*4.4. Optimization Results under Sudden Variations for Irradiance and Temperature Conditions*

To evaluate the output power characteristics under harsh environmental conditions considering the influence of temperature on MPP, this paper sets the irradiance intensity of the array as 800 W/m2, 800 W/m2, 600 W/m2, 400 W/m2, and 400 W/m2 at 0–0.8 s, while the ambient temperature is maintained at 25 ◦C. Later, from 0.8 to 2 s, the array experience suddenly changes light intensity to 800 W/m2, 600 W/m2, 400 W/m2, 200 W/m2, and 200 W/m2, while the environmental temperature is increased to 30 ◦C. The resulting P-V characteristics outputs are depicted in Figure 16, where the GMPP for the two stages is 3798 W and 2237 W. Figure 17 shows the dynamic shading simulations for three algorithms under harsh environmental conditions.

**Figure 16.** P-V characteristics of PV array outputs under sudden variations for irradiance and temperature conditions.

**Figure 17.** Power outputs of three algorithms under sudden variations for irradiance and temperature conditions. (**a**) PSO-BOA algorithm; (**b**) PSO algorithm; and (**c**) BOA.

According to the findings illustrated in Figure 17, the PSO-BOA algorithm successfully tracks the theoretical GMPP with high precision. In contrast, the PSO algorithm is prone to falling into local optima after abrupt changes in irradiance and temperature, leading to significant deviations from the theoretical GMPP. Although the BOA can track the theoretical GMPP, the error is still greater than that of the PSO-BOA algorithm. As for convergence speed, the PSO-BOA algorithm shows the fastest convergence speed and the least oscillation. Conversely, the PSO algorithm converges extremely slowly after a sudden change in conditions, oscillating around GMPP. The BOA converges slowly with a large power oscillation amplitude. Notably, in harsh environmental conditions, the PSO-BOA algorithm outperforms different algorithms in the context of both convergence speed and power oscillations.
