The PV modules have a major impact on the global transition to renewable energy sources. However, one of the most critical challenges in PV systems is the Maximum Power Point Tracking (MPPT) control. The key objective of this paper is to implement intelligent MPPT based on an online optimization technique by Grey Wolf Optimizer (GWO) to Artificial Neural Network (ANN) called ANN-GWO for a PV modules power plant 400kW. The ANN-GWO methodology is designed to enhance the efficiency of the PV system by adapting to varying environmental conditions. The ANN-GWO aims to optimize the MPPT process dynamically, ensuring maximum power extraction from the PV modules. This novel approach outperforms the conventional Perturb and Observe (P&O) method, a Proportional-Integral-Derivative (PID) controller and ANN-GWO techniques by adapting more effectively to rapid changes in sun irradiance and temperatures applied to the proposed PV panels. Overall, the result of the proposed ANN- GWO is a more reliable and consistent energy yield, even under fluctuating weather conditions, making the ANN-GWO a promising solution for large-scale solar energy applications. MATLAB/SIMULINK (Version R2022b) has been utilized to execute the simulation and obtain the findings which provided a comprehensive analysis of the proposed photovoltaic (PV) system.

One of the key parts of overhead transmission lines that support and insulate the power transmission line is the string insulator discs. This need involves a meticulous design of string insulators for achieving high string efficiency, this is necessary for efficient power transmission. This paper has two aims, first improving and developing the efficiency of string insulators of overhead transmission lines 33kV and 132kV to be efficient, properly insulated and safe. Based on an optimum value of the capacitance (self-capacitance) which can be achieved via the proposed algorithm named Modified Particle Swarm Optimization (MPSO) technique which is the second aim of this paper. The MPSO algorithm tuned the self-capacitance (Cnew) as well as operating the model at the high efficiency of the string insulators with respect to its mechanical effectiveness. Moreover, analysis and evaluations of the systems (33kV and 132kV) as a classic model, proposed model and the MPSO have been carried out by MATLAB/Simulink R2022b environment. On the basis of the simulation findings, this paper produced a specific theoretical design for two transmission line systems 33kV and 132kV; where remarkable that the string efficiencies of 33kV and 132kV systems increased by 14.24% and 64.54% respectively. Also, compared the results between the proposed MPSO and conventional PSO.

This study proposes an intelligent controller for a hydrogen-powered plug-in fuel cell hybrid electric vehicle (FCHEV) that integrates a fuel cell (FC) with two energy storage systems, which are ultracapacitor (UC) and battery (BAT), which results in a high dynamic response along with maintaining efficient use of resources for energy storage. Moreover, this controller works on effectively managing the system power flow to reduce the amount of power needed for the FCHEV. An effective energy management system (EMS) has been developed that utilizes the fuzzy logic controller (FLC) and artificial neural networks (ANNs) to achieve the EMS requirements. Also, the proposed system operates these three power sources at high efficiency with their mechanism performance, meets load power demands efficiently, and uses less hydrogen. Furthermore, the Crow Search Algorithm (CSA) and Particle Swarm Optimization (PSO) methods are utilized to adjust the parameters of the wavelet neural network that is connected to the PI controller, called WNN-PI. The DC-DC converters control the output voltage of the FC and BAT for maintaining the DC-bus voltage constant at 300 volts. The state-of-charge (SOC) for the BAT and UC is also considered in this study. The proposed system is analyzed and evaluated using the MATLAB/Simulink environment, and two vehicle driving cycles were implemented using the ADVISOR Simulator.

This paper presents three hybrid AC/DC topologies for the CIGRE European low-voltage benchmark grid to evaluate their impact on voltage regulation, current compliance, and power-sharing capability under realistic operating conditions. The proposed topologies integrate a dedicated DC network in parallel with the existing AC infrastructure through voltage source converters (VSCs), enabling controlled power exchange between the two subsystems. This structure facilitates improved voltage support and more flexible integration of distributed renewable energy resources, many of which inherently operate in DC. A decentralized droop-based control strategy is employed as a uniform baseline to control the VSCs and assess the intrinsic performance of each topology. The proposed architectures are evaluated using realistic 24-h load profiles under scenarios with and without droop control. The results demonstrate significant improvements in voltage stability and feeder current management, particularly under high DC penetration conditions. Overall, the study provides a reproducible benchmark framework for topology-level comparison of hybrid AC/DC low-voltage distribution networks.