showing 3 related works from this author
Sensorless induction machine drive for fly-wheel generation unit based on a TLS-based non-linear observer
2016
This paper proposes a sensorless technique for an induction machine Flywheel Energy Storage System (FESS) based on a non-linear observer integrated with a total least-squares speed estimator taking into consideration the IM (Induction Machine) saturation effects. The nonlinear observer is based on an original formulation of the dynamic model of the IM including the magnetic saturation, rearranged in a space-state form, after assuming as state variables the stator current and the rotor magnetizing current space-vectors in the stator reference frame. The choice of the observer gain has been made by the use of Lyapunov's method. The speed signal needed by the non-linear observer for the flux e…
The on-line curvilinear component analysis (onCCA) for real-time data reduction
2015
Real time pattern recognition applications often deal with high dimensional data, which require a data reduction step which is only performed offline. However, this loses the possibility of adaption to a changing environment. This is also true for other applications different from pattern recognition, like data visualization for input inspection. Only linear projections, like the principal component analysis, can work in real time by using iterative algorithms while all known nonlinear techniques cannot be implemented in such a way and actually always work on the whole database at each epoch. Among these nonlinear tools, the Curvilinear Component Analysis (CCA), which is a non-convex techni…
A Novel Self-organizing Neural Technique for Wind Speed Mapping
2009
Systems with high nonlinearities are, in general, very difficult to model. This is particularly true in geostatistics, where the problem of the estimation of a regionalized variable (RV) given only a small amount of measurement stations and a complex terrain surface is very challenging. This paper introduces a novel strategy, which couples the Curvilinear Component Analysis (CCA) and the Generalized Mapping Regressor (GMR). CCA, which is a nonlinear projector of a data manifold, is here used in order to find the intrinsic dimension of the data manifold, just giving an insight on the nonlinearities of the problem. This analysis drives the pre-processing of the data set used for the training …