Solar Power Control System based on Machine Learning Methods

The article describes the control system of a solar power plant based on machine learning technologies. Neural network technologies have been used to control the distribution of electricity produced in a solar power plant. In this paper, it is proposed to use a neural network to track the point of maximum power, for more efficient charge control. This is a method of regulating the battery charge to increase the amount of electricity received. The neural network manages the controllers that manage the process and provide efficient current increase only when the output voltage of the solar panel above the battery voltage. A multilayered neural network was used to implement the intelligent control system with a training procedure based on the back propagation algorithm. After training, the system finds the point of maximum power and takes into account the current charge of the batteries for the redistribution of voltage and current, which results in an increase in the power of the system compared to the scheme on traditional controllers.