Optimization of Maximum Power Point Tracking (MPPT) of Photovoltaic System using Artificial Intelligence (AI) Algorithms

Although solar energy is one of the most available renewable energy resources, its usage is strongly influenced by environmental and technological aspects. Photovoltaic (PV) offers an environmentally friendly source of electricity, which is however still relatively costly today. In order to minimize the output power cost, the maximum power point tracking (MPPT) of the PV output for all sunshine conditions is a key factor to maximize the power output of a PV system for assigned conditions of radiation and temperature. The core of the MPPT is represented by the implemented algorithm devoted to find and maintain the operation near to the Maximum Power Point (MPP). In this paper, starting from the set of equations modeling a PV module, an innovative procedure to optimize the performance and efficiency of the MPPT algorithms is presented, simulated and verified. Artificial intelligence algorithms, specifically PSO and PSO combined with Incremental conductance algorithm, are used to achieve the stated goals. Studies on the conventional and intelligent algorithms are conducted and a comparison between their efficiencies and drawbacks is presented. Flowcharts are developed and simulation tools are identified. MATLAB simulations are shown and resulting graph and efficiencies under several irradiance values are specified along with comparison with the previously achieved performance of MPPT algorithms and different set of Data