances by varying adaptively the sliding window size. The possibility of this follows from the fact that the window size affects the filter characteristics in different ways. The ARHOFIR filter chooses a large horizon size in the absence of abrupt disturbances and a little during the time intervals of their action. Due to this, it has better transient characteristics compared to the KF and RHOFIR filter at intervals where there is temporary uncertainty and may provide the same accuracy of estimates as the KF in their absence. By simulation, it is shown that the ARHOFIR filter is more robust than the KF and RHOFIR filter for the temporarily uncertain systems.

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Section: Introductionmentioning

confidence: 99%

Finite Impulse Response Filtering Algorithm with Adaptive Horizon Size Selection and Its Applications

Skorohod

2023

Journal of Robotics and Control

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“…These could originate the divergence problem in the Kalman filter [1][2][3]. In order to prevent divergence problems, finite impulse response (FIR) filters have been used as an alternative to the Kalman filter [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. Since FIR filters estimate the states by using finite measurements on the most recent time interval, these filters are known to be more robust against modeling uncertainties and numerical errors that cause of divergence problem in Kalman filter.…”

Section: Introductionmentioning

confidence: 99%

Recursive Optimal Finite Impulse Response Filter and Its Application to Adaptive Estimation

Kwon

Kim

2022

Applied Sciences

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In this paper, the recursive form of an optimal finite impulse response filter is proposed for discrete time-varying state-space models. The recursive form of the finite impulse response filter is derived by employing finite horizon Kalman filtering with optimally estimated initial conditions. The horizon initial state and its error covariance on the horizon are optimally estimated by using recent finite measurements, in the sense of maximum likelihood estimation, then initiating the finite horizon Kalman filter. The optimality and unbiasedness of the proposed filter are proved by comparison with the conventional optimal finite impulse response filter in batch form. Moreover, an adaptive FIR filter is also proposed by applying the adaptive estimation scheme to the proposed recursive optimal FIR filter as its application. To evaluate the performance of the proposed algorithms, a computer simulation is performed to compare the conventional Kalman filter and adaptive Kalman filters for the gas turbine aircraft engine model.

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Toward a systematic control design for solid oxide fuel cells

Ghadrdan

2020

Design and Operation of Solid Oxide Fuel Cells

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Robust Estimation and Tracking of Power System Harmonics Using an Optimal Finite Impulse Response Filter

Cited by 6 publications

References 25 publications

Finite Impulse Response Filtering Algorithm with Adaptive Horizon Size Selection and Its Applications

Finite Impulse Response Filtering Algorithm with Adaptive Horizon Size Selection and Its Applications

Recursive Optimal Finite Impulse Response Filter and Its Application to Adaptive Estimation

Toward a systematic control design for solid oxide fuel cells

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