Power quality improvement of solar energy system with of the photovoltalic with deep neural network controller

Power quality control has an important place in electrical energy production, especially in renewable energy systems. In this study, power quality of PV solar energy system with deep learning artificial neural network was analyzed and investigated. Generally, certain alternative power generation sources, including wind, solar, and hydropower are not detrimental to nature. For this reason, solar and wind power have been declared as useful alternative energy resources, and besides, they are abundant. In this paper the performances of solar energy systems in weather conditions changing are investigated. This paper has been written with a major objective to suggest a new algorithm, which is based on deep neural network, and to apply it for maximum power point tracking. Today, solar power is very popular alternative energy source due to its enormous availability in nature. In this thesis, the photovoltaic cell systems will investigate under various weather conditions. Based on the findings, the developed an advanced intelligent controller system that tracks the maximum power point. The Maximum Power Point Tracking controller is a must for the renewable energy sources due to unpredictable weather conditions. High power quality ideally produces electrical power that is always available, completely pure and noise-free, has a sinusoidal waveform, and is always within voltage and frequency tolerances. In this paper, a new method is implemented on the solar energy system for improving the power quality based on the deep learning neural network. In this study, a new approach and energy efficiency algorithm are presented for the optimum design of the grid-connected PV system with battery storage installation in the engineering faculty building of Baghdad University.Throughout our study, the simulations were tested in Matlab 2020a version.