# A Novel MPPT Based Reptile Search Algorithm for Photovoltaic System under Various Conditions
###### Abstract  
Solar systems connected to the grid are crucial in addressing the global energy crisis and meeting rising energy demand. The efficiency of these systems is totally impacted by varying weather conditions such as changes in irradiance and temperature throughout the day. Additionally, partial shading (PS) adds to the complexity of the nonlinear characteristics of photovoltaic (PV) systems, leading to significant power loss. To address this issue, maximum power point tracking (MPPT) algorithms have become an essential component in PV systems to ensure optimal power extraction. This paper introduces a new MPPT control technique based on a novel reptile search optimization algorithm (RSA). The effectiveness of the proposed RSA method is evaluated under different weather conditions with varying irradiance and partial shading. The results of the RSA algorithm are compared to other existing bio-inspired algorithms and show superior performance with an average efficiency of 99.91%, faster dynamic response of 50 ms, and less than 20 watts of oscillation. The RSA-MPPT based technique provides higher efficiency, faster settling time, and minimal oscillation around the maximum power point (MPP), making it a reliable solution for effective solar power harvesting. are constantly changing throughout the day [3; 4; 5]. This challenge is further compounded in the case of partial shading, which occurs when solar radiation is unevenly distributed across the PV system's array due to obstacles such as clouds, trees, and buildings in the surrounding area [6]. To address these challenges and issues, PV systems are equipped with a special controller, known as MPPT controller. The role of the MPPT controller in the PV system is crucial to optimizing the generated electricity and increasing the overall efficiency. The controller, which is guided by a sophisticated control algorithm, regulates the DC-DC converter duty cycle to make sure the global maximum power point (GMPP) is consistently tracked under varying operational conditions [7; 8]. Several traditional MPPT tracking methods, including Perturb and Observe (P&O) [9], Incremental Conductance (INC) [10], and Hill Climbing (HC) [11], have been introduced over the years. These algorithms are simple and easy to implement, but they can suffer from steady-state oscillation, slow convergence, and potential for maximum power tracking failure when subjected to rapidly changing irradiance or partial shading conditions. Many works such as [12; 13; 14; 15] have been carried out to overcome these drawbacks by utilizing a variable step size rather than a fixed step size. A certain improvement in power efficiency and tracking speed has been noticed but the oscillation problem that leads to power losses was not well avoided and the complexity of the newly introduced algorithm had increased compared to the conventional methods. To address these shortcomings of conventional methods, various bio-inspired meta-heuristic algorithms have been proposed in the literature. In [16] new Particle Swarm Optimization (PSO) algorithm based on Lagrange Interpolation is proposed. This strategy solved the difficulties of the traditional method by employing a numerical computation to initialize the particles around the Global point. Although the proposed algorithm showed good accuracy in reaching MPP and superior stability around this point in steady radiation scenarios, its convergence rate is low in varying radiation conditions, and it is hard to implement in practical mode. In [17; 18], Grey Wolf Optimization (GWO) and Artificial Bee Colony (ABC) algorithms were proposed; both algorithms showed a good performance in MPP tracking but with drawbacks of high complexity, computational time, and cost. Further drawbacks of these algorithms include difficulties with parameter initialization and calculation in a large population.