Maximum power point tracking of solar photovoltaic under partial shading conditions based on improved salp swarm algorithm

Owing to the nonlinear voltammetric characteristics of Photovoltaic(PV) cells, multi-peaks emerge on the voltammetric curve under partial shading conditions (PSCs). This phenomenon complicates the attainment of maximum power through maximum power point tracking (MPPT), thereby jeopardizing the stability and reliability of the PV cells' operation. To address the challenges posed by PSCs and enhance the precision and expeditiousness of MPPT in PV cells. This paper proposes a PV-MPPT method based on the improved salp swarm algorithm (ISSA). Firstly, to bolster the algorithm's exploratory capacity, tent chaotic initialization is employed to refine the initial search scope, thereby swiftly homing in on the proximity of the maximum power point(MPP). Secondly, during the algorithm's iterative phase, the tracking accuracy and convergence velocity are harmonized through an integration of Levy flight and lion swarm algorithm, and counteract the tendency of PV-MPPT to become ensnared at local MPP under PSCs. Ultimately, simulations conducted under different PSCs scenarios, along with comparison results with other methods, demonstrate that the PV system using ISSA achieves the highest MPPT tracking accuracy and the shortest tracking time. The proposed ISSA is adept at affording superior MPPT performance within PV systems.