Publications Scientifiques

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    Choosing the adapted artificial intelligence method (ANN and ANFIS) based MPPT controller for thin layer PV array
    (Springer, 2023) Bouchetob, Elaid; Nadji, Bouchra
    Because of the many advantages that artificial intelligence technologies provide in comparison to more conventional methods, a rising number of solar power plants are beginning to use them in their monitoring of the MPP. When there is a sudden change in solar temperature and irradiance, it is possible that the MPP will not be tracked as accurately. As a consequence of this, these methods could make up for the deficiencies of those that are more well-established (P&O, IC, etc.). Aside from that, there is a wide range of methods to AI, each of which has a particular advantage. By making some minor adjustments to the architecture, an artificial neural network (ANN) and an adaptive neuro-fuzzy inference system (ANFIS) were used to monitor the MPP of Thin Layer panel technology at the Oued Nechou installation in Ghardaia. Each connection channel now has six panels rather than the previous maximum of 12 panels, and the junction box has 210 channels rather than the prior maximum of 105 channels. In the last step, a DC-DC boost converter is used to increase the power output voltages produced by the module
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    Minimum action time of a robust fuzzy speed controller for induction machine drive
    (Advances in Modelling and Analysis C, 2007) Chetate, Boukhmis; Bradai, Rafik
    In this paper, we propose a procedure to design an optimal fuzzy controller for indirect field oriented controlled induction machine drives. This controller has best possible performances with a minimum action time possible in a practical implementation. First, we design a fuzzy PI controller having the maximum of fuzzy sets (7 input/output membership functions), which show better static and dynamic performances. This controller is specific to speed close loop of an indirect field oriented induction machine drive. Then, in order to minimize its composition the ANFIS (Adaptive Network-Based Fuzzy Inference System) structure is applied to perform a structural and parametric optimization of this controller. We propose also, a procedure to reproduce the input/output mapping of this controller with an approximation using artificial neural networks (ANN)