Intelligent control based hybrid renewable energy system for grid applications

The focus of the work is the augmentation of Power Quality available into the Alternating Current (AC) grid through a hybrid Remote Electrical Tilt (RET) comprising arrays of solar photovoltaics and wind turbines (WTs). Hybrid energy systems present a difficult control problem due to their ever-changing operational points on highly variable and nonlinear renewable sources. The applications suffer when they are subjected to this kind of dynamism and uncertainty by the old control algorithms like fuzzy logic, hybrid fuzzy–Proportional-Integral (PI), and cascaded Proportional-Integral–Proportional-Resonant (PI-PR) pairs. An artificial-intelligence-based control algorithm thus presents an answer that can overcome the limitations of traditional power control methods in favor of the features common in modular and helpful controllers for the highly variable and nonlinear system. Mathematical formulations for solar cells, WT, and the neural network-based control algorithm are presented in the thesis. Furthermore, the behavior of the controller is simulated and verified numerically in the MATLAB/Simulink software under various operating conditions and modes. The results at the end demonstrate very clearly that the artificial intelligence controller has indeed augmented the Power Quality greatly, enhancing the dynamic performance of the hybrid power conversion systems, thus giving strong promise for artificial intelligence techniques in advanced renewables integration.