Despite of being a cleaner energy resource, the solar photovoltaic (SPV) system faces the dynamic and unpredictable changes in environmental conditions; hence, conventional and extant maximum power point tracking (MPPT) methods can get stuck at local minima. However, on account of less switching strain on the DC–DC converter, the drift-free P&O MPPT method can be supervised with effective bio-inspired metaheuristic algorithms to maximize its robustness and efficiency to generate photovoltaic power. Therefore, in this paper, the efficiency of the drift-free P&O MPPT method is significantly enhanced using a grey wolf skill embedded levy flight optimization (LI-GWO) method as a new approach. Firstly, a single-ended primary inductor converter (SEPIC)-based grid-connected SPV system is modeled to assess the MPPT performance. Further, using the LI-GWO enhanced drift-free P&O algorithm, the duty cycle of the SEPIC is regulated by updating the position of the grey wolfs based on the Brownian motion of the levy flights. Moreover, the exploration, exploitation and convergence analysis are carried out to examine the effectiveness of the proposed LI-GWO + drift-free P&O algorithm. In this manner, the proposed algorithm attains the global maxima quickly and, thereafter, the global MPP (GMPP) is tracked by the drift-free P&O itself with the less switching strain. The performance of the proposed MPPT approach is compared with the other conventional and hybrid metaheuristic-based MPPTs to show effectiveness under the newly formulated extreme weather condition model.

尽管太阳能光伏（SPV）系统是一种更清洁的能源，但它面临着环境条件的动态和不可预测的变化；因此，传统和现有的最大功率点跟踪（MPPT）方法可能会陷入局部最小值。然而，由于 DC-DC 转换器的开关压力较小，无漂移 P&O MPPT 方法可以通过有效的仿生启发式算法进行监督，以最大限度地提高其稳健性和发电效率。因此，本文采用灰狼技能嵌入式征飞行优化（LI-GWO）方法作为一种新方法，显着提高了无漂移 P&O MPPT 方法的效率。首先，对基于单端初级电感转换器 (SEPIC) 的并网 SPV 系统进行建模，以评估 MPPT 性能。此外，使用LI-GWO增强型无漂移P&O算法，通过基于征召航班的布朗运动更新灰狼的位置来调节SEPIC的占空比。此外，还进行了探索、开发和收敛分析，以检验所提出的 LI-GWO + 无漂移 P&O 算法的有效性。通过这种方式，所提出的算法快速获得全局最大值，此后，全局 MPP (GMPP) 由无漂移 P&O 本身以较小的开关应变进行跟踪。将所提出的 MPPT 方法的性能与其他传统和混合元启发式 MPPT 进行比较，以显示在新制定的极端天气条件模型下的有效性。