Abstract:
The integration of DC microgrids with renewable energy sources has revolutionized power production. This study presents a multi-source DC microgrid system incorpo- rating various sources, such as photovoltaic , wind energy, fuel cells, superconducting magnetic energy storage, batteries, and supercapacitors. The system’s mathematical modeling and state space representation are developed, along with control law synthe- sis. An artificial neural network is utilized for maximum power point tracking of the PV and wind turbine systems during daylight hours, while a fuzzy logic controller is employed for energy storage technologies to mitigate load variations. To ensure ef- fective control, several advanced control laws are designed, including the conditioned adaptive barrier function integral terminal sliding mode control , barrier function super- twisting double integral sliding mode control , barrier function supertwisting integral sliding mode control , and barrier function super twisting sliding mode control. The gains of these controllers are optimized using an improved grey wolf optimization algo- rithm. The performance of the optimized controllers is analyzed through graphical and tabular representations. Additionally, hardware-in-loop tests are conducted using the C2000 Delfino MCU F28379D launchpad to validate the real-time efficiency of the con- trollers. Optimized using an improved grey wolf optimization algorithm, the proposed control strategies demonstrate improved stability and performance. Through exten- sive experimentation, it is determined that the CABFIT-SMC controller exhibits faster convergence and reduced chatter compared to the other controllers. The experimental results validate the effectiveness and real-time efficiency of the controllers, showcasing their potential for practical implementation in DC microgrid systems
