Review some of the metaheuristic optimization algorithms in MPPT

]n last decades, renewable energy resources have gain more attention as the demand of energy is increasing. Solar power is undoubtedly the most operational resource in the category of renewable energies as it is available worldwide. Solar power can be transformed into electricity in two ways; by CSP power plants or Photovoltaic systems. the solar energy is transformed to heat and then this energy is transformed to electricity in CSP power plants. However, this transformation of energy can be done in a single-step process by means of solar panels and PV technology. This technology is highly depended on solar irradiance and temperature. For each specific moment, there is a maximum power point (MPP) and its value is depended on irradiance and temperature. For operating in optimum position, firstly it is needed to track the MPP and secondly, to operate in that position. In normal condition, there is only a single MPP and no local points. There are various algorithms and techniques to discover the MPP in this condition. However, in partial shading condition, there are several local optimums and a global optimum. The usual methods cannot operate sufficiently. Metaheuristic optimization algorithms can be used to track MPP in both normal condition and partial shading condition. The main advantage of using them is that they are fast in operation and they do not trap in local optimums. This paper studies the genetic algorithm, PSO algorithm and grey wolf optimization algorithm in Maximum Power Tracking (MPPT) in PV systems.

Keywords:

MPPT , PV Inverter , Genetic algorithm , PSO algorithm , GWO algorithm