AI Controller-Based Hybrid Grid Integration of Photovoltaic and Doubly-Fed Wind Power Systems with Wind Turbine, Drive Shaft, Generator, Control System, and Grid Simulation Models

The integration of photovoltaic systems and doubly-fed wind power into hybrid power grids controlled by artificial intelligence (AI) controllers has garnered significant attention in recent years. This approach employs advanced simulation models implemented through MATLAB/Simulink environments to analyze the dynamic performance of key components including wind turbine aerodynamics, transmission shaft torsional dynamics, doubly-fed induction generator (DFIG) electrical characteristics, AI-based control systems implementing fuzzy logic or neural network algorithms, and power grid stability parameters. Researchers utilize AI controllers to optimize power generation through real-time maximum power point tracking (MPPT) algorithms for photovoltaic arrays and pitch control systems for wind turbines. The control system architecture typically includes signal processing blocks for sensor data acquisition, adaptive PID controllers with self-tuning capabilities, and grid synchronization modules implementing phase-locked loop (PLL) techniques. Simulation models incorporate mathematical representations of mechanical drive trains using two-mass model formulations, electrical generator dynamics through dq-axis transformation equations, and power flow analysis using Newton-Raphson iterative methods. The development of such integrated systems is critical for meeting renewable energy demands while reducing fossil fuel dependency. Current research focuses on implementing machine learning algorithms for predictive maintenance and reinforcement learning for optimal energy dispatch, with continuous advancements expected in grid resilience and energy management systems.