Estimation for power quality disturbances with multiplicative noises and correlated noises: a recursive estimation approach

Abstract

In this paper, the recursive estimation problem is investigated for the power quality disturbances. A system model for the power quality disturbances with multiplicative noises and correlated noises is proposed based on engineering practice. The multiplicative noises are considered to account for the stochastic disturbances on the system states. The process noise and the measurement noise are assumed to be one-step autocorrelated. The process noise and the measurement noise are twostep cross-correlated. Attention is focused on the design of an unbiased and recursive estimation algorithm in the presence of multiplicative noises and correlated noises. First, the state covariance, one-step prediction error covariance and estimation error covariance are derived. Then, the estimation error covariance is minimised by appropriately designing the estimator gain. Finally, simulation experiments under three scenarios are carried out to illustrate the effectiveness of the proposed estimation scheme.

Event-Triggering State and Fault Estimation for a Class of Nonlinear Systems Subject to Sensor Saturations

“…Finally, we have utilized two simulation examples to show the validity of the designed ETSFE algorithm. Our future research topics would be to extend the main results in this paper to the sensor networks where multiple coupling sensors are involved [ 22 ] and to apply the main results in practical engineering such as power systems [ 3 ].…”

“…State estimation/filtering problems have always been one of the fundamental issues in the areas of target tracking, navigation and positioning, electric power systems, econometrics, biosystems, etc. Therefore, enormous research attention has been paid to the state estimation problems and some elegant work has been reported, see e.g., [ 1 , 2 , 3 , 4 , 5 ]. According to different performance indices, the current state estimation approaches include Kalman filtering (KF), extend Kalman filtering (EKF), filtering and so on.…”

Stubborn state estimation for nonlinear distributed parameter systems subject to measurement outliers

“…It is well known that state estimation problem has long been a topic of concern as the dynamical analysis category and there have been a large number of research results available in the existing literature, see for example, References 12‐20. For the state estimation problem of DPSs, the corresponding results have been few relatively, see for example, References 21,22.…”

Maximum‐correntropy‐based Kalman filtering for time‐varying systems with randomly occurring uncertainties: An event‐triggered approach

“…As one of the fundamental issues in system control and signal processing, the state estimation problem has received considerable research attention over the past decades, 1‐6 and a variety of state estimation schemes have been developed based on different performance indices 7‐9 . Among different kinds of state estimation schemes, the well‐known Kalman filtering (KF) algorithm has been deemed to be a powerful means to estimate the state of linear systems with Gaussian noises.…”