Adaptive estimation and filtering

The most common estimation algorithms used today
for power system static and dynamic state estimation are the
variants of Kalman filter (KF) like Extended Kalman Filter
(EKF) and Unscented Kalman Filter (UKF). These model based
estimation algorithms are well known for their accuracies.
However, it is a well known fact that EKF requires fine tuning
and good initial guess for optimum performance. Moreover,
these adaptive filters generally employed for estimation purposes
require high computational power when it comes to real time
estimation. Therefore, in this paper we propose a computationally
light yet effective estimation algorithm based on state space
model which have not yet been applied to the problem of power
system dynamic state estimation. We derive and propose the
use of state space least mean fourth algorithm for the purpose
of dynamic state estimation considering the problem of a two
phase permanent magnet synchronous motor. The algorithm
has been employed successfully in this paper in the dynamic
state estimation of the highly non linear synchronous motor.
The problem has been investigated in the presence of Gaussian
noise to show the effectiveness of the algorithm. Moreover, the
algorithm is also compared with the performance of the EKF.

The most common estimation algorithms used today for power system static and dynamic state estimation are the variants of Kalman filter (KF) like Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF). These model based estimation algorithms are well known for their accuracies. However, it is a well known fact that EKF requires fine tuning and good initial guess for optimum performance. Moreover, these adaptive filters generally employed for estimation purposes require high computational power when it comes to real time estimation. Therefore, in this paper we propose a computationally light yet effective estimation algorithm based on state space model which have not yet been applied to the problem of power system dynamic state estimation. We derive and propose the use of state space least mean fourth algorithm for the purpose of dynamic state estimation considering the problem of a two phase permanent magnet synchronous motor. The algorithm has been employed successfully in ...

The paper addresses the problem of effectively and robustly controlling a 3D overhead crane under the payload mass uncertainty, where the control performance is shown to be consistent. It is proposed to employ the sliding mode control technique to design the closed-loop controller due to its robustness, regardless of the uncertainties and nonlinearities of the under-actuated crane system. The radial basis function neural network has been exploited to construct an adaptive mechanism for estimating the unknown dynamics. More importantly, the adaptation methods have been derived from the Lyapunov theory to not only guarantee stability of the closed-loop control system, but also approximate the unknown and uncertain payload mass and weight matrix, which maintains the consistency of the control performance, although the cargo mass can be varied. Furthermore, the results obtained by implementing the proposed algorithm in the simulations show the effectiveness of the proposed approach and the consistency of the control performance, although the payload mass is uncertain.

The most common estimation algorithms used today for power system static and dynamic state estimation are the variants of Kalman filter (KF) like Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF). These model based estimation algorithms are well known for their accuracies. However, it is a well known fact that EKF requires fine tuning and good initial guess for optimum performance. Moreover, these adaptive filters generally employed for estimation purposes require high computational power when it comes to real time estimation. Therefore, in this paper we propose a computationally light yet effective estimation algorithm based on state space model which have not yet been applied to the problem of power system dynamic state estimation. We derive and propose the use of state space least mean fourth algorithm for the purpose of dynamic state estimation considering the problem of a two phase permanent magnet synchronous motor. The algorithm has been employed successfully in ...