# Design and Analysis of a Hybrid MPPT Method for PV Systems Under Partial Shading Conditions
## 1 Introduction
[PERSON] and [PERSON] propose the use of the Lock-On Mechanism MPPT Algorithm, which has been optimized for low oscillation, steady and excellent performance, and adaptive duty cycle ratio measurement [23]. The algorithm, a hybrid Perturb & Observe-Look Up Table Algorithm (LuT), has been proposed by [PERSON] et al. and is presented for reacting to quick changes in weather conditions, great efficiency, and accurate and quick MPP tracking [24]. The study by [PERSON] and [PERSON] shows the Variable Step Size MPPT Method based on Perturb & Observe-Incremental Conductance Method (IC), which ensures minimal oscillation, quick MPP tracking, and dynamic step size [25]. In contrast to traditional MPPT methods, [PERSON] and [PERSON] present Perturb & Observe and the Model Predictive Control (MPC) MPPT algorithm, which has the features of good performance, quick response time, control without a comparison structure, and a successful efficiency ratio [26]. In the study by [PERSON] et al., a system based on Perturb & Observe and Incremental Conductance MPPT algorithms for Cube Satellites with low implementation cost, increased MPP tracking accuracy, easy-to-apply MPPT,and adaptability to environmental changes was proposed [27]. A hybrid Fractional Short Circuit Current (FSCC) and Perturb & Observe MPPT algorithm was designed by [PERSON] et al. to enhance quick environmental change adaption, quick MPP monitoring, and high efficiency and to provide a steady state without the need for an irradiance sensor or intelligent weather forecasting [28]. [PERSON] and [PERSON] have demonstrated that a hybrid Grey Wolf Optimization (GWO) and Incremental Conductance MPPT algorithm, which minimizes torque swing, quickly reaches MPPT, enhances excellent efficiency, and quickly reacts [29]. The Shuffled Frog Leaping Algorithm (SFLA)-Incremental Conductance hybrid MPPT method, the algorithm proposed by [PERSON] and [PERSON], has been implemented to provide the system with reliability, low computational time, and low computational complexity [30]. [PERSON] et al. present a novel hybrid MPPT algorithm based on Neural Network, Model Predictive Control, and Kalman Filter, which has great performance in terms of efficiency, oscillations, MPP tracking accuracy, MPP tracking speed, overshoots, and output power [31]. Various methods combining many MPPT algorithms continue to be developed in the literature. According to the article by [PERSON] et al., a hybrid Stochastic Evolutionary Search-Specific Hill Climbing MPPT algorithm has been shown to provide cheap implementation costs and little computing complexity [32]. [PERSON] et al. presented a new hybrid Backstepping-Sliding Mode MPPT algorithm, which has been developed to provide strong performance, low overshoot, and reduced oscillation [33]. [PERSON] and [PERSON] highlight the features of a Hybrid Two-Stage Global Maximum Power Point Tracking Algorithm in terms of energy saving, fast response, and adaptability to changing conditions [34]. In the work of [PERSON], the Improvised Binary Sequence-Perturb & Observe hybrid MPPT algorithm has been proposed, which has quick reaction time, adaptability, and precise MPP tracking [35]. [PERSON] et al. present a new hybrid method referred to as Whale Optimization (WO)-Pattern Search Algorithm based on ANFIS, which had good results in terms of excellent power output, effective MPP tracking, and high efficiency outcomes [36]. Some methods include evolutionary algorithms and nature-inspired optimization algorithms. [PERSON] et al. introduce the Genetic Algorithm (GA)-Based Fractional Open Circuit Voltage (FOCV) MPPT algorithm, which enhances rapid MPP tracking, has excellent efficiency, and decreases voltage swing [37]. The Hybrid Differential Evolution (DE)-Feed Forward Neural Network MPPT Algorithm, as introduced by [PERSON] et al., has been made to provide a high effectiveness level, a quick tracking capability, and high MPP precision [38].