A High Gain Expandable DC-DC Converter with PSO-based MPPT Tracking for Partial Shading Conditions

To match up the voltage requirement at the utility or transmission side, a high-gain DC-DC converter is often required to elevate the low output voltage of solar PV. Also, to harness the peak power from the solar panel, many algorithms have been given but the conventional algorithm is only suitable for the single peak PV characteristics. In partial shading conditions, multi-peak PV characteristics are observed where a heuristic approach-based algorithm is needed to track the global peak power from PV characteristics. So, in this paper, a high-gain expandable DC-DC converter is proposed that incorporates a particle swarm optimization (PSO) based MPPT tracking algorithm for solar PV applications. The configuration of the proposed high gain converter is comprised of an L
n
C
2n-2
network and it can be expanded up to n-cells where each cell consists of one inductor, two capacitors, and one diode. To analyze the performance of the converter, n=3 i.e., the L
3
C
4
network-based converter has been chosen. The PSO-based MPPT algorithm has been applied to track the global peak power under partial shading conditions in MATLAB. Also, a 300 W scaled-down laboratory prototype is developed and tested under steady-state and dynamic conditions.