The nonlinear characteristics and low efficiency of photovoltaic (PV) systems remain critical challenges that necessitate advanced solutions. This study proposes two innovative Maximum Power Point Tracking (MPPT) algorithms based on the Whale Optimization Algorithm (WOA) and Grey Wolf Optimization (GWO). The primary advantage of these methods lies in their adaptive step-size optimization, leveraging multiple criteria to determine the optimal step size. A novel fitness function was developed to improve tracking accuracy, minimize ripple, and reduce overshoot. Simulation results demonstrated remarkable improvements, including up to 98% reduction in ripple, 67% reduction in overshoot, and significant improvements in tracking accuracy compared to fixed-step methods. Field validation was conducted using real-world data from the Ain El Melh PV station in Algeria on June 21, 2023. Experimental results confirmed the effectiveness of the proposed methods, with the WOA-based MPPT achieving up to 99% ripple reduction and 40% overshoot reduction under dynamic environmental conditions. A comparative analysis of MPPT algorithms revealed superior performance metrics for the bio-inspired methods. The PO-WOA algorithm achieved the highest efficiency of 98.87% in simulation and 98.94% in real data, surpassing both PO and PO-GWO. It also minimized power loss to 0.56 W in simulation and 0.39 W in real data, demonstrating its optimization capabilities under fluctuating conditions. Although its response time was slightly longer than other methods, at 0.65 s in simulation and 0.48 s in real data, it prioritized stability and precision. These findings underscore the potential of WOA and GWO algorithms to enhance PV system performance, offering robust and efficient solutions for optimizing energy output in both simulation and real-world scenarios.

In this work, we have developed two new intelligent maximum power point tracking (MPPT) techniques for photovoltaic (PV) solar systems. To optimize the PWM duty cycle driving the DC/DC boost converter, we have used two optimization algorithms namely the whale optimization algorithm (WOA) and grey wolf optimization (GWO) so we can tune the PID controller gains. The oscillation around the MPP and the fail accuracy under fast variable isolation are among the well-known drawbacks of conventional MPPT algorithms. To overcome these two drawbacks, we have formulated a new objective fitness function that includes WOA/GWO based accuracy, ripple, and overshoot. To provide the most relevant variable step size, this objective fitness function was optimized using the two aforementioned optimization algorithms (i.e., WOA and GWO). We have carried out several tests on Solarex MSX-150 panel and DC/DC boost converter based PV systems. In the simulation results section, we can clearly see that the two proposed algorithms perform better than the conventional ones in term of power overshoot, ripple and the response time.

Recently, automotive manufacturers have prioritized cruise control systems and controllers, recognizing them as essential components requiring precise and adaptable designs to keep up with technological advancements. The motion of vehicles is inherently complex and variable, leading to significant non-linearity within the cruise control system (CCS). Due to this non-linearity, conventional PID controllers often perform suboptimally under varying conditions. This research introduces a fractionalized-order PID (FrOPID) controller, which has an extra parameter that makes regular PID controllers work better. A comparative analysis is conducted between classical PID controllers and FrOPID controllers optimized using three metaheuristic algorithms: Harris Hawks Optimization (HHO), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). The evaluation is carried out using a linearized model of the vehicle cruise control system (VCCS). The results demonstrate that fractionalized-order PID controllers significantly outperform conventional PID controllers, particularly regarding rise time and settling time. Among the designs that were considered, the one that combines HHO and FrOPID works the best at finding a balance between responsiveness and stability. It is also the most durable and flexible, able to adapt to changes in vehicle mass and environmental conditions. This highlights the effectiveness of fractionalized-order controllers in managing the dynamic behavior of vehicles.

In this study, we engineered an advanced electrolyzer utilizing Matlab Simscape. Our primary objective is to Green hydrogen production has emerged as a cornerstone for achieving sustainable energy transitions. Electrolyzer systems play a pivotal role in converting renewable energy into hydrogen, yet their performance is heavily influenced by the design, operation, and integration with renewable energy sources. This study presents an advanced modeling and simulation framework for electrolyzers, emphasizing the optimization of green hydrogen production under dynamic operational conditions. The proposed model incorporates key factors such as temperature, pressure, and electrical input variability to accurately predict electrolyzer performance. By integrating renewable energy sources, such as photovoltaic (PV) systems, and employing novel optimization techniques, this study demonstrates significant improvements in efficiency and hydrogen yield. Comprehensive validation is performed using real-world data, highlighting the model's applicability for large-scale deployment. This research provides a robust foundation for developing cost-effective, efficient, and sustainable green hydrogen production systems, addressing critical challenges in renewable energy integration and contributing to the global energy transition.