In response to the escalating global energy crisis, the motivation for this research has been derived from the need for sustainable and efficient energy solutions. A gap in existing renewable energy systems, particularly in terms of stability and efficiency under variable environmental conditions, has been recognized, leading to the introduction of a novel hybrid system that combines photovoltaic (PV) and wind energy. The innovation of this study lies in the methodological approach that has been adopted, integrating dynamic modeling with a sophisticated control mechanism. This mechanism, a blend of model predictive control (MPC) and particle swarm optimization (PSO), has been specifically designed to address the fluctuations inherent in PV and wind power sources. The methodology involves a detailed stability analysis using Lyapunov’s theorem, a critical step distinguishing this system from conventional renewable energy solutions. The integration of MPC and PSO, pivotal in enhancing the system’s adaptability and optimizing the maximum power point tracking (MPPT) process, improves control efficiency across key components like the doubly fed induction generator (DFIG), rectifier-sourced converter (RSC), and grid-side converter (GSC). Through rigorous MATLAB simulations, the system’s robust response to changing solar irradiance and wind velocities has been demonstrated. The key findings confirm the system’s ability to maintain stable power generation, underscoring its practicality and efficiency in renewable energy integration. Not only has this study filled a crucial gap in renewable energy control systems, but it has also set a precedent for future research in sustainable energy technologies.

In the field of optimizing wind system control approaches and enhancing the quality of electricity generated on the grid, this research makes a fresh addition. The Sliding Mode Control (SMC) technique produces some fairly intriguing outcomes, but it has a severe flaw in the oscillations (phenomenon of reluctance: chattering) that diminish the system’s efficiency. In this paper, an AST (adaptive super twisting) approach is proposed to control the wind energy conversion system of the permanent magnet synchronous generator (PMSG), which is connected to the electrical system via two converters (grid-side and machine-side) and a capacitor serves as a DC link between them. This research seeks to regulate the generator and grid-side converter to monitor the wind rate reference given by the MPPT technique in order to eliminate the occurrence of the chattering phenomenon. With the help of this approach, precision and stability flaws will be resolved, and the wind system will perform significantly better in terms of productivity. To evaluate the performance of each control in terms of reference tracking, response time, stability, and the quality of the signal sent to the network under different wind conditions, a detailed description of the individual controls is given, preceded by a simulation in the Matlab/Simulink environment. The simulation study validates the control method and demonstrates that the AST control based on the Lyapunov stability theory provides excellent THD and power factor results. This work is completed by a comparative analysis of the other commands to identify the effect on the PMSG wind energy conversion system.

To propel us toward a greener and more resilient future, it is imperative that we adopt renewable sources and implement innovative sustainable solutions in response to the escalating energy crisis. Thus, renewable energies have emerged as a viable solution to the global energy crisis, with photovoltaic energy being one of the prominent sources in this regard. This paper represents a significant step in the desired direction by focusing on detailed, comprehensive dynamic modeling and efficient control of photovoltaic (PV) systems as grid-connected energy sources. The ultimate goal is to enhance system reliability and ensure high power quality. The behavior of the suggested photovoltaic system is tested under varying sun radiation conditions. The PV system is complemented by a boost converter and a three-phase pulse width modulation (PWM) inverter, with MATLAB software employed for system investigation. This research paper enhances photovoltaic (PV) system performance through the integration of model-predictive control (MPC) with a high-gain DC–DC converter. It improves maximum power point tracking (MPPT) efficiency in response to the variability of solar energy by combining MPC with the traditional incremental conductance (IN-C) method. Additionally, the system incorporates a DC–AC converter for three-phase pulse width modulation, which is also controlled by predictive control technology supported by Particle Swarm Optimization (PSO) to further enhance performance. PSO was selected due to its capability to optimize complex systems and its proficiency in handling nonlinear functions and multiple variables, making it an ideal choice for improving MPC control performance. The simulation results demonstrate the system’s ability to maintain stable energy production despite variations in solar irradiation levels, thus highlighting its effectiveness.