state estimation is the foundation of any control and decision making in power networks. the first requirement for a secure network is a precise and safe state estimator in order to make decisions based on accurate knowledge of the network status. this paper introduces a new estimator which is able to detect bad data with few calculations without need for repetitions and estimation residual cal...

ph control is a challenging problem due to its highly nonlinear nature. in this paper the performances of two different adaptive global linearizing controllers (glc) are compared. least squares technique has been used for identifying the titration curve. the first controller is a standard glc based on material balances of each species. for implementation of this controller a nonlinear state est...

State estimation is the foundation of any control and decision making in power networks. The first requirement for a secure network is a precise and safe state estimator in order to make decisions based on accurate knowledge of the network status. This paper introduces a new estimator which is able to detect bad data with few calculations without need for repetitions and estimation residual cal...

In this study, we propose a novel use of reinforcement learning for estimating hidden variables and parameters of nonlinear dynamical systems. A critical issue in hidden-state estimation is that we cannot directly observe estimation errors. However, by defining errors of observable variables as a delayed penalty, we can apply a reinforcement learning frame-work to state estimation problems. Spe...

the problem of observer design for nonlinear systems has got great attention in the recent literature. the nonlinear observer has been a topic of interest in control theory. in this research, a modified robust sliding-mode observer (smo) is designed to accurately estimate the state variables of nonlinear systems in the presence of disturbances and model uncertainties. the observer has a simple ...

In this paper, a novel stator current based Model Reference Adaptive System (MRAS) estimator for speed estimation in the speed-sensorless vector controlled induction motor drives is presented. In the proposed MRAS estimator, measured stator current of the induction motor is considered as a reference model. The estimated stator current is produced in an adjustable model to compare with the measu...