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Adaptive neuro-synergetic control technique for SEPIC converter in PV systems

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Abstract

In this paper, an adaptive nonlinear method is proposed for both Maximum Power Point Traking Control and output voltage regulation of a Single-Ended Primary Inductance Converter (SEPIC). The main objective of this adaptive synergetic controller is to maintain a constant switching frequency of the converter and to stabilize the output voltage under uncertain weather conditions in real time. A hybrid control scheme has been derived and a Radial Basis function neural network used for approximation of unmeasurable or unmeasured variables. The effect of the controller to provide a maximum power transfer from PV side to SEPIC converter under unpredictable weather conditions and load variations has been verified through simulations. The stability of the close-loop system is insured by Lyapunov’s theory and the proposed algorithm gives good results compared to the Sliding Mode Controller used in the same context.

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Correspondence to Jean de Dieu Nguimfack-Ndongmo.

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Nguimfack-Ndongmo, J.d.D., Kenné, G., Kuate-Fochie, R. et al. Adaptive neuro-synergetic control technique for SEPIC converter in PV systems. Int. J. Dynam. Control 10, 203–216 (2022). https://doi.org/10.1007/s40435-021-00808-1

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  • DOI: https://doi.org/10.1007/s40435-021-00808-1

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