Comparison of MPPT using GA optimized ANN employing PI controller for solar PV system with MPPT using incremental conductance

Power sector is facing a major issue in meeting the increasing power demand due to the unavailability of resources to meet the demand using conventional sources. Thus, the demand for renewable resources has increased. The prime energy resources in use are wind energy and solar energy. Solar energy is the most promising energy source since it is the most abundant energy source. In order to obtain maximum efficiency from the solar module, Maximum Power Point Tracking algorithms are used. Many methods available for maximum power point tracking are perturb and observe, incremental conductance, parasitic capacitance, constant voltage and constant current etc. Due to some of the disadvantages of these methods the use of Artificial Neural Network for maximum power point tracking has increased. Optimization techniques can be used for optimizing the data used for training artificial neural network. Genetic algorithm is the optimization technique used in this paper. PI controller is used to reduce the error. In this paper, maximum power point tracking using genetic algorithm optimized artificial neural network employing PI controller is compared with maximum power point tracking using incremental conductance.