Ensemble Kalman Filter based Dynamic State Estimation of PMSG-based Wind Turbine

Dynamic state estimation of power systems using intelligent particle filtering based on ant colony optimisation for continuous domains

“…However, for large Gaussian noise, the measurement and process noise are considered 2.5 times larger than those of Case 1. This means that the values of Q d and R d in (26) and (27) are multiplied by 2.5. Assumption of Gaussian distribution for process and measurement noise is questionable in practice.…”

“…Ning et al [25] proposed a local sequential ensemble Kalman filter (EnKF) to improve the estimation accuracy of the dynamic states and the parameters of a multi-machine system using PMU data. In the EnKF, the distribution of the states is represented by a collection of samples, known as ensembles [26]. All the abovementioned Kalman filters assume the joint Gaussian distribution of both measurements and states and apply the Bayesian approach to find the Kalman gain [3].…”

A General Dynamic State Estimation Framework for Monitoring and Control of Permanent Magnetic Synchronous Generators-Based Wind Turbines

“…[23] and [24] use EKF and Ensemble Kalman Filter (EnKF) to estimate the dynamic state of PMSG-WTs, respectively. In these only existing publications on the DSE of PMSG-WTs [23][24], a major limitation is presented: the physical wind generation system model (i.e. the "plant" model in control theory) and the controller model are blended in the estimation process, which results in the following problems.…”

“…(2) While the physical model of PMSG-WTs is relatively fixed, a variety of control algorithms can be applied, which will drastically change the controller model. Thus, the blended model proposed in [23][24] is not generally applicable to PMSG-WTs with different types of controllers. It should be noted that some existing work already implements the principle of decoupling for wind turbines [25], but only for the mechanical part of the system, and not for the entire electromechanical system and the DSE application.…”

Model Reference Adaptive System with Finite-Set for Encoderless Control of PMSGs in Micro-Grid Systems

“…A variety of speed and position estimation methods have been proposed for permanent-magnet synchronous machines (PMSMs) and induction machines (IMs), and recently they were applied successfully to SMPMSGs/DFIGs. The well-known observers in the literature include the following: phase-locked loop (PLL) [13][14][15], model reference adaptive system (MRAS) [16][17][18][19][20][21][22], sliding-mode observers [23][24][25], extended Kalman filter (EKF) [26][27][28], unscented Kalman filter (UKF) [29][30][31], and others. Due to the simplicity, ease of implementation, and direct physical interpretation, MRAS-based observers have received increased interest from researchers and engineers.…”