MPPT controller based on Machine learning for stand-alone Photovoltaic array

Abstract

The output of solar energy from photovoltaic cells has different functions related to different algorithms. In paper, an efficient algorithm for lifting maximum-power tracking (MPP) track with solar power systems to transform the use of machine learning (ML) to pre-existing and virtual reality (P&O) the way. P&O operates on the basis of various cycles of action by the MPP and is itself the most probable and accurate algorithm. However, the speed of assembly in the MPP is usually small in this way and it varies in different climatic conditions. This paper describes the use of ML in reducing, the downtime results in a significant increase in MPP estimates. Recommended the algorithm predicts MPP based on the fast rates of solar radiation, solar cell temperature and humidity as the input features in the ML multivariate local return model and are used to download the maximum available power (MAP). The learning algorithm and as time goes on, the estimation gets closer available power. The simulation was performed with python and yielded a 99.8% efficiency in the measuring MPP after training for 83 hours. In this paper, we describe the machine learning and cooling system used in the smart home. In particular, we proposed a temperature controller based on machine learning and verified performance using real-time location data. With the experimental results, it is possible that the efficiency of the temperature control using the machine learning was verified. As a result of performance testing, the proposed system shows that there are variations in performance depending on the user's health pattern, and that the system works best if you have a particular lifestyle.