INTELLIGENT MODEL FOR MAXIMIZING THE GENERATED POWER OF A RECONFIGURABLE SOLAR POWER PLANT

Abstract

The global maximum power point tracking of a solar power plant in partial shading demands a global optimization. Standard algorithms for tracking of maximum power point do not provide for a maximum global power of a solar power plant during real time mode due to low convergence. A model of aximizing the generated power of a reconfigurable solar power plant was developed as a modified fuzzy deep neural network based on the modified quantum-behaved particle swarm optimizer. This neural network consists of the following: convolutional units, recurrent neural networks, and fuzzy units. By processing the sensor signals and images of the solar array, the set modified fuzzy deep neural network generates a reference voltage and an electrical interconnection matrix of the parallel-serial solar array, maximizing its power under non-uniform insolation. The neural network demonstrates such advantages as robustness, better efficiency, and tracking speed in comparison with the model of a reconfigurable solar power plant based on the particle swarm optimization.