NOURI Benaidja

BARKATI Hamza

Modélisation floue d’un système Non linéaire

Softcomputing techniques are receiving attention as optimisation techniques for many industrial applications. Although these techniques eliminate the need for derivatives computation, they require much work to adjust their parameters at the stage of research and development. Issues such as speed, stability, and parameters convergence remain much to be investigated. This paper discusses the application of the method of reference model to determine parameters of asynchronous machines using two optimisation techniques. Softcomputing techniques used in this paper are evolutionary strategy and the chemotaxis algorithm. Identification results using the two techniques are presented and compared with respect to the conventional simplex technique of Nelder and Mead. Discussion about the chemotaxis algorithm as the most promising optimisation technique is presented, giving its advantages and disadvantages.

Citation

Nouri BENAIDJA , , (2009), Softcomputing identification techniques of asynchronous machine parameters: Evolutionary strategy and chemotaxis algorithm, Turkish Journal of Electrical Engineering & Computer Sciences, Vol:17, Issue:1, pages:69-85, The Scientific and Technological Research Council of Turkey

Four evolutionary techniques (scatter search, evolutionary programming, ant colony, and particle swarm algorithms) were used for off-line identification of three phase asynchronous machine parameters. Optimization techniques were then tested on two distinct machines. In order to evaluate how much good the achieved machines parameters obtained, experimental and simulation input-output behaviors are presented for each method. The performances in term of objective function and convergence time prove the effectiveness of this class of optimization methods.

Citation

Nouri BENAIDJA , abdelrazak rahmouni , Nabil Khenfer, , (2006), Identification of asynchronous machine parameters by evolutionary techniques, Electric Power Components and Systems, Vol:34, Issue:12, pages:1359-1376, Taylor and Francis Ltd

The present study describes a comparative study of genetic algorithm and neighbourhood techniques used for off-line identification of three phase asynchronous machine parameters. All the methods are then tested on two distinct machines and the influence of the start time of identification, with reference to the time of startup of machine, is studied. Results show the superiority of genetic algorithm for low values of the start time of identification.

Citation

Nouri BENAIDJA , abdelrazak rahmouni , Nabil Khenfer, , (2006), Heuristics for Asynchronous Machine Identification:Genetic Algorithm and Neighbourhood Techniques, Asian Journal of Information Technology, Vol:5, Issue:8, pages:854 - 859, Medwell Journals