The aim of this research is to manage the voltage of an active distribution grid with a low X/R ratio and multiple Photovoltaic Distributed Generators (PVDGs) operating under varying conditions. This is achieved by providing a methodology for coordinating three voltage-based controllers implementing an Adaptive Neuro-Fuzzy Inference System (ANFIS). The first controller is for the On-Line Tap Changer (OLTC), which computes its adequate voltage reference. Whereas the second determines the required Active Power Curtailment (APC) setpoint for PVDG units with the aim of regulating the voltage magnitude and preventing continuous tap operation (the hunting problem) of OLTC. Finally, the last component is an auxiliary controller designed for reactive power adjustment. Its function is to manage voltage at the Common Coupling Point (CCP) within the network. This regulation not only aids in preventing undue stress on the OLTC but also contributes to a modest reduction in active power generated by PVDGs. The algorithm coordinating between these three controllers is simulated in MATLAB/SIMULINK and tested on a modified IEEE 33-bus power distribution grid (PDG). The results revealed the efficacy of the adopted algorithm in regulating voltage magnitudes in all buses compared to the traditional control method.

In this work, an Adaptive Fuzzy Logic Controller is studied to optimize the transfer of the power provided by a photovoltaic generator. The adaptation process is carried out online in two tasks: the adaptation of the rules consequences and the self-organization of the internal structure of the Fuzzy Controller. In comparison with two types of traditional controls, the performance of the controller studied is validated. The simulation results show that the controller studied makes it possible to reduce the response time by 3 % compared to the conventional controller, and minimizes the steady-state error by eliminating the phenomenon of oscillation around the PPM and that the proposed controller exhibits good behavior with a wide range of power.

This work discusses an enhanced photovoltaic system using the Particle Swarm Optimization algorithm. The proposed method has been evaluated under several scenarios of partial shading, and the results obtained are contrasted with the Perturb and Observe technique. Hence, it has shown its effectiveness in locating the global maximum power point whatever the meteorological conditions, especially in inhomogeneous conditions. This technique makes it possible to enhance the effectiveness of the photovoltaic system studied because it makes it possible to minimize the loss of power in the case of non-homogeneous irradiation conditions.

The increasing integration of renewable energy sources (RES) is accelerating the shift from traditional synchronous machine-based power systems to inverter-dominated grids. This transition poses significant frequency stability challenges, as power electronic interfaces lack the inherent kinetic energy storage of conventional generators, resulting in low system inertia. To address these challenges, this study proposes an adaptive virtual inertia control system based on fuzzy logic, which offers notable advancements in frequency dynamics. The proposed controller dynamically adjusts the virtual inertia constant in real-time by leveraging inputs such as frequency and the rate of change of frequency (RoCoF). This adaptive approach overcomes the limitations of fixed inertia systems, ensuring improved frequency stability and superior transient performance during load disturbances. Simulation results validate the system's effectiveness, showing reduced frequency overshoots, minimized deviations, and faster recovery to nominal frequency compared to conventional fixed inertia methods. By rapidly damping oscillations and enhancing transient stability, the proposed system significantly outperforms traditional techniques. Moreover, the study reviews current virtual inertia strategies, control topologies, and explores future research directions for integrating advanced virtual inertia into modern grids. These findings demonstrate the robustness of fuzzy logic based adaptive inertia for stabilizing low-inertia microgrids with high RES penetration.