Optimalization of ANFIS-PSO Algorithm Based on MPPT Control for PV System Under Rapidly Changing Weather Condition

The output power of a Photovoltaic (PV) system is affected by changing of weather conditions such as temperature and irradiance. However, the PV characteristic curve has a certain point called the maximum power point (MPP). An algorithm is needed to ensure the PV at the Maximum Power Point, which is called the MPPT Algorithm. This algorithm must be able to produce maximum power in rapidly changing weather condition. In this paper, simulation justification of an Adaptive Neuro Fuzzy Inference System ANFIS-PSO based on MPPT PV controller has been provided to reach MPPT PV. Simulation system consist of three simulations: Variable Temperature and Constant Irradiance; Variable Irradiance and Constant Temperature; Variable Temperature and Variable Irradiance as Simultaneous. The performance of the ANFIS-PSO controller method is compared to Perturb and Observe (P&O) and Incremental Conductance (Inc). It provides fast response, precise and accurate PV tracking under rapidly changing weather conditions like irradiance and temperature. The simulation show that the PV system has been functional with zero steady state error and rapid tracking convergence velocity under highly irradiation. An ANFIS-PSO has better performance tracking ability compared with conventional method like P&O and Inc for proper training under uniform and ununiform weather conditions. An ANFIS-PSO can generate the output active power which is higher than another controller. The efficiency of an ANFIS-PSO reach 98.36% in Standard Test Condition (STC). The main contribution of this proposed method for academic knowledge is to obtain the best MPPT configuration based on ANFIS-PSO algorithm and acceptance of MPPT controller design.