Ant colony optimisation has been tailored to suit maximum power point tracking (MPPT) in photovoltaic (PV) systems and is presented in this study. Artificial ants are deployed in the solution space and are made to forage and the ants which find better sources of food are retained while ants fail to search effectively are deleted from the population. The greedy search of potential ants for better food location leads to identification of higher power peaks in the PV system. The concept is modelled suitably and MPPT curves in a few PV configurations are simulated and found to be promising. Experiments were also conducted to show the veracity of the new method.
This study explains a systematic design procedure for the output voltage regulation of a boost-type DC-DC converter employing evolutionary algorithms. The feedback controller design for output voltage regulation is formulated as an optimisation problem and the controller constants are identified via evolutionary search. The design procedure employing genetic algorithm, differential evolution and artificial immune system is lucidly described. Computer simulation results supported by experimental evidence clearly demonstrate that the controllers estimated through evolutionary algorithms are capable of delivering enhanced output voltage regulation under different types of load and supply disturbances.
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