چكيده به لاتين
In recent years, due to problems such as energy shortages and environmental pollution, renewable energy has attracted much attention. One of the main steps in this way is the use of solar energy. The efficiency of a photovoltaic system depends on weather conditions. The change in temperature and radiation level changes the output power of the system. Photovoltaic power generation systems are usually set up, using maximum power point tracking to maximize the output of the photovoltaic module's current and voltage output.
Model predictive control is a new control method that has recently been used extensively to control power converters. This method has also been used in photovoltaic systems in the last two decades. In this thesis, a thorough evaluation and analysis of the maximum power point tracking methods based on the model predictive control applied to the Boost converter is provided. In the following, a new method that uses the concept of model predictive control to estimate the behavior of the photovoltaic modules in each step and to achieve low oscillations around the maximum power point is presented. The simulation results of this method are compared with other methods of mode predictive control. In general, all model predictive control methods have the same tracking speed and all of the methods examined in the thesis have reached their maximum value at a given time. But these methods are different in the case of stable state oscillations. The proposed method in the thesis has low oscillations in the radiation below 800W/m^2, but this method is very large in excess of 1000W/m^2 radiation. This method is combined with a new method of predictive control, which produces low oscillations in high radiation, so that it has low output oscillations.
Keywords: Solar Cells, Photovoltaic Systems, Maximum Power Point Tracking, Model Predictive Control
