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

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.