Intelligent MPPT Control of Solar PV Systems
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Changing weather conditions, such as temperature and solar irradiance, affect the output power of a PV system. This makes PV systems to operate on a wide voltage and current range but produces maximum power only at the operating point called the Maximum Operating Point (MPP) located at the PV curve. And to ensure that the PV operates at this point, an MPPT algorithm is required to provide maximum power under varying solar irradiance and temperature. Some of the conventional MPPT control techniques considered in this study are Perturb and Observe (P&O) and Incremental conductance. P&O is used because it is clear and easy to implement, however, this technique causes oscillations around the MPP point, causing high power losses when applied to large-scale PV systems. The Incremental Conductance measures the ration between the instantaneous conductance and the increasing in the PV system’s conductance to perform MPP tracking. This method however, is proved to prevail against the problems generated by the P&O technique. These techniques add small increments to the output power and the duty cycle of the PV system and this is basically due to the non-linear output characteristics of the PV module hence making the conventional MPPT techniques to have imprecise tracking and low accuracy under changing irradiance and temperature. This led to the birth of Artificial Intelligent based MPPT algorithm to improve the efficiency and performance of the conventional techniques as these tracks better the non-linear output characteristics than the conventional methods. This MPPT AI based is an Adaptive Neuro Fuzzy Inference System (ANFIS) that combines the features of Artificial Neuro Network and Fuzzy Logic Control to achieve faster reaction, good accuracy with superb precision under varying irradiance and temperature levels. However, this control is constrained by heavy computations and long time to train due to large training data required. Because of these constraints, various studies have been carried out on improve the Intelligent control technique contributing to the birth of ANFIS-PSO based MPPT that makes use of Particle Swarm Optimization (PSO) to make the PV panel to operate at the global best (Gbest) position but not the global minimum operating point. This combination provides fast response, precise and accurate PV tracking under varying irradiance and temperature conditions. Currently, this hybrid technique is being utilized in MPPT control of Grid connected PV systems.