AI Summary & Key Takeaways
AI InsightAdditionally, the controller performance was analyzed based on overshoot, settling time, and rise time values as shown in the resulting graphs.
📌 3 Key Takeaways
Abstract
The increasing awareness of the need for renewable and clean energy sources has become a significant agenda item, especially as global energy demand continues to rise. Studies on renewable energy systems, which provide healthier conditions for current and future generations while meeting energy demand, are becoming increasingly widespread both locally and globally. Hybrid energy systems, formed by combining multiple energy sources, have recently introduced innovative solutions for the integration and management of various energy types. However, the voltage levels obtained from these systems are often low, making it necessary to boost the voltage for storage and household use.To address this, DC-DC boost converters are used to increase the voltage generated by solar panels, wind turbines, or hybrid energy systems. PID (Proportional-Integral-Derivative) controllers are typically required for converter control. However, conventional constant-gain PID controllers and classical PID tuning methods are often ineffective, as they rely on mathematical formulations or experimental system response analyses. To overcome this challenge, meta-heuristic optimization algorithms provide a viable alternative, offering a more stable and faster system response. In this study, a hybrid energy system consisting of a Proton Exchange Membrane (PEM) fuel cell, PV panel, and wind turbine was modeled in the Matlab/Simulink environment. A DC-DC boost converter was designed to elevate the system's output voltage to the desired reference level, enhancing system stability. Three different optimization methods—Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and Artificial Bee Colony (ABC) algorithms—were employed to adjust the parameters of the PID controller used for converter control. The PID coefficients obtained through these optimization algorithms are presented and compared. The performance of the tuned PID controller was evaluated through system response analysis under variable load conditions and by calculating the Root Mean Square Error (RMSE) between the output voltage and the specified reference value. Additionally, the controller performance was analyzed based on overshoot, settling time, and rise time values as shown in the resulting graphs.
Özet
The increasing awareness of the need for renewable and clean energy sources has become a significant agenda item, especially as global energy demand continues to rise. Studies on renewable energy systems, which provide healthier conditions for current and future generations while meeting energy demand, are becoming increasingly widespread both locally and globally. Hybrid energy systems, formed by combining multiple energy sources, have recently introduced innovative solutions for the integration and management of various energy types. However, the voltage levels obtained from these systems are often low, making it necessary to boost the voltage for storage and household use.To address this, DC-DC boost converters are used to increase the voltage generated by solar panels, wind turbines, or hybrid energy systems. PID (Proportional-Integral-Derivative) controllers are typically required for converter control. However, conventional constant-gain PID controllers and classical PID tuning methods are often ineffective, as they rely on mathematical formulations or experimental system response analyses. To overcome this challenge, meta-heuristic optimization algorithms provide a viable alternative, offering a more stable and faster system response. In this study, a hybrid energy system consisting of a Proton Exchange Membrane (PEM) fuel cell, PV panel, and wind turbine was modeled in the Matlab/Simulink environment. A DC-DC boost converter was designed to elevate the system's output voltage to the desired reference level, enhancing system stability. Three different optimization methods—Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and Artificial Bee Colony (ABC) algorithms—were employed to adjust the parameters of the PID controller used for converter control. The PID coefficients obtained through these optimization algorithms are presented and compared. The performance of the tuned PID controller was evaluated through system response analysis under variable load conditions and by calculating the Root Mean Square Error (RMSE) between the output voltage and the specified reference value. Additionally, the controller performance was analyzed based on overshoot, settling time, and rise time values as shown in the resulting graphs.