Abstract
This paper presents an adaptive maximum power point tracking (MPPT) control of a grid-connected photovoltaic (PV) energy conversion system utilizing neuro-fuzzy (NF) technique. The particle swarm optimization algorithm is used to train the membership functions while the recursive least squares algorithm is used to update the consequent parameters of the NF based MPPT scheme to cope with changing operating condition of PV solar system. The MPPT algorithm maximizes conversion efficiency by adjusting the duty cycle of the buck-boost converter to change the output voltage of the solar panel and hence, achieving the maximum panel output power for a given set of environmental conditions. The training data for NF scheme is obtained by operating the system using the perturb and observe (PO) MPPT algorithm. The performance of the proposed NF-based MPPT algorithm is validated in both simulation and real-time. The prototype PV system is built and the designed NF-based MPPT algorithm is implemented in laboratory environment using the DSP board DS1104. It is found that the proposed NF-based MPPT scheme achieves a very fast response with minor oscillations while transferring maximum power from solar panel to the grid line as compared to the conventional PO based MPPT scheme.
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