In this paper, we have presented a simulation study to analyze the power quality of three phases medium voltage grid connected with distribution generation (DG) such as photovoltaic (PV) farms and its control schemes. The system uses two-stage energy conversion topology composed of a DC to DC boost converter for the extraction of maximum power available from the solar PV system based on incremental inductance technique and a three-level voltage source inverter (VSI) to connect PV farm to the power grid. To maintain the grid voltage and frequency within tolerance following disturbances such as voltage swells and sags, a fuzzy logic-based Dynamic Voltage Restorer is proposed. The role of the DVR is to protect critical loads from disturbances coming from the network. Different fault conditions scenarios are tested and the results such as voltage stability, real and reactive powers, current and power factor at the point of common coupling (PCC) are compared with and without the DVR 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.

Wind energy conversion systems (WECSs) require robust and efficient control strategies to ensure optimal energy conversion. This study proposes a nonlinear and resilient control approach using a fractional‐order proportional integral‐ and fractional‐order proportional derivative (FOPI–FOPD) controller for direct power regulation of a doubly fed induction generator (DFIG)–based WECS. To meet the control objectives, two cascaded FOPI–FOPD controllers were designed, resulting in 12 parameters requiring precise tuning. To optimize these parameters, the Gazelle optimization algorithm (GOA) was employed, targeting the minimization of key performance‐based cost functions: mean error (ME), mean absolute error (MAE), mean‐square error (MSE), and integral time absolute error (ITAE). These functions integrate dynamic response criteria such as overshoot, rise time, and settling time. Simulation results highlight the effectiveness of the GOA‐tuned FOPI–FOPD controller, particularly when using ITAE as the optimization criterion. The controller significantly reduces power ripples by 86.13% in active power and 75.66% in reactive power. It also improves transient response by reducing rise time by 0.035 ms, settling time by 0.3 ms, and completely eliminating overshoot. Moreover, the proposed strategies lower the current total harmonic distortion (THD) by approximately 21.43% compared to the basic strategy. The proposed ITAE–GOA–FOPI–FOPD technique ensures system stability and enhances performance across various operating conditions.

This article explores the transformative potential of Artificial Intelligence (AI) in reshaping Algeria’s industrial landscape within the broader framework of digital transition. Focusing on key sectors such as energy, agriculture, and manufacturing, the study examines how AI technologies ranging from machine learning and computer vision to natural language processing are being leveraged to enhance efficiency, safety, and decision-making across industrial operations. Through a detailed case study of the national energy leader, Sonatrach, the research highlights measurable improvements including reduced equipment downtime, more accurate resource forecasting, and accelerated data analysis. The article also discusses the alignment of these technological advancements with Algeria’s national digital strategy, while critically addressing persistent challenges such as data infrastructure, workforce readiness, and sectoral scalability. Ultimately, this study offers academic and policy-driven insights into how AI can serve as a catalyst for sustainable industrial modernization in Algeria .