Non Integer Identification of Rotor Skin Effect in Induction Machines

Fractional identification of rotor skin effect in induction machines is presented in this paper. Park‘s transformation is used to obtain a system of differential equations which allows to include the skin effect in the rotor bars of asynchronous machines. A transfer function with a fractional derivative order has been selected to represent the admittance of the bar by the help of a non integer integrator which is approximated by a J+1 dimensional modal system. The machine parameters are estimated by an output-error technique using a non linear iterative optimization algorithm. Experimental results show the performance of the modal approach for modeling and identification.DOI:http://dx.doi.org/10.11591/ijece.v3i3.228

Parameter estimation for knowledge and diagnosis of electrical machines

International audienceThe type of control system used for electrical machines depends on the use (nature of the load, operating states, etc.) to which the machine will be put. The precise type of use determines the control laws which apply. Mechanics are also very important because they affect performance. Another factor of essential importance in industrial applications is operating safety. Finally, the problem of how to control a number of different machines, whose interactions and outputs must be coordinated, is addressed and solutions are presented. These and other issues are addressed here by a range of expert contributors, each of whom are specialists in their particular field. This book is primarily aimed at those involved in complex systems design, but engineers in a range of related fields such as electrical engineering, instrumentation and control, and industrial engineering, will also find this a useful source of information

A Comparative Study of Identification Techniques for Fractional Models

A comparative study of methods for fractional system identification is presented in this paper. The fractional system is modeled by the help of a non integer integrator which is approximated by a J+1 dimensional modal system composed of an integrator and first order systems. This identification method is compared to other techniques available in the Matlab toolbox. The model parameters are estimated by an output-error technique using a non linear iterative optimization algorithm. Numerical simulations show the performance of the modal approach for modeling and identification.DOI:http://dx.doi.org/10.11591/ijece.v3i2.213

Fractal Fract

The usual approach to the integration of fractional order initial value problems is based on the Caputo derivative, whose initial conditions are used to formulate the classical integral equation. Thanks to an elementary counter example, we demonstrate that this technique leads to wrong free-response transients. The solution of this fundamental problem is to use the frequency-distributed model of the fractional integrator and its distributed initial conditions. Using this model, we solve the previous counter example and propose a methodology which is the generalization of the integer order approach. Finally, this technique is applied to the modeling of Fractional Differential Systems (FDS) and the formulation of their transients in the linear case. Two expressions are derived, one using the Mittag–Leffler function and a new one based on the definition of a distributed exponential function

Diagnosis of induction machines by parameter estimation

International audienceThe type of control system used for electrical machines depends on the use (nature of the load, operating states, etc.) to which the machine will be put. The precise type of use determines the control laws which apply. Mechanics are also very important because they affect performance. Another factor of essential importance in industrial applications is operating safety. Finally, the problem of how to control a number of different machines, whose interactions and outputs must be coordinated, is addressed and solutions are presented. These and other issues are addressed here by a range of expert contributors, each of whom are specialists in their particular field. This book is primarily aimed at those involved in complex systems design, but engineers in a range of related fields such as electrical engineering, instrumentation and control, and industrial engineering, will also find this a useful source of information

The Moments in Control: a tool for Analysis, Reduction and Design

In this paper we present a new method of model reduction via the moments. The reduction technique is composed of two steps, the first one consists on using the Least Squares linear optimization algorithm to minimize a cost function representing the norm 2 of the error between different moments of the full order function and the reduced model. This solution represents an initialization of the second step algorithm which is based a Non Linear Programming minimizing a new criterion composed of the cost function of the first step and an equality constraint

Design of a MIMO PID Robust Controller using moments based approach

In this paper we present a new technique for robust MIMO controllers synthesis and reduction based on a reference model and moments approach intended to control a MIMO thermal system.The reference model allows to specify the performances requirements for the closed loop and improve the controller robustness while the moments tool (frequency and time ones) is used to reduce the controller structure using a Non Linear Optimization. The implementation on the real system associates this methodology of MIMO PID controllers synthesis with Broïda’s identification technique in order to carry out a auto-tuning procedure [2][11]

Estimation paramétrique pour le diagnostic des processus : Application à la bobine à noyau de fer

International audienceLe travail présenté dans cet article concerne la mise en oeuvre d'une méthodologie de diagnostic des procédés industriels basée sur l'estimation paramétrique. Cette technique s'appuie sur une modélisation de la signature de défaut en associant au mode commun (modèle sain) un mode différentiel (modèle de défaut) qui traduit le dysfonctionnement. L'identification des paramètres du modèle commun indique donc l'état dynamique du système, tandis que le suivi d'évolution des paramètres du mode différentiel permet la détection et la localisation du déséquilibre. Notre méthodologie a été validée expérimentalement sur une bobine à noyau de fer dédiée au diagnostic des défauts de type réduction et court-circuit de spires

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From the french school of regulation during 19th century to the emergence of automatic control after the second world war

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