Designing a Fast-speed Hybrid GMPPT Method for Solar PV System under Partial Shading Conditions

Partial shading conditions (PSC) generate multiple local maxima in the power-voltage characteristics of photovoltaic (PV) systems increasing the risk of traditional maximum power point tracking (MPPT) algorithms converging to local instead of global optima and thus causing energy-harvesting losses. This paper presents a novel Sand Cat Swarm Optimization (SCSO) with Sooty tern optimization (STOA) algorithms for global maximum power point tracking (GMPPT) under complex PSCs. The proposed STSC is different from conventional two-stage hybrid GMPPT algorithms which usually adopt independent global and local tracking methods, the STSC keeps the SCSO original exploration-exploitation decision process and conditionally activates the search operator derived from STOA, without calling STOA alone as a separate optimizer. The proposed approach is validated in MATLAB/Simulink for a boost-converter-based PV system under four different shading patterns. Performance is evaluated in terms of tracking efficiency, settling time, harvested energy, and power ripple. The results validate the efficacy of the proposed STSC algorithm in achieving reliable GMPP convergence, minimizing transient power loss, and maintaining stable operation under severe partial shading conditions. The results show that the proposed STSC reduces the average settling time from about 0.303 s to 0.132 s with a reduction of 56.5%. Further, the average PV-power standard deviation is reduced from 32.03 W to 7.08 W, a reduction of 77.9%. At the same time, the mean tracking efficiency rises from 97.44% to 99.22 %, which is an improvement of 1.71%.