Parameter estimation and validation of cascaded DC-DC boost converters for renewable energy systems using the IGWO optimization algorithm

The voltage amplitude generated by renewable energy sources is often unstable, necessitating the use of power electronic circuits for effective grid integration. Among these, DC-DC converters play a critical role in maintaining a constant DC link voltage, typically 400 V or 800 V, at the input of inverter circuits that supply power to the load or the grid. The study focuses on the voltage gain behavior of a high-gain dual cascaded DC-DC boost converter designed for PV (photovoltaic) power systems. Using ANSYS Electronics software with its parametric solver, a comprehensive dataset was generated based on key parameters such as input voltage, power switch duty ratio, and switching frequency.
The Improved Grey Wolf Optimizer (IGWO) algorithm was employed to estimate mathematical models for this dataset using linear and quadratic equations. The accuracy of the proposed models was validated across six test scenarios, demonstrating superior performance compared to traditional optimization algorithms, including Harmony Search (HS), Particle Swarm Optimization (PSO), Differential Evolution (DE), and the standard Grey Wolf Optimizer (GWO). Experimental validations yielded output voltages of 23.5 V and 36.1 V for input voltages of 4.8 V and 6.2 V, respectively, closely aligning with simulation results of 23.113 V and 36.447 V.
The findings, supported by detailed simulations and graphical analyses, highlight the IGWO algorithm's precision and reliability in predicting converter output voltages under variable input conditions. This work advances renewable energy systems integration by enhancing the modeling and performance of cascaded DC-DC boost converters.