Comparison of advance intelligence algorithms for maximum power point tracking

In order to mitigate the detrimental impact of energy generation through fossil fuel on environment, the world is continually moving towards more and more use of Renewable Energy Technologies (RET). Amongst RETs Photovoltaic (PV) energy now has become most attractive as it is abundantly available and presently commercially comparable to conventional energy. To extract maximum power from PV arrays Maximum Power Point Tracking (MPPT) techniques are used. However, conventional MPPT algorithms perform well in uniform irradiance condition but ineffective in partial shaded condition (PSC). This demands development of efficient optimization techniques which are capable of seeking the global maximum power point effectively in PV systems under PSC. In the paper, performance comparison of conventional MPPT techniques (Perturb and Observe and Incremental Conductance) and techniques using advanced intelligence algorithms (Particle Swarm Optimization and Fire Fly) has been done under uniform irradiance and PSC in terms of efficiency, convergence speed, oscillations in output etc.