# Design and Analysis of a Hybrid MPPT Method for PV Systems Under Partial Shading Conditions
## 1 Introduction
The ANFIS-based hybrid Crow Pattern Search (CPS)-Incremental Conductivity Algorithm is proposed by [PERSON] et al., pointing to minimized power ripple, high efficiency, higher output power, and a low sampling rate [39]. In the work of [PERSON] et al., an MPPT algorithm based on the Bat Metaheuristic Optimizer has been proposed, where the MPP tracking error is reduced, the MPP tracking speed is increased, the response time to changing irradiance conditions is reduced, the MPP tracking oscillation performance is significantly improved, the pump water flow rate of the proposed system is increased, and the solar water pump system is made more efficient [40]. In the work of [PERSON] et al., a hybrid Gray Wolf Optimization and Perturb & Observe MPPT algorithm was proposed, which has the features of fast and precise MPP tracking with a high tracking rate [41]. [PERSON] and [PERSON] proposed a hybrid MPPT algorithm using the Genetic Algorithm and Ant Bee Colony Optimization, which has been optimized for high MPP accuracy, a fast calculation speed, and few calculation steps [42]. In the work of [PERSON] et al., a hybrid Cuckoo Search (CS) and Gray Wolf Algorithm was designed, with rapid MPP tracking, zero oscillation in a constant state, more effectiveness than GWO, and good dependability [43]. [PERSON] et al. presented a system based on ANFIS using the Zebra Optimization Algorithm and the Artificial Gorilla Troops Optimization Algorithm, which has the features of good performance, quick computing time, and effective hybrid design [44]. In some studies, algorithms using machine learning and artificial intelligence were developed. [PERSON] et al. propose a new Adaptive Fuzzy Logic Controller MPPT method, which provided improved tracking performance, increased performance, and reduced active power ripple [45]. The study by [PERSON] et al. shows that a hybrid Artificial Neural Networks (ANN) and Hill Climbing MPPT algorithm is capable of flexibility in response to changing conditions, minimal processing demands, and no necessity for more equipment [46]. In the study by [PERSON] et al., the ANFIS-Ant Bee Colony Hybrid MPPT algorithm was proposed, providing a low rate of harmonic distortion, safe grid connection, quick reaction, high efficiency, and precise MPP tracking [47]. A hybrid MPPT algorithm based on the Perturb & Observe--Artificial Neural Networks algorithm was introduced by [PERSON] and [PERSON], which provides a high power factor, low harmonic distortion, accurate MPP tracking, low oscillation in steady state, quick MPP tracking, high efficiency, and irradiance condition adaptation [48]. A hybrid MPPT method based on MSFLA and the Fuzzy Logic Controller (FLC) was proposed by [PERSON] et al. ; it has consistent reliable MPP, high effectiveness, high power output, quick MPP tracking, and the integration of battery energy storage [49]. In a work by [PERSON] et al., a hybrid MPPT algorithm based on ANFIS--Particle Swarm Optimization was proposed, providing fast and precise MPP tracking, zeta converter integration, minimal harmonic distortion, quick computation times, and no need for extra sensors [50]. In the study by [PERSON] et al., an MPPT method based on Artificial Neural Networks was proposed, which uses only current and voltage measurement, can track the reference voltage stably, has a low error rate, can respond quickly to changes, and provides stable operating conditions [51]. Innovative methods are also being developed in MPPT studies. The study by [PERSON] et al. shows that a new MPPT method based on the Bat Algorithm and Fuzzy Logic Controller has the features of high efficiency, the integration of the grid and batteries, a quick reaction time, minimal oscillation, fit for the application, and precise and high MPP tracking [52]. [PERSON] et al. presented the Modified Invasive Weed Optimization--Perturb & Observe hybrid MPPT algorithm, aims to provide a minimal voltage ripple, is grid-compatible, and can track MPP well [53].