In this study, a ResNeSt-based deep learning approach to beamforming for 5G massive multiple-input multiple-output (MIMO) systems is presented. The ResNeSt-based deep learning method is harnessed to simplify and optimize the beamforming process, consequently improving performance and efficiency of 5G and beyond communication networks. A study of beamforming capabilities has revealed potential to maximize channel capacity while minimizing interference, thus eliminating inherent limitations of the traditional methods. The proposed model shows superior adaptability to dynamic channel conditions and outperforms traditional techniques across various interference scenarios.

The Fluctuations in demand and weather conditions have a significant impact on the frequency and the voltage of Algeria's isolated PIAT power grid. To maintain stability and reliable power supply, it is crucial to keep these quantities close to their expected levels. An automatic (FRR) is employed to regulate real-time frequency deviations caused by integrating variable renewable energy (VRE), specifically wind and solar power in the Kabertene region. In order to mitigate wind power fluctuations, a power system stabilizer is implemented, which helps dampen oscillations. The use of Maximum Power Point Tracking (MPPT) techniques optimizes the extraction of power from solar panels under varying conditions. For efficient scheduling and dispatch of VRE generation, particle swarm optimization (PSO)-based algorithms are used. These algorithms ensure optimal utilization of renewable energy sources by considering their intermittent nature. This study proves the effectiveness of these techniques in enhancing grid stability, reducing frequency deviations, and improving VRE integration. Valuable insights are provided on their practical implementation, playing a crucial role in transitioning to a cleaner and more sustainable energy system.

Credible control and overall stabilization of closed-loop nonlinear systems presented in a port-controlled hamiltonian structure (PCH-D) were forever the development of the simultaneous interconnection and damping assignment passivity-based control (SIDA-PBC) method. The robustness and reliability of the method called into question against noise and certainly modelling errors. Indeed, a new scheme has been presented to control a doubly fed induction generator (DFIG) based on the energy form and exploiting the electrical parameters of the closed loop system. The results obtained provide a new technique and implement more freedom when designing the diagram of the advanced controller during the production of active power. The contribution of this paper is researching on the advanced nonlinear methodes used, many simulations were carried out in simulation using the MATLAB/Simulink environment under important operating conditions, allowing to demonstrate the feasibility of the proposed method and verify the performance considering the robustness.