Abstract
In the last decade, artificial intelligence (AI) techniques have been extensively used for maximum power point tracking (MPPT) in the solar power system. This is because conventional MPPT techniques are incapable of tracking the global maximum power point (GMPP) under partial shading condition (PSC). The output curve of the power versus voltage for a solar panel has only one GMPP and multiple local maximum power points (MPPs). The integration of AI in MPPT is crucial to guarantee the tracking of GMPP while increasing the overall efficiency and performance of MPPT. The selection of AI-based MPPT techniques is complicated because each technique has its own merits and demerits. In general, all of the AI-based MPPT techniques exhibit fast convergence speed, less steady-state oscillation and high efficiency, compared with the conventional MPPT techniques. However, the AI-based MPPT techniques are computationally intensive and costly to realize. Overall, the hybrid MPPT is favorable in terms of the balance between performance and complexity, and it combines the advantages of conventional and AI-based MPPT techniques. In this paper, a detailed comparison of classification and performance between 6 major AI-based MPPT techniques have been made based on the review and MATLAB/Simulink simulation results. The merits, open issues and technical implementations of AI-based MPPT techniques are evaluated. We intend to provide new insights into the choice of optimal AI-based MPPT techniques.
THE solar power system is widely used nowadays due to its cost-effectiveness and high efficiency [

Fig. 1 Curve of power versus voltage for a solar panel under PSC.
Apart from electronically implemented MPPTs, there are other techniques to improve solar energy efficiency such as integrated soft-computing weather forecast and adjustment of the tilting angle of solar panel to track the sun direction [
1) Lack of adaptive, robust and self-learning capabilities.
2) High steady-state error, power oscillation at MPP and slow transient response.
3) Inability to find GMPP, trapping at local MPP and incorrect perturbation direction under PSC or sudden irradiance change due to MPPT failure [
In general, the existing AI-based MPPT techniques utilize the sensory information including solar irradiance Ee, input voltage of solar power system and input current measurements to predict and estimate the GMPP throughout the non-linear P-V curve. The integration of AI in MPPT accelerates the convergence speed and transient response because of their complex, robust, self-learning and digitalised system. MPPT techniques are categorized into two major groups: conventional HC MPPT and AI-based MPPT [
The contributions of this paper are as follows: ① the applicability and utilizations of AI in MPPT for solar power system are reviewed; ② current development and research areas of AI in MPPT are overviewed; ③ comparative analysis and performance evaluation of each AI algorithm in MPPT techniques are provided. In this paper, popular AI-based MPPT techniques are compared and evaluated. This paper provides a comprehensive insight into the latest development and advancement of AI, which is applied in MPPT for the solar power system. In general, all conventional MPPT techniques exhibit the common disadvantages, including power fluctuation, inability to operate normally under PSC and rapid irradiance changes, trapping at one of the local MPPs and oscillation around MPP [

Fig. 2 Block diagram of typical MPPT.
The design of a conventional control (CC) system involves mathematical modelling, which consists of all the dynamics of the plant and is known as the mathematicians’ approach since the designer must model the plant mathematically before it is to be controlled. In contrast, to develop an IC system, the system behavior is necessary for the inputs and the IC system is responsible for autonomous and abstract modelling [
FLC is a control system based on fuzzy logic which converts analogue inputs into continuous digital values of 0 and 1 [
(1) |
(2) |
where Err is the number of erro; P is the ratio of change of power; V is the change of voltage; is the rate of change of error; and PPV and VPV are the output active power and voltage of PV panels, respectively. A fuzzy controller can be implemented on any low to medium powerful microcontroller including Arduino Mega and Microchip to manipulate the output duty cycle D of the DC-DC converter depending on T and , which searches the MPP of the solar power system [
1) If and , , then D is decreased by .
2) If and , , then D is increased by .
3) If and , , then D is decreased by +D.
4) If and , , then D is increased by -D.
5) If , then MPP is achieved.
For each step, taking and considering the sign of P and V, the following conditions are concluded.
1) If , then .
2) If , then
3) If , then .
Another type of FLC is reduced-rule FLC (RR-FLC), which improves the simplicity of FLC by reducing the computational load [

Fig. 3 Block diagram of general FLC.
ANN or connectionist system is inspired by the biological neural networks from animal brains. It is utilized to train and test for the non-linearity relationship between I-V and P-V. From input current, input voltage, irradiance, temperature to metrological data, ANN fetches these inputs and continuously learns to fit the behavior of the solar power system for the maximum power [
From the collection of the simulation or hardware setup, the dataset is acquired by inputting solar irradiances, temperatures, solar power system voltage or current to ANN in finding the corresponding Pmax or Vmax output as shown in

Fig. 4 Structure of an ANN-based MPPT.
The advantages of ANN include exceptional accuracy in modelling non-linearity and resolving problems without any prior knowledge or any model [
GA is a general AI-based optimization method applied to different optimization problems. It is widely used in MPPT to compute the voltage reference of PV panel by modifying a population of individual solutions. In general, GA has relatively small oscillations, rapid convergence speed and fast dynamics by using voltage-step selection GA algorithm [
However, despite its performance, GA is not recommended to optimize very large-scale, highly complex and excessive problems due to its simplified algorithm. In the optimization process of MPPT, GA is initialized by starting the initial parent population as an array:
(3) |
where n is the population size; and represents the initial voltage values when the algorithm starts the optimization. The objective function is the generated output power of the solar power system. The evaluation of fitness values for each position is executed by the objective function. Then, they are used to evolve the population and improve the population fitness through the generations. Compared with conventional GA, the algorithm must be reinitialized specifically for MPPT application because of sudden changes in load, solar irradiance or PSC. Therefore, the following conditions reinitialize the GA-based MPPT technique once they have been satisfied in (4) and (5).
(4) |
(5) |
where k is the current measurement; and is the next iteration of the measurement.
GA is invented based on the evolution of chromosomes.

Fig. 5 Flowchart of a typical GA method in MPPT.
The most common SI-based MPPT is PSO algorithm. It is a heuristic method for resolving MPPT optimization problem. The position of a particle represents the possible solution and the duty ration represents the solution space [
GWO is one of the modern heuristic optimization techniques, which is inspired by the lifestyle of the grey wolves. The leader is defined as , subleader is called as , the lower rank is called as and the lowest rank is called as . A GWO-based MPPT is dependent on the hunting techniques of the grey wolves by obeying the order of , and in the priority order. The algorithm will converge to the prey, which is GMPP in this paper.
Another type of SI is FA which is based on the behavior and flashing of fireflies. The ideology is that the attractiveness is proportional to the brightness of a firefly. In this context, fireflies can converge into an optimal solution by the attractiveness. Similarly, FA can be utilized as a type of SI in MPPT to find the optimal MPP [
CS is an emerging SI algorithm based on the reproduction strategy of some species of Cuckoo birds that lay their eggs in the nests of other birds. CS optimization algorithm is inspired by this parasitic reproduction approach. The basis of CS is to find the right host nest, which is similar to the searching for food. It is a random process and can be modelled by using a mathematical optimization approach. The Lévy flight model is the most common method to model food seeking trajectory of an animal. Hence, in CS-based MPPT, the Lévy flight model is used to characterize the nest seeking approach of a reproduction process of Cuckoo bird. Mathematically, the Lévy flight model represents a random walk where the step sizes are defined by using Lévy distribution. It has fast MPPT speed and high tracking accuracy regardless of any weather condition. It is a simpler MPPT technique with only three particles and only one parameter to be tuned [
GSA is based on the concept of Newtonian gravity and laws of motion, which states that particles tend to accelerate towards each other because they attract each other [
1) The population size is assigned with the upper and lower limits of the duty cycle for the DC-DC converter, which usually ranges from 10% to 90%.
2) Solar agents are uniformly positioned between the search space intervals to achieve the optimum convergence speed.
3) For each agent position, PV output power is calculated. The power of MPPT is assumed as the mass of the agents.
4) The force G acting between the agents and the net force acting on each agent is computed.
5) The acceleration of each agent is calculated.
Apart from conventional GSA, an improved GSA has dynamic weight in the change factor of the gravity constant. The factors of memory and population information are added into the updated formula of particle velocity [
Hybrid MPPT is a general term to describe the integration of two or more MPPT either from AI or conventional techniques. One of the most popular hybrid MPPT is the integration of ANN with conventional P&O algorithm, which is known as “neural network P&O controller” [
Another popular hybrid MPPT is an adaptive neuro-fuzzy inference system (ANFIS) which integrates ANN and FLC together. It has the advantages of both ANN and FLC. ANN is trained to estimate the optimal MPP and used to drive an FLC-based MPPT. ANFIS and fuzzy logic are optimal, flexible and adaptable to any new configuration for smart power management and solar power system [
ANN is deployable based on hybrid PSO and GSA, alongside with FLC. For instance, PSO-GSA generates a random initial population first and send them to ANN for data training [
Another hybrid MPPT is the integration of two powerful machine learning (ML) techniques, coarse-Gaussian support vector machine (CGSVM) and ANN, which is known as ANN-CGSVM technique. CGSVM is a type of non-linear SVM learning technique categorized as a data mining technique [
Bayesian ML is a method specialized in unsupervised classification, curve detection, and image segmentation. It is applicable in MPPT to achieve GMPP [

Fig. 6 General structure of RL-based MPPT.
DE-based MPPT is an optimization method to use target vectors as the population in each iteration. The more particles are used, the larger the search space, the slower the convergence speed. DE is meta-heuristics since it searches very large spaces of possible solutions and does not guarantee an optimal solution [
The AI-based MPPT techniques are compared with regards to the following parameters: ① tracking speed for MPP; ② tracking accuracy; ③ steady-state oscillation; ④ complexity of the algorithm which affects the computation time; ⑤ overall cost. The general classification of the popular AI-based MPPT techniques is categorized as FLC, ANN, SI, hybrid, GA, ML and other new emerging algorithms, according to their descending popularity. There are other emerging algorithms which may not be included in this paper due to the limited space and constrained area. Approximate citation popularity of AI-based MPPT versus year is shown in

Fig. 7 Approximate citation popularity of AI-based MPPT versus year.
FLC is invented in the year 1965, and it is popular in that decade. After that, ANN, GA, SI, hybrid and ML are invented at a respective timeline, and all of them are still applicable in AI-based MPPT over the decades. In the result section, there are three major comparative tables and one categorization figure. The tables include the merits and open issues for each AI-based MPPT, the comparison of parameters between all AI-based MPPT, and the available AI-based MPPT in recent years.
The categorization figure presents a clear representation of available AI-based MPPT in each category and classifications. Generally, the evaluation of AI-based MPPT techniques is executed in terms of several parameters and features which include the number of control variables (input sensory parameters), the utilized platform (software: MATLAB/Simulink; hardware: arm cortex microcontroller, Arduino, Raspberry Pi, and DSP board-dSpace), the solar panel parameters, the switching frequency of DC-DC converter, the type of DC-DC converters (buck, boost, buck-boost, Ćuk or SEPIC), tracking/convergence speed or transient time, oscillation accuracy and MPPT efficiency. In recent years, bio-inspired algorithms and ML are very popular due to their sophistication in terms of accuracy, speed and performance. More parameters are considered as input parameters instead of only current and voltage inputs. It includes the humidity, shading, cloud and metrological data. All algorithms aim to have fast convergence or tracking speed, low steady-state oscillation, simple cost-effective implementation, fast computational capability and high efficiency with the minimum power loss.
The recent AI-based MPPT techniques are typically more advanced and efficient but require a huge amount of data, highly complex and costly. The balance between the performance and the cost or complexity is critical for the application of MPPT in a specific area.

Fig. 8 Classification and categorization for popular AI-based MPPT techniques in recent years.
The family of SI is the largest in AI-based MPPT, mainly because its algorithms are inspired by biological swarm intelligence (SI) due to fast performance and high accuracy. The hybrid and ML have a great variety of sub-categories. The hybrid MPPT is relatively versatile as the AI-based MPPT is easily integrated with each other. ML is another popular technique. It has various approaches and techniques to learn from the experience or dataset in order to output the maximum power. FLC, ANN, and GA do not have any sub-categories. The emerging algorithms have the latest advancing techniques in MPPT, which is continuously improving and populating.
As illustrated in Figs.

Fig. 9 Performance evaluation of each AI-based MPPT in term of each category.

Fig. 10 Performance of AI-MPPT techniques.
To validate and compare the performance of AI-based MPPT techniques, an extensive simulation based on MATLAB/Simulink R2020a is conducted. The simulation setup is to study, evaluate and investigate the dynamic behavior of the AI-based MPPT under PSC. The optimal MPP is benchmarked against the searching process of each AI-based MPPT. As illustrated in

Fig. 11 MATLAB/Simulink simulation for comparison of AI-based MPPT.
The PV panel SunPower module (SPR-305E-WHT-D) inputs with varying solar irradiance Ee and T. It is simulated under PSC to emulate the practical environment. A 5 kHz DC-DC boost converter is designed and its insulated-gate bipolar transistor (IGBT) switching devices are controlled by the AI-based MPPT controller to output the most optimized voltage and current for MPP.
A DC-AC converter (inverter) based on synchronverter topology is deployed to convert optimized solar MPPT of DC output to AC output in supplying AC for the three-phase balanced resistive load RL. The MPPT controller is the variable that has been changed from FLC, ANN, SI, hybrid, GA to ML to compare their tracking ability for MPP under PSC, which is validated as shown in
As shown in

Fig. 12 I-V and P-V curves of solar panel under STC. (a) With constant temperature at 25°C and varying irradiance. (b) With constant irradiance at 1000 W/
PSC analysis is conducted by emulating PSC for the inputs of the solar panel. To simulate PSC, the current is adjusted to allow multiple peaks in the P-V curves. Besides, MPPT failure caused by dynamic irradiance changes is investigated. The current source of solar cells is adjusted automatically using the look-up table. The PSC effects on the solar module are accounted for, which enables partial shading on certain cells. The phenomenon is common for the practical environment where partial shading occurrs when there are dirt, leaf, cloud, tree and other obstacles that block the sunlight.

Fig. 13 Local MPP and GMPPT performance for AI-based MPPT under PSC.
It is self-explanatory that SI and hybrid MPPT are performing optimally by tracking GMPP, which is the highest possible output of solar power system. This is because of the algorithm optimization, population searching ability and combination of different algorithms. ML and ANN are also performing well while GA tracks the local MPP with some steady-state oscillations. However, the performance of FLC is relatively unsatisfactory owing to its slow transient response and inability to track GMPP. It is trapped at the local MPP and results in lower power conversion efficiency.
The tracking ability of AI-based MPPT controller for MPP with constant irradiance is simulated.

Fig. 14 MPPT ability of different algorithms. (a) FLC. (b) ANN. (c) SI. (d) Hybrid. (e) GA. (f) ML.
The extensive comparison and investigation on various AI-based MPPT have clearly shown that each algorithm has its own merits and demerits. The choice of algorithm is solely depending on the choice of the designer. Typically, the input parameters of MPPT are and , which are acquired through voltage and current sensors. Pin,pv is then computed by using . However, solar irradiance, temperature, metrological data of humidity and shading are required to train AI. Irradiance and temperature are used by some MPPT techniques to define MPP [
Another important aspect of AI-based MPPT is to search for GMPP under PSC or varying irradiance and temperature. The failure of MPPT could be caused by the inability of the algorithm to search for GMPP. It will be stuck at the local MPP and thus cannot produce the optimal power output. In general, SI approaches are based on the searching for the optimal solution in the search space. The acting participants in the optimization can be a reminiscence of an ant for ACO [
Apart from MPPT, an inverter is the medium interface between the solar power system and the power grid. Hence, an efficient inverter is important for converting DC to AC and acting as anti-islanding protection [
This section aims to recommend AI-based MPPT to be applied in the solar power system and their future research areas. The traditional MPPT techniques are phasing out since the latest AI-based MPPT techniques have better performance and stability. The development of the AI-based MPPT is dependent on the latest advancement in ML and DL. The main challenges include the ability to search for GMPP and the complexity of the algorithm.
For the conventional MPPT such as open current, open voltage, P&O and IC, they are recommended for simple and low-cost application which does not require high performance. In order to resolve, optimize and predict the non-linearity of the PV cell without staying at local MPP under PSC, the AI-based MPPT techniques are recommended for optimal performance, accuracy and convergence speed. For the type of EA, GA is faster than classical methods, but it tends to stick at local minima. The improved GA requires higher computation resources and different parameters require tuning. In contrast, DE is fast and accurate without any employment of probability distribution. However, its population can be stagnant in some sub-optimal values. PSO has the highest performance by considering different best positions to update the population, which is also simple to be implemented in hardware and independent from the installed system [
Theoretically, the occurrence of voltage fluctuation is defined as a continuous change in the voltage when devices or appliances that require a higher load are extensively used. The parameters of an AI-based MPPT controller are the design complexity, ability to track GMPP, cost-effectiveness, PV panel dependency, prior training requirement, dataset requirement, convergence speed, analogue or digital architecture, required sensory information, periodic tuning, stability, SSE, efficiency, and TET. The balance between the complexity and performance of the algorithm should be considered when designing AI-based MPPT. In the general context, the higher the performance of AI-based MPPT, the more complex the designed algorithm. Therefore, TET and computation time are affected.
The most critical aspects of the AI-based MPPT are the ability to track GMPP. Besides, real-time solar panel experiments lack concrete evidence. A general design flow for the standardized AI-based MPPT is in lack of studies. In a grid-connected solar power system, MPPT is also a crucial element to be integrated with synchronverter to act as a DC-DC-AC converter, which is to provide the maximum power extraction and virtual inertia concurrently [
We provide a detailed comparison of popular AI-based MPPT techniques for the solar power system. They are designed to track GMPP instead of local MPP in alleviating the effects of PSC. Each technique is compared in terms of algorithm structure, cost, complexity, platform, input parameters, tracking speed, oscillation accuracy, efficiency and their applications. The AI-based MPPT techniques are generally classified into FLC, ANN, SI, hybrid, GA, ML and other emerging techniques. Generally, all of them exhibit good convergence speed, small oscillation at steady state and accurate tracking, even under PSC or rapid change of irradiance. However, most of the techniques are costly and complex to build and require more datasets compared with conventional MPPT techniques. Compared with FLC, ANN, and GA, other emerging and newer algorithms including hybrid, SI, ML and DL are also recommended due to their newer architectures with adaptive learning capabilities, fully digitalized system and fewer open issues. In contrast, ANN and FLC are not much preferred due to their ageing architecture, periodic tuning requirement and inability in tracking MPP under PSC. This review is expected to provide a detailed insight into the latest advancement of AI-based MPPT techniques for the application in the solar power system.
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