Alpha beta filter

About: Alpha beta filter is a research topic. Over the lifetime, 5653 publications have been published within this topic receiving 128415 citations.

Observer-based grid voltage disturbance rejection for grid connected voltage source PWM converters with line side LCL filters

Abstract: This work presents an extended disturbance observer design for grid voltage disturbance rejection of voltage source PWM converters with LCL filters. The proposed observer design leads to a cascaded extended disturbance observer using either the converter side current or the line side current for feedback. Theoretical aspects including the LCL filter system observability, observer pole placement strategy and practical implementation are discussed to achieve an observer formulation. The analysis presented is based on a fully analytical observer formulation in the discrete time-domain. The discrete state-space modeling of the LCL filter dynamics is discussed and a step-by-step observer formulation is presented. The theoretical analysis is verified on a 22 kW laboratory type adjustable speed drive system where the control algorithms are implemented on a dSpace system. The analysis reveals excellent disturbance rejection capabilities of the proposed cascaded extended disturbance observer design for both feedback cases.

A Kalman Filter Approach to Precision GPS Geodesy

Abstract: The high stability of the GPS signals makes it possible to determine differential position over short baselines with an accuracy of the order of centimeters. This has been demonstrated using very long baseline interferometry (VLBI) methods of radio astronomy. This paper presents an alternative approach using Kalman filter methods. It is based on an adaptive filter concept developed by D. T. Magi11 in 1965. The scheme employs parallel Kalman filters with each filter being modeled for a different integer wavelength assumption. As the phase measurement sequence progresses, the adaptive scheme “learns” which Kalman filter corresponds to the correct hypothesis, and thus it both resolves the wavelength ambiguity and estimates differential position simultaneously. Simulated results are presented which indicate convergence in about 10 minutes of satellite observation time.

A robust extended Kalman filter for power system dynamic state estimation using PMU measurements

Abstract: This paper develops a robust extended Kalman filter to estimate the rotor angles and the rotor speeds of synchronous generators of a multimachine power system. Using a batch-mode regression form, the filter processes together predicted state vector and PMU measurements to track the system dynamics faster than the standard extended Kalman filter. Our proposed filter is based on a robust GM-estimator that bounds the influence of vertical outliers and bad leverage points, which are identified by means of the projection statistics. Good statistical efficiency under the Gaussian distribution assumption of the process and the observation noise is achieved thanks to the use of the Huber cost function, which is minimized via the iteratively reweighted least squares algorithm. The asymptotic covariance matrix of the state estimation error vector is derived via the covariance matrix of the total influence function of the GM-estimator. Simulations carried out on the IEEE 39-bus test system reveal that our robust extended Kalman filter exhibits good tracking capabilities under Gaussian process and observation noise while suppressing observation outliers, even in position of leverage. These good performances are obtained only under the validity of the linear approximation of the power system model.

A Comparison of Multi Hypothesis Kalman Filter and Particle Filter for Multi-target Tracking

Abstract: Visual tracking of multiple targets is a key step in surveillance scenarios, far from being solved due to its intrinsic ill-posed nature. In this paper, a comparison of MultiHypothesis Kalman Filter and Particle Filter-based tracking is presented. Both methods receive input from a novel online background subtraction algorithm. The aim of this work is to highlight advantages and disadvantages of such tracking techniques. Results are performed using public challenging data set (PETS 2009), in order to evaluate the approaches on significant benchmark data.

Observer design for differentiable Lipschitz nonlinear systems with time-varying parameters

Abstract: This paper develops observer design techniques in a unified framework for both time invariant and parameter varying Lipschitz nonlinear systems that are differentiable w.r.t. state variables. First, a new sufficient condition for asymptotic convergence is developed for both the extended Luenberger observer and a two-DOF nonlinear observer for time-invariant nonlinear systems. In addition to ensuring asymptotic convergence, extension of this observer design technique to optimization of a L2 performance criterion is presented, which enables the observer to handle the unknown disturbance inputs as well as ensure robustness to model uncertainty. Next, augmentation of this technique to parameter varying nonlinear (PVNL) systems is developed. Different from methods suggested in the LPV literature, a simple but non-conservative finite dimensional relaxation method for quadratic parameter dependent LMIs is presented. These results constitute perhaps the first systematic observer design methodology in literature for PVNL systems. Finally, a simulation result for vehicle slip angle estimation is presented to verify the performance of the developed observer design methods.