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Dynamic state estimation with additive noise for load frequency control using bilateral fuzzy adaptive unscented Kalman filter by Sohrab Mokhtari; Kang K. Yen is a Engineering article available to read on EtoBox.

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This paper introduces a novel approach, called bilateral fuzzy adaptive unscented Kalman filter (BFAUKF), for dynamic state estimation (DSE) in non-linear systems with uncertainties. This algorithm uses a Mamdani fuzzy logic to regulate the measurement noise covariance in the conventional unscented Kalman filter (UKF) algorithm. By doing so, a more accurate DSE can be achieved compared with the stand-alone UKF estimation method. In the BFAUKF, besides the disturbances in the measurement noise covariance, the covariance of the error in the state estimation is also applied to obtain a more accurate estimation. To show the effectiveness of this algorithm, a DSE design based on the proposed approach is developed for a load frequency control (LFC) in a two-area power grid. Four scenarios of noises impact the whole system in the process and the measurement spaces. The proposed method shows a significant improvement in the estimation of the dynamic states of the LFC model.

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Author

Sohrab Mokhtari; Kang K. Yen

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Engineering (Physical Sciences)