Optimization algorithm based maximum power point tracking system design for solar energy system

Since the beginning of the 2000s, rapid developments in technology and the increase in the world population approaching 30% increased the need for energy demand day by day. The increase in energy demand and the limited fuel reserves in fossil fuel power generation plants have focused the researchers on the use of sustainable or renewable energy sources. Today, wind, solar, and hydroelectric energy sources are among the most widely used renewable energy sources. Solar energy systems are defined as energy sources with varying operating characteristics under different atmospheric conditions. To achieve higher efficiency in changing operating conditions, these systems must be operated at maximum power points. Both classical and artificial intelligence-based algorithms are used to operate solar energy systems at the maximum power point. Advances in technology have made it easier to use algorithms based on artificial intelligence in these systems. In this thesis, the design of the maximum power point tracking algorithm based on cuckoo search, gray wolf algorithm, and particle swarm optimization algorithms is explained in detail. Simulation studies of the proposed maximum power point tracking algorithms for off-grid solar energy systems under constant irradiation, constant temperature, and partial shaded atmospheric conditions are carried out. The results obtained from the proposed methods were compared among themselves and their success in finding the maximum power point was evaluated. According to the comparison results, it can be stated that the results obtained from the gray wolf algorithm under changing atmospheric conditions are more successful than other algorithms in finding the maximum power point.