Enhance quality of Power in Grid Tie Solar Photovoltaic System using Deep learning MPPT

The energy market is being forced to shift towards sustainable energy due to the increase in energy demand caused by population growth, industrialization, urbanisation and a notable increase in greenhouse gas emissions from conventional energy sources. Solar PV systems are becoming widely used due to the growing need for renewable energy; nevertheless, integrating these systems comes with a number of difficulties. Keeping the grid stable, controlling voltage, protecting the grid, following grid codes and standards, attaining system flexibility, and taking market and regulatory considerations into account are some of these problems. Considering photovoltaic systems produce random power, maximum power point tracking (MPPT) methods must be used in these kinds of systems. Here, local fluctuations at the peak of power plague the traditional perturb and observe (P&O) approach. On the other hand, smart control methods, such as deep learning-based control, have been shown to improve nonlinear systems' performance. Consequently, an attempt is presented in this research to distinguish between the grid-connected solar photovoltaic system's operational performance using Deep Learning Control-based MPPT and Traditional P&O-based MPPT. Matlab Simulink is used to simulate the suggested model, and the output is examined to evaluate the system's efficiency and performance.