# A Novel MPPT Based Reptile Search Algorithm for Photovoltaic System under Various Conditions
###### Abstract
To solve the MPPT problem, the author of [19] provided an Ant Colony Optimization (ACO) method with a novel pheromone update; the introduced algorithm showed good robustness, high efficiency, and fast convergence speed in tracking the true MPP of the system. However, practical implementation, the complexity of computation, and high cost are the main constraints to the adaptation of these approaches in real-time scenarios. Reference [20] presented a new MPPT based on Dragonfly Optimization (DOA) algorithm; results indicated that the suggested DOA approach outperforms previous strategies in terms of power efficiency and response time. The main drawback of this algorithm is transient oscillation. In [21], a marine predator algorithm-based MPPT control technique is introduced. This algorithm mimics the predator-prey relationship found in marine species. The experimental findings demonstrate that this approach can accomplish good tracking abilities under various external situations. However, its convergence speed was low, and its performance was affected by the initial conditions. Refs. [22; 23] proposed Fish Swarm Optimization (PSO) and Whale Optimization Algorithm (WOA) algorithms, respectively. Abilities of these algorithms to approach the global maximum under partially shaded situations are significantly impacted, and its implementation is becoming more challenging because the training required is quite extensive. Reference [24] introduced a hybrid shuffled frog-leaping and pattern search algorithm (HSFLA-PS) for MPPT; the hybrid approach was able to identify the global maximum power peak in distinct climatic scenarios with a high convergence rate and efficiency, according to the presented results. Though, the complexity of the proposed HSFLA-PS MPPT controller can make it more difficult to design, and implement in a real-world PV system. In [25], the author suggested an additional filtration and distribution procedure called Random walk in addition to the ordering of solutions to develop a new algorithm called Ordered Flower Pollination Algorithm. The proposed algorithm (Ordered FPA) has outperformed the well-known FPA algorithm [26] under different weather circumstances. Ref. [27] suggests a novel MPPT controller for a hybrid system. It employs IFFO-FLC to tune the controller parameters. Results showed that the proposed algorithm provided better performance in terms of convergence speed and high efficiency compared to the other algorithms. However, it requires multiple parameters tuning, which can be a time-consuming and challenging task. After a critical evaluation of all the published algorithms, it observed that, although many of them have shown effectiveness in global MPPT searches, sometimes they show disappointing performance in some circumstances by getting stuck at local maximum power point. In other words, the published algorithms' best performance is not always guaranteed, and it depends on the complexity of the PV system and operational conditions. From this perspective, developing or applying a new intelligent algorithm with higher potential for MPPT application has always been encouraged to improve MPP search performance. Following a thorough literature research on the newly introduced intelligent optimization algorithms, Reptile Search Optimization (RSA) algorithm has been noted due to its efficacy, strength, adaptive capabilities, and notable design. RSA has a unique method of updating solution positions through four mechanisms. This unique mechanism allows the algorithm to preserve diversity among potential solutions while identifying the optimal one. Moreover, it ensures continuity of exploration, especially in the case of local optima stagnation in the last iterations. This algorithm can efficiently track the global solution and avoids local optima. Taking into account the abovementioned facts, the authors are motivated to apply the RSA algorithm to extract the maximum power of a grid-connected PV system under different weather scenarios.