Maximum power point tracking of photovoltaic system using ant colony and particle swam optimization algorithms

A continuous oscillation in the steady state causes a reduction in the PV module output power. In addition it cannot operate the module at its maximum output power in rapidly changing of weather conditions. So, there is a need of MPPT system to sample the output of the cells and apply the proper resistance(load)to obtain maximum power for any given environmental conditions. A new method to track the global MPP is presented, which is based on Ant Colony Optimization (ACO) combined with Particle Swarm Optimization (PSO)that controlling a DC-DC converter connected at the output of PV array, such that it maintains a constant input-power load. This model indicates the DC-DC converter is an interleaved boost converter topology which will increase the efficiency and reduce the ripple factor which is easily control and greater stability can be achieved. By using this model we get very low conduction and switching losses then switching frequency is improved and size of the system also reduced. The proposed method has the advantage that it can be applied in either stand alone or grid-connected PV systems comprising PV arrays with unknown electrical characteristics and does not require knowledge about the PV modules configuration.