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A Black-Box Model for Estimation of the Induction Machine Parameters Based on Stochastic Algorithms

A Black-Box Model for Estimation of the Induction Machine Parameters Based on Stochastic Algorithms
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Author(s): Julien Maitre (Université du Québec à Chicoutimi, Canada), Sébastien Gaboury (Université du Québec à Chicoutimi, Canada), Bruno Bouchard (Université du Québec à Chicoutimi, Canada) and Abdenour Bouzouane (Université du Québec à Chicoutimi, Canada)
Copyright: 2017
Pages: 25
Source title: Nature-Inspired Computing: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-5225-0788-8.ch021

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Abstract

Knowledge on asynchronous machine parameters (resistances, inductances…) has become necessary for the manufacturing industry in the interest of optimizing performances in a production system (roll-to-roll processing, wind generator…). Indeed, accurate values of this machine allow improving control of the torque, speed and position, managing power consumption in the best way possible, and predicting induction machine failures with great effectiveness. In these regards, the authors of this paper propose a black-box modeling for a powerful identification of asynchronous machine parameters relying on stochastic research algorithms. The algorithms used for the estimation process are a single objective genetic algorithm, the well-known NSGA II and the new ?-NSGA III (multi-objective genetic algorithms). Results provided by those show that the best estimation of asynchronous machines parameters is given by ?-NSGA III. In addition, this outcome is confirmed by performing the identification process on three different induction machines.

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