Abstract
This paper presents a unique combination of an interleaved soft switched boost converter (ISSBC) run by a set of two photovoltaic panel (PV) with a distributed MPPT, suitable to guarantee MPPT even under partial shadowed conditions, managed by an adaptive neuro fuzzy inference system trained by the training data derived from a particle swarm optimization (PSO–ANFIS) unit. The ISSBC is followed by a, single phase cascaded H bridge five-level inverter (CHI) driven by the individual DC outputs of the ISSBC, with selective harmonic elimination scheme to eliminate typically the seventh order harmonics. A comparison of different intelligent distributed maximum power point tracking (MPPT) algorithms for photovoltaic (PV) system under partial shadow conditions is carried out. The use of the ISSBC guarantees mitigation of ripple and it is meant to handle higher currents with minimal switching losses. Simulation was carried out in the Matlab Simulink environment and an experimental verification with a scaled down model validated the proposed scheme. It has been thus established, by both simulation and experimental verification, that the PSO–ANFIS model of distributed MPPT scheme of control outperforms other schemes of control for MPPT.
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