An Efficient Hybrid Algorithm for Optimal PID Control of Fuel Cell Power Systems

The growing global demand for clean, stable, and sustainable energy has underscored both the limitations of fossil fuels and the intermittency issues inherent to renewable energy sources. As a result, fuel cells have emerged as a promising complementary technology capable of providing efficient and continuous power through electrochemical energy conversion. Within fuel cell power systems, the boost converter plays a pivotal role by regulating the inherently low and variable output voltage, making precise and reliable control essential to achieving high efficiency, system stability, and robust dynamic performance. Although numerous linear and nonlinear control methods have been explored, PID controllers remain one of the most practical and cost-effective solutions for systems with moderate load variations, particularly when their parameters are optimally tuned. This paper introduces a novel hybrid optimization algorithm for optimal PID tuning in fuel-cell-based boost converter systems, integrating Ant Colony Optimization (ACO), Grey Wolf Optimizer (GWO), and Bald Eagle Search (BES). The proposed method incorporates pheromone initialization based on the quality of initial solutions to accelerate convergence, controlled noise injection during the exploitation phase to prevent premature convergence, and a weighted pheromone-driven search mechanism to enhance the exploration of promising regions. While GWO contributes robust global exploration capability, BES reinforces accurate local exploitation. Collectively, the proposed

کلیدواژه ها:

Fuel cell ، boost converter ، PID controllers