Table 4.

Concise breakdown of different algorithms for MPPT.

Method	Reference	Remark	Validation method
Artificial Neural Network (ANN)	[135]	In order to find the MPP quickly and accurately, a new MPPT technique established on artificial neural networks has been proposed.	Experimental parameters such as Voltage, Current, Radiation Intensity are taken to train ANN Model. The model is best suited in field-programmable gate array.
	[136]	When utilizing an ANN-based MPPT controller, the output voltage is more stable and has fewer oscillations than when using other controllers. The controller is more reliable overall and operates at a higher speed.	The performance is compared with perturb and Observe method and validation is done.
	[137]	In order to train neural networks, an algorithm called error back propagation is utilized. The NN has the benefits of quickly tracking the MPP.	ANN obtained results are validated by using standalone PV Panel connected to boost dc-dc converter.
	[138]	In this paper, design and analysis of a MPPT controller for a solar structure implemented in an ANN. The results reveal that ANN-based MPPT provides good performance under consistent and rapidly changing environmental circumstances.	PV cell is modeled with Cuk converter and output is analysed with ANN results.
	[139]	This research presents a Rprop–NN–based technique for PV-MPPT.	The existing studies cases are used for comparison of results obtained from Rprop-NN model.
	[140]	A low-complexity MPPT technique that is established on the neural network (NN) model of the solar module is presented in this research.	The calculated power output is compared with results obtained from proposed algorithm.
Fuzzy Logic Controller (FLC)	[141]	In this research, a P&O-based fuzzy logic MPPT controller that is tailored for rapidly changing insolation circumstances is presented. The power generated by MPPT controllers that make use of FLC is superior to that generated by conventional methods.	The power generated by MPPT controllers that make use of FLC is superior to that generated by conventional methods.
	[142]	In this article, the author presents the design and analysis of an isolated PV system that makes use of a push-pull converter and an MPPT algorithm that is built on fuzzy logic. The results of the simulation demonstrate that the proposed methodology is capable of tracking the MPPT in an effective manner.	The hardware PV Cell of 250W prototype is used to validate results getting from MPPT algorithm
	[143]	This article suggests employing a fuzzy logic controller (FLC) to run a recommended PV control system at the MPP of the array for each moment.	The results are compared with exiting literature.
	[144]	For a PV system that is linked to the grid, a unique MPPT method that is built on fuzzy logic (FL) has been presented. The proposed method works well with grid-connected PV systems, achieving 99.6 % efficiency.	The experimental results at different irradiation levels are used for validation purpose.
	[145]	This work provides a modified MPPT algorithm for multi-peak PV arrays that operate in partial shadowed situations. The fuzzy logic control is the basis for this proposal. By comparing the suggested method to particle swarm optimization and hardware experiments, its robustness, accuracy, and stability are confirmed.	The experimental results using one-diode PV model is used.
Particle Swarm Optimization (PSO)	[146]	The approach reduces steady-state oscillation (to almost zero) once the MPP is discovered. The proposed approach can track MPP in harsh environmental conditions. Due to the simplicity of the algorithm and the ease with which it may be computed, its implementation on a microcontroller with a lower cost can be accomplished.	The experimental result regarding voltage, current, duty cycle is obtained under similar conditions as that of simulations.
	[147]	This research uses an enhanced PSO algorithm to track PV MPP. To increase tracking speed, PSO particles are given an initial value defined by the I–U and P–U curves.	The improved PSO results are compared with conventional PSO in order to check feasibility of newly proposed algorithm
	[148]	In this research, a novel benchmark test is presented in order to assess the effectiveness of various EA-inspired MPPT algorithms in comparison to a variety of shaded curves.	The experimental PV curve is compared with curved obtained from PSO algorithm for validation.
	[149]	In this paper, an IPSO-based MPPT approach for tracking MPP was presented. The findings showed that the suggested method has a high convergence speed, and the structure of the enhanced MPPT algorithms is very simple.	The effectiveness of novel techniques is compared with previous published results related with incremental conductance.
	[150]	The aim of this work is to provide a velocity of PSO-based Levy flight (VPSO-LF) for global MPPT of PV systems operating with PSCs. Under a variety of PV array configurations, it has been discovered that the results obtained through the use of VPSO-LF are superior to those obtained via the use of standard PSO and hill-climbing algorithm.	The results are validated with experimental results obtained from PV Simulator integrated with boost converter.
	[151]	PSO is employed to identify the optimal sliding mode controller (SMC) gains for P&O algorithm.	The conventional fixed step P&O and Solarex MSX-60 module results are compared with proposed PSO algorithm.
Ant colony optimization (ACO)	[152]	An ACO technique is developed in this work. This technique effectively follows the global peak and enhances the performance of PV arrays as a result.	The conventional fixed step P&O results are compared with proposed ACO algorithm.
	[153]	For maximal power point tracking in this investigation, a brand-new bio-inspired technique known as ACO NPU was utilized.	The results are compared with existing literature related with PSO & ACO and conventional P&O results.
	[154]	The objective of this research is to provide a technique for controlling the speed of a SRM that is powered by a PV system. In order to find the ideal PI parameters, an approach known as ACO is being utilized.	The optimization problem is built for speed controller motor and result are validated with analytical results obtained from optimization problem.
	[155]	This study adapts ant colony optimization to MPPT in photovoltaic (PV) systems. The idea is represented appropriately, and MPPT curves in a few different PV systems are simulated.	PV Cell with four series-four parallel is used in experiment to validate results related with maximum power tracked, convergence time.
Genetic algorithm (GA)	[115]	This research uses GAs-based MPPT to increase PV system convergence, speed, and accuracy. The suggested technique tracks the global MPP effectively, which is important for partial shading.	The results are validated with test model built in facility of LIAS laboratory, Poitiers, France.
	[156]	This research provides a MPPT method that is based on a GA for a PV array that is coupled with a BSU The MPPT strategies based on GA have been compared to the conventional PO algorithm, and they have been found to be competitive with it.	Conventionally P&O is used for comparison of results obtained.
	[157]	GA optimized ANN- MPPT technique is suggested. The primary goal of this design is to do away with the dc–dc converter, and the losses that come along with it. The efficacy of the suggested strategy has been demonstrated through the use of both simulation and experimental findings.	The 60W PV system is used to construct experimental setup and results are used for comparison with algorithm obtained results.