Publications Scientifiques

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Author: search.filters.author.Kheldoun, AissaSubject: search.filters.subject.Self-excitation

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    A Taguchi method-based optimization algorithm for the analysis of the wind driven-self-excited induction generator
    (Institute of Advanced Engineering and Science (IAES), 2024) Boukenoui, Rachid; Bradai, Rafik; Kheldoun, Aissa
    This paper investigates the use of a new global optimization algorithm that is based on Taguchi method to determine the performance parameters of self-excited induction generator being driven by variable speed wind. This analysis is based on solving equations obtained from the per-phase equivalent circuit of the induction generator. The equations have two unknowns namely the frequency and the magnetizing reactance. Both unknown are strongly dependent on the wind turbine speed, the capacity of the excitation, the load being connected at the terminals of the stator and eventually the per-phase equivalent circuit parameters. The resulting equations are nonlinear and subsequently to solve them one can employ either gradient-based algorithms or heuristic algorithms. This paper uses a new heuristic algorithm based on the Taguchi method which, in addition to its global research capability, offers superior characteristics in terms of accuracy and ease of implementation. A comparison with recently published optimization methods is carried out to show its performances in terms of accuracy and ease of implementation. The MATLAB software will be used to perform this analysis on a machine of 0.75 kW while some will be validated experimentally to confirm the aforementioned benefits.
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    Performance analysis of stand-alone six-phase induction generator using heuristic algorithms
    (Elsevier, 2019) Bouhadjraa, Dyhia; Kheldoun, Aissa; Zemouche, Ali
    The paper exhibits the performance analysis of six-phase self-excited induction generator for stand-alone wind energy generation system. The analysis is based essentially on solving the nonlinear equivalent circuit of the SP-SEIG, which is to find the per-unit frequency F and the magnetizing reactance Xm minimizing the determinant of the nodal admittance matrix Y instead of solving two non-linear equations with two unknowns. Hence, the equation-solving problem is converted to an optimization problem. The obtained minimum yields the adequate magnetizing reactance and frequency which will be used subsequently to compute the self-excitation process requirements in terms of the prime mover speed, the excitation capacitance and the load impedance on the one hand and to predict the generator steady state performance parameters on the other. In this work, the analysis is performed using three different global search algorithms, the genetic algorithm (GA), the particle swarm optimization (PSO) technique and the Taguchi optimization method (TM). A study of some simulation results is carried out using MatLab to compare between these three algorithms in terms of accuracy and guaranteed convergence in finding the minimum of the admittance
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    Application of new optimisation algorithm to self-excited induction generator analysis
    (IEEE, 2013) Kheldoun, Aissa; Refoufi, Larbi; Khodja, Djalal Eddine
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    Analysis of the self-excited induction generator steady state performance using a new efficient algorithm
    (Elsevier, 2012) Kheldoun, Aissa; Refoufi, Larbi; Khodja, Djalal Eddine
    The paper presents the application of DIRECT algorithm to analyse the performance of the Self-excited induction generator (SEIG). To the author best knowledge, this is the first attempt to apply it to such a problem. DIRECT algorithm is used to minimize the induction generator’s admittance without the need to separate it into its real and imaginary parts. No initial guess is required as it needs only the upper and lower values of the unknown variables which are easily determined. The obtained minimum admittance yields the adequate magnetizing reactance and the frequency. These two key parameters are then used to compute the self-excitation process requirements in terms of the prime mover speed, the capacitance and the load impedance on the one hand and to predict the generator steady state performance parameters on the other. Very good agreement between predicted results and experimental measurements is achieved