Maximum Power Point Tracking for PV Systems Using a Buck-Boost Converter
Keywords:
Buck-boost converter, Irradiance variation, MPPT, PSO algorithm, Solar PV, State-space modelingAbstract
One of the main challenges with solar photovoltaic systems is that their power output is never completely constant. It changes constantly with available sunlight and panel temperature. This means the point at which the panel delivers maximum power keeps shifting throughout the day. Without a system to track this point, a lot of energy is wasted. In this work, we address this issue by building and simulating a Buck-Boost DC-DC converter along with a particle swarm optimization (PSO) based maximum power point tracking (MPPT) controller. PSO is particularly effective here because it can quickly adjust when conditions change suddenly, like when a cloud blocks the sunlight. We implemented the entire system in simulation, using a nonlinear PV module model, averaged state-space equations for the converter, and a time-varying irradiance profile that reflects real weather patterns. We assessed the system based on how accurately it tracks the true maximum power point, how the duty cycle reacts during sudden changes, and how quickly it converges after a disturbance. Our results show that the PSO-MPPT approach achieves tracking efficiency of 85 to 92%, recovers quickly from sudden irradiance drops, and keeps the converter operating steadily. Overall, these findings support the use of optimization-based MPPT as a practical and reliable method for PV systems that operate in real-world, changing conditions.
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