چكيده به لاتين
Over the past decade, the use of renewable energy sources, such as wind and solar, has expanded dramatically, and these sources have been replaced by fossil fuels due to fossil fuel pollution. Considering the fact that renewable resources are heavily dependent on weather conditions, it is essential to use some methods to achieve maximum power. For this purpose, the use of algorithms such as perturbation and observation and incremental conductance is very popular. In this thesis, an improved incremental conductance algorithm, which is 2.66 times faster than conventional incremental conductance, is used. Then, the wind-solar energy model and various methods for achieving maximum power in them have been explained. Because the existence of DC-DC converter is essential to achieve maximum power and given that multi-input converters not only are simpler, more cost-effective, and more efficient, but also have a better performance than several independent one input converters. Threrfore, in the suggested wind-solar hybrid system, instead of using multiple independent converters, a high-efficiency multi-input DC-DC boost converter is used to achieve maximum power. To track the maximum power point more precisely and quickly, using classical controllers such as PI is very common. But the dependence of controller parameters on the operating point and on model of the system, highlights the need to use nonlinear controllers more than ever before.
The predictive control method is one of the new methods used to achieve maximum power. In this method the switching operation is done according to the discrete model of the system and minimization of the cost function. For this purpose, the currents of wind and solar resources are controlled by predictive control in order to reach the reference value maximum power point. Compared with the classic PI method, the speed of the predictive controller has doubled and fluctuation has dropped by 2.5%. In addition, the steady state error in the prediction method has dropped by 0.8%. All simulation results are presented using the MATLAB software version R2016b.
