A Comprehensive Decade-Long Review of Advanced MPPT Algorithms for Enhanced Photovoltaic Efficiency

Abstract

Photovoltaic energy has become a key pillar in the transition to sustainable energy systems, driven by the need for efficient energy conversion and the reduction of dependency on fossil fuels. Maximum Power Point Tracking (MPPT) is central to optimizing the performance of photovoltaic systems by ensuring the maximum extraction of solar energy, even under fluctuating environmental conditions. This review provides a comprehensive analysis of MPPT algorithms developed and refined over the past decade (2015–2025), highlighting major breakthroughs in algorithmic approaches, from conventional methods such as Perturb and Observe (P&O) and Incremental Conductance (IncCond) to more advanced techniques incorporating artificial intelligence, fuzzy logic, and hybrid systems. The paper evaluates the evolution of MPPT techniques, focusing on their effectiveness in real-world applications, particularly in optimizing photovoltaic output under diverse operating conditions such as partial shading, temperature variations, and rapid irradiance changes. Furthermore, it discusses the ongoing challenges in the field and the promising directions for future research, aiming to further enhance the reliability and efficiency of solar power systems worldwide.

1. Introduction

In recent years, photovoltaic (PV) energy has emerged as one of the most promising renewable energy sources, driven by the global push towards decarburization and the increasing need for sustainable, decentralized electricity generation [1]. Worldwide, solar energy deployment has grown rapidly due to declining costs, technological advancements, and supportive policies [2]. In Africa, where solar irradiance is among the highest globally, PV systems hold significant potential to address energy access challenges, especially in remote and underserved areas [3]. However, despite this potential, the performance and reliability of PV installations can be hindered by environmental fluctuations such as irradiance variation, temperature changes, and partial shading [4].

To address these issues, Maximum Power Point Tracking (MPPT) algorithms have become essential in optimizing the output of PV systems. MPPT ensures that the photovoltaic modules operate at their optimal power point, thereby significantly improving energy efficiency and overall system performance. Various MPPT techniques have been proposed over the years, ranging from classical control methods to intelligent and adaptive algorithms [5,6,7].

This review aims to provide a comprehensive synthesis of MPPT techniques developed and refined over the past decade, from 2015 to 2025. The paper categorizes and analyzes the evolution of these algorithms, evaluates their effectiveness under different operating conditions, and highlights emerging trends and research directions. The goal is to offer valuable insights for researchers, engineers, and policymakers working to enhance the efficiency and reliability of PV systems worldwide.

2. Foundations of MPPT Techniques

2.1. Principle of Maximum Power Point Tracking

Photovoltaic modules exhibit a nonlinear current–voltage (I-V) and a power–voltage (P-V) characteristic, which are presented in Figure 1a,b. For any given irradiance and temperature condition, there exists a unique operating point, known as the Maximum Power Point, at which the power output of the module is maximized [6,7,8]. The position of this point varies continuously with environmental changes, as shown in Figure 1. MPPT refers to the process of dynamically adjusting the operating voltage or current of the PV array so as to continuously extract the maximum possible power, thereby improving the system’s overall efficiency.

The MPPT controller, typically embedded in a DC-DC converter, monitors electrical signals from the PV module and adjusts the duty cycle to follow the MPP in real time [9]. Without MPPT, significant portions of available solar energy would be lost, especially under rapidly changing weather conditions [9,10]. Several environmental and electrical parameters influence the location of the MPP and thus the performance of the MPPT algorithm:

• Irradiance (G): Solar irradiance directly impacts the current output of PV modules. An increase in irradiance increases power generation, but also shifts the MPP [11].
• Temperature (T): Higher temperatures generally reduce the open-circuit voltage of a PV cell, thus lowering the maximum power output [12].
• Voltage (V): The operating voltage of the PV module determines the power point location. Tracking the voltage corresponding to the MPP is central to MPPT algorithms [13,14].
• Current (I): The output current changes with both load and environmental conditions and is monitored to assess power variations [15].

Figure 1 shows two important curves that describe how a photovoltaic panel works. In part (a), the I–V curve shows how the current changes with voltage. At first, the current stays almost constant, while the voltage increases; then, it suddenly drops near the open-circuit voltage. The point where the product of voltage and current is highest is called the Maximum Power Point; marked by V_MPP and I_MPP in part (b), the P–V curve shows how the power output increases with voltage until it reaches the MPP, then starts to decrease. This point represents the best operating condition of the panel. MPPT algorithms are used to find and follow this point in real time; so, the system can always produce the maximum power, even when sunlight or temperature changes.

The performance of a photovoltaic panel is simulated to illustrate the effects of varying irradiance and temperature. It can be seen in Figure 1c,d that as irradiance increases, the power output and current of the system also increase. On the other hand, higher temperatures lead to a noticeable decrease in output voltage and overall power, as shown in Figure 1e,f. These trends confirm the strong dependence of photovoltaic performance on environmental conditions, particularly, solar irradiance and temperature.

2.2. General Classification of MPPT Techniques

MPPT techniques are generally divided into four principal categories according to their tracking strategies: traditional methods, intelligent algorithms, optimization-based approaches, and hybrid techniques that combine elements from different categories [15]. Each group follows a unique mechanism to identify and maintain operation at the maximum power point of photovoltaic systems [14,16]. The effectiveness of these methods depends greatly on their ability to adapt to varying environmental factors such as fluctuating sunlight intensity and temperature changes, which affect the power output [17,18].

Table 1. Summary of MPPT technique categories and their acronyms.

Category	Technique	Abbreviation
Traditional MPPT Control Methods	Perturb and Observe Incremental Conductance Constant Voltage Open Circuit Voltage Short Circuit Current Hill Climbing Load Current Fractional Open Circuit Voltage Fractional Short Circuit Current Ripple Correlation Control Modified Perturb and Observe Modified Incremental Conductance	P&O InC CV OCV SCC HC LC FOCV FSCC RCC Mod P&O Mod InC
Intelligent MPPT Control Methods	Fuzzy Logic Control Artificial Neural Network Genetic Algorithm Support Vector Machine Reinforcement Learning Decision Tree-Based Control	FLC ANN GA SVM RL DT
Optimization MPPT Control Methods	Particle Swarm Optimization Ant Colony Optimization Cuckoo Search Optimization Differential Evolution Harmony Search Algorithm Firefly Algorithm Simulated Annealing Grey Wolf Optimization	PSO ACO CSO DE HAS FA SA GWO
Hybrid MPPT Control Methods	Adaptative Neuro-Fuzzy Inference System Fuzzy Logic—Perturb and Observe Artificial Neural Network with Incremental Conductance GA combined with FLC ANFIS with Particle Swarm Optimization Hybrid Swarm Intelligence Techniques	ANFIS FLC-P&O ANN-InC GA-FLC ANFIS-PSO HIS

below offers a clear summary of the different classifications of MPPT methods.

To ensure a structured and insightful exploration of maximum power point tracking strategies in photovoltaic systems, the remainder of this article is organized into several focused sections. The next one offers a comprehensive presentation of the principal MPPT technique categories: conventional methods, intelligent control algorithms, optimization based approaches, and hybrid techniques that integrate features from two or more categories to enhance adaptability and accuracy.

Following this technical overview, the manuscript delves into a review of scientific research conducted over the past decade, highlighting how various MPPT methods have been implemented and evaluated across different photovoltaic system architectures and under diverse environmental conditions. This critical synthesis draws on experimental studies, simulation results, and real-world deployment data to assess the evolution, performance trends, and research gaps in the field.

Building on these insights, the article then presents a comparative analysis of the reviewed MPPT techniques, examining their operational principles, responsiveness to environmental variations, tracking efficiency, implementation complexity, and computational cost. This comparative study aims to provide a clear understanding of the advantages and limitations of each method, thus guiding researchers and practitioners toward the most appropriate solutions for different application contexts.

3. Classical MPPT Control Methods

3.1. Different Traditional MPPT Techniques

3.1.1. Perturb and Observe Algorithm

The Perturb and Observe (P&O) algorithm is one of the earliest and most widely adopted methods for maximum power point tracking in photovoltaic systems, particularly because of its simplicity, ease of implementation, and low computational overhead [19]. The core idea of the algorithm is to apply a small perturbation to the PV array voltage (or current) and observe the resulting change in power output [16]. Based on the direction of this change, the system decides whether to continue in the same direction or reverse it to reach the MPP [16,17,18,19].

Mathematically, let P(k) and V(k) denote the power and voltage of the PV system at the k-th sampling instant [20]. The algorithm evaluates the change in power ΔP and the change in voltage ΔV between two successive samples:

ΔP = P(k) − P(k − 1)

(1)

ΔV = V(k) − V(k − 1)

(2)

The control logic of the P&O algorithm is based on the following decision rule [21]:

• If ΔP > 0 and ΔV > 0: increase voltage (continue in the same direction).
• If ΔP > 0 and ΔV < 0: decrease voltage.
• If ΔP < 0 and ΔV > 0: decrease voltage (reverse direction).
• If ΔP < 0 and ΔV < 0: increase voltage.

The P&O MPPT technique is traditionally implemented using either fixed or adaptive step sizes, allowing for various enhancements in tracking performance [22]. A schematic representation of the algorithm’s operational flow is illustrated in Figure 2.

3.1.2. Modified Perturb and Observe

The Modified Perturb and Observe (P&O) method is an enhanced version of the conventional P&O technique, developed to address its inherent limitations, such as oscillations around the maximum power point and poor performance under rapidly changing environmental conditions. In the standard P&O algorithm, the system periodically perturbs the operating voltage or current and observes the resulting change in output power to determine the tracking direction [17,18,19,20,21,22,23]. However, this approach can lead to continuous oscillations near the MPP and incorrect tracking when irradiance levels change abruptly [23]. To overcome these issues, several modifications have been introduced to improve accuracy, stability, and responsiveness. The most common enhancements include:

• Adaptive Step Size

Rather than using a fixed perturbation step, the modified P&O method dynamically adjusts the step size based on the proximity to the MPP [23,24]. When far from the MPP, larger steps are used to accelerate convergence, and as the system nears the MPP, smaller steps are applied to minimize oscillations and improve precision [24].

$$\Delta \mathrm{V}=\mathsf{\alpha }°\left|{\displaystyle \frac{dP}{dV}}\right|$$

(3)

where α is a scaling factor that controls the adaptation rate.

• Decision Delay or Hysteresis

To prevent unnecessary switching and oscillations, a dead zone (hysteresis band) can be introduced. If the change in power ΔP is below a certain threshold, the algorithm maintains the current voltage, assuming that the system is already near the MPP [25].

|ΔP| < ε

(4)

where ε is a predefined power threshold.

• Irradiance-Aware Modifications

Some modified P&O algorithms incorporate external inputs such as irradiance or temperature variation rates to differentiate between power changes due to environmental shifts and those caused by perturbations [26]. This helps prevent misinterpretation and improves robustness under dynamic conditions.

3.1.3. Incremental Conductance

The Incremental Conductance method is a widely used traditional MPPT technique that offers higher tracking accuracy compared to the Perturb and Observe method, particularly under rapidly changing environmental conditions [27]. This approach is based on the observation of the slope of the power–voltage curve of a photovoltaic array [27].

At the maximum power point (MPP), the derivative of the output power with respect to the voltage is zero:

$${\displaystyle \frac{\mathrm{d}\mathrm{P}}{\mathrm{d}\mathrm{V}}}=0$$

(5)

Given that power P is the product of current I and voltage V, the derivative becomes:

$${\displaystyle \frac{\mathrm{d}\mathrm{P}}{\mathrm{d}\mathrm{V}}}={\displaystyle \frac{\mathrm{d}(\mathrm{I}\times \mathrm{V})}{\mathrm{d}\mathrm{V}}}=\mathrm{I}+\mathrm{V}{\displaystyle \frac{\mathrm{d}\mathrm{I}}{\mathrm{d}\mathrm{V}}}$$

(6)

Setting this to zero at the MPP yields the tracking condition:

$${\displaystyle \frac{\mathrm{d}\mathrm{I}}{\mathrm{d}\mathrm{V}}}=-{\displaystyle \frac{\mathrm{I}}{\mathrm{V}}}$$

(7)

Thus, the InC method continuously calculates both the instantaneous conductance ${\displaystyle \frac{\mathrm{I}}{\mathrm{V}}}$ and the incremental conductance ${\displaystyle \frac{\mathrm{d}\mathrm{I}}{\mathrm{d}\mathrm{V}}}$ to determine whether the operating point is below, at, or above the MPP:

• If ${\displaystyle \frac{\mathrm{d}\mathrm{I}}{\mathrm{d}\mathrm{V}}}>-{\displaystyle \frac{\mathrm{I}}{\mathrm{V}}}$, the system is operating to the left of the MPP → increase voltage.
• If ${\displaystyle \frac{\mathrm{d}\mathrm{I}}{\mathrm{d}\mathrm{V}}}<-{\displaystyle \frac{\mathrm{I}}{\mathrm{V}}}$, the system is operating to the right of the MPP → decrease voltage.
• If ${\displaystyle \frac{\mathrm{d}\mathrm{I}}{\mathrm{d}\mathrm{V}}}=-{\displaystyle \frac{\mathrm{I}}{\mathrm{V}}}$, the system is at the MPP → maintain the current voltage.

3.1.4. Modified Incremental Conductance Method

The Modified Incremental Conductance method is an advancement of the conventional InC algorithm, developed to enhance tracking precision and dynamic performance in photovoltaic systems. While the traditional InC technique relies on the comparison between incremental and instantaneous conductance to locate the maximum power point, it can suffer from limitations such as steady-state oscillations and slow response under rapidly changing irradiance conditions [27,28,29].

To address these shortcomings, the modified InC introduces improvements that allow for more intelligent decision-making and smoother operation. One of the key features of the modified approach is the use of adaptive logic to monitor environmental stability [28]. For instance, when the system identifies that the MPP has been reached, it maintains the operating point without further adjustment unless a change in external conditions, such as irradiance or load, is detected [28].

In this improved version, the algorithm can include mechanisms such as:

• Flag-based control to distinguish between tracking and steady-state modes.
• Hysteresis thresholds to avoid unnecessary fluctuations near the MPP.
• Irradiance sensitivity to anticipate and react to abrupt environmental changes.
• Duty cycle freezing during stable conditions to reduce switching losses.

By incorporating these modifications, the algorithm ensures faster convergence to the MPP with reduced oscillations and more robust performance during dynamic weather scenarios [30]. It strikes a balance between precision and speed, making it a popular choice for real-time MPPT applications in modern PV systems [31].

3.1.5. Constant Voltage Method

The Constant Voltage method is one of the simplest MPPT strategies. It assumes that the maximum power point voltage V_mpp remains approximately constant under various irradiance levels [32]. The system operates at a fixed reference voltage, typically determined from prior experimental data or during a calibration phase [33].

The control algorithm compares the PV voltage V_pv to the reference value V_ref, and adjusts the duty cycle of the DC-DC converter to maintain the voltage close to V_ref [34].

V_ref ≈ k × V_oc

(8)

where

V_ref is the target voltage for operation.

V_oc is the open-circuit voltage of the PV array.

k is a constant typically between 0.7 and 0.8.

3.1.6. Open-Circuit Voltage Technique

This method is based on the empirical observation that the MPP voltage is a fixed fraction of the open-circuit voltage of the PV panel. Periodically, the PV array is disconnected to measure V_oc; then, the system sets the operating voltage to a fraction of it [35].

V_mpp ≈ k × V_oc

(9)

3.1.7. Short-Circuit Current

Similar in spirit to the OCV method, SCC uses the short-circuit current I_sc to estimate the current at the MPP [36]. The PV system is briefly short-circuited to measure I_sc, and then the operating current is set to a known fraction of it [37].

I_mpp ≈ k × I_SC

(10)

where k is typically around 0.9.

3.1.8. Hill Climbing

Hill Climbing is a dynamic method that adjusts the duty cycle of the converter and monitors the resulting change in output power [38]. If the power increases, the algorithm continues in the same direction. If the power decreases, it reverses the direction of the perturbation [39].

ΔP = P(k) − P(k − 1)

(11)

where P(k) is the PV power at the current step.

3.1.9. Fractional Open-Circuit Voltage

This technique improves upon the basic OCV method by using a fixed fraction of V_oc without repeatedly measuring it. Instead, the value is estimated from past measurements or stored data [40].

V_mpp = k × V_oc

(12)

3.1.10. Fractional Short-Circuit Current

As an improvement over SCC, this method uses previously known or estimated values of I_sc to determine the current setpoint for the converter, thereby avoiding frequent short-circuiting [41].

I_mpp = k × I_SC

(13)

3.1.11. Ripple Correlation Control

Ripple Correlation Control (RCC) utilizes the inherent ripples in voltage and current generated by high-frequency switching converters [42]. It analyzes the correlation between power fluctuations and these ripples to guide the operating point toward the MPP [43].

$${\displaystyle \frac{dP}{dV}}={\displaystyle \frac{d(I\times V)}{dV}}=I+V\times {\displaystyle \frac{dI}{dV}}$$

(14)

RCC identifies the sign of ${\displaystyle \frac{dP}{dV}}$ based on ripple behavior and adjusts the operating point accordingly [44].

3.2. Overview of Previous Works on Classical MPPT Techniques Developed Between 2015 and 2025

Between 2015 and 2025, considerable research has been dedicated to the refinement and evaluation of classical MPPT techniques, with the objective of improving their performance under diverse operating conditions. Scholars have focused on enhancing traditional algorithms such as Perturb and Observe, Incremental Conductance, and their modified variants, addressing their limitations in terms of tracking speed, accuracy, and the response to rapidly changing irradiance and temperature [45]. Several works have proposed adaptive step size mechanisms and hybrid configurations to reduce steady-state oscillations and improve convergence toward the true maximum power point [45,46]. This section presents a synthesized review of the most significant contributions made during this period, emphasizing the methodological advancements, experimental validations, and practical challenges encountered in implementing classical MPPT strategies in real photovoltaic systems.

3.3. Comparative Summary of Classical MPPT Methods

This part presents a detailed comparative summary of the most widely used classical MPPT techniques.

Table 2. Summary of previous works on classical MPPT techniques.

Classical MPPT Method	Year	Observations	Reference
Perturb And Observe	2015	P&O technique demonstrated a fast response and reliable tracking of the true MPP with minimal computational effort, outperforming simpler and more complex methods in terms of both efficiency and practicality.	[47]
Improved P&O	2015	The improved P&O algorithm enhances efficiency by reducing oscillations and preventing tracking divergence, achieving higher MPPT performance without additional hardware.	[48]
Incremental Conductance	2016	The proposed variable-step INC method improves tracking speed and accuracy under dynamic conditions, outperforming fixed-step approaches without requiring hardware changes.	[49]
Open-Circuit Voltage	2016	The Open-Circuit Voltage (OCV) method proves reliable for MPPT in TEG systems, maintaining accurate tracking even when thermal contact resistances are considered.	[50]
Hill Climbing	2016	The Hill Climbing method, combined with an enhanced SEPIC converter, enables effective MPPT and stable voltage output under varying irradiance and temperature conditions.	[51]
Fractional Open-Circuit Voltage and Fractional Short-Circuit Current	2021	Enhanced FSSC and FOCV algorithms using sensorless estimation improve tracking accuracy and eliminate power interruptions, achieving up to a 38% efficiency gain over conventional methods.	[52]
InC and Ripple Correlation Control	2021	RCC outperforms IC by minimizing steady-state ripples and improving stability under varying irradiance, though it shows slight underdamping during transitions.	[53]
P&O InC	2018	INC technique demonstrated higher tracking efficiency and faster response than P&O under varying atmospheric conditions.	[54]
Modified Perturb and Observe	2022	The proposed Modified P&O algorithm enhances tracking speed and reduces oscillations, achieving 99.7% efficiency by dynamically adjusting step sizes across PV curve regions.	[55]
OCV And P&O	2024	Analytical solution of the Fractional Open-Circuit Voltage outperforms P&O by boosting power output up to 5% and stabilizing voltage faster under varying weather, highlighting its adaptability and efficiency.	[56]
RCC method	2017	The study highlights the limitations of conventional RCC in multilevel inverter systems where multiple harmonics distort ripple signals. By introducing modified gradient estimation techniques, the authors improve MPPT accuracy under complex harmonic environments, demonstrating enhanced tracking performance in both steady and dynamic states.	[10]
Improved InC	2022	The proposed variable-step incremental conductance method enhances MPP tracking by segmenting the I–V curve into four regions with adaptive step sizes. It achieves faster response and zero steady-state oscillations under rapid irradiance changes, improving tracking accuracy and PV efficiency over traditional fixed-step INC.	[57]
Hill Climbing	2020	This study integrates the Hill Climbing MPPT algorithm with a solar tracking mechanism controlled by an Arduino and DC motor. By continuously adjusting panel orientation to optimize sunlight incidence, the system boosts voltage and net power output, even after accounting for the actuator and controller’s power consumption.	[58]
P&O and InC	2024	This work compares P&O and Incremental Conductance (INC) MPPT methods, highlighting P&O’s limitations under rapid irradiance changes and INC’s superior adaptability and tracking precision. Simulation results using MATLAB/Simulink confirm INC’s enhanced performance in maintaining MPP under dynamic conditions.	[59]
P&O	2025	An enhanced fixed-step P&O MPPT method is proposed and simulated using a boost converter, achieving 99.9% power efficiency. This approach improves the energy conversion of a PV system under varying conditions, demonstrating high overall performance with a simple control strategy.	[60]
InC	2025	A fixed-step INC-based MPPT algorithm is optimized through step size and frequency tuning for a low-power PV system. Simulation and experimental results confirm high efficiency (up to 98.93%) and fast dynamic response, making it suitable for practical, low-cost applications with AC loads.	[61]

below highlights key performance aspects such as algorithmic complexity, responsiveness to environmental variations, tracking precision, and real-time implementation feasibility. These criteria are essential for evaluating the practicality and effectiveness of each method under real-world photovoltaic operating conditions. The comparative analysis aims to assist researchers and engineers in selecting the most suitable approach based on the specific requirements of their PV systems.

Table 3. Comparative summary of classical MPPT control methods.

Method	Complexity	Adaptability	Accuracy	Real-Time Feasibility
Perturb & Observe	Low	Moderate	Moderate	High
Improved P&O	Moderate	Improved	High	High
Incremental Conductance	Moderate	High	High	Moderate to High
Modified Incremental Conductance	High	Very High	Very High	Moderate
Constant Voltage	Very Low	Low	Low	High
Open-Circuit Voltage	Low	Low	Low to Moderate	Moderate
Short-Circuit Current	Low	Low	Low	Moderate
Fractional OCV	Low	Low	Moderate	High
Fractional SCC	Low	Low	Moderate	High
Hill Climbing	Low	Moderate	Moderate	High
Ripple Correlation Control	High	High	High	Moderate

provides a comparative overview of classical MPPT control methods. It highlights each method’s complexity, adaptability, accuracy, and suitability for real-time implementation. While simpler approaches like Perturb & Observe and Constant Voltage offer ease of use and fast response, more advanced techniques such as Modified Incremental Conductance deliver higher accuracy and adaptability, albeit with increased complexity.

4. Intelligent MPPT Control Methods

4.1. Different Intelligent MPPT Methods

4.1.1. Fuzzy Logic Control

Fuzzy Logic Control is a heuristic-based MPPT method inspired by human reasoning and linguistic rules [27]. It does not require an exact mathematical model of the photovoltaic system, making it highly adaptable and robust in complex or nonlinear environments [27]. The FLC system typically consists of three components: fuzzification, a rule base (inference engine), and defuzzification [27].

• Fuzzification transforms numerical inputs (like the change in power and voltage) into fuzzy variables [62].
• The inference engine applies a set of “if–then” rules that mimic expert knowledge (If ${\displaystyle \frac{dP}{dV}}$ is small and positive, then decrease duty cycle slightly) [27].
• Defuzzification converts the fuzzy output back into a numerical value to adjust the duty cycle [27].

4.1.2. Artificial Neural Network

Artificial Neural Networks emulate the human brain’s learning capabilities to identify patterns between input and output parameters in PV systems. Typically, the ANN is trained with historical or simulated data containing variables like solar irradiance, temperature, voltage, and current to predict the optimal operating point [63]. An ANN structure includes an input layer, one or more hidden layers, and an output layer, as illustrated in Figure 3. After training, the ANN can generalize to unseen data, making it suitable for real-time MPPT [64].

• The training process uses algorithms such as backpropagation to minimize the mean squared error between predicted and actual outputs [63,64,65].
• Once trained, the ANN directly estimates the duty cycle or voltage corresponding to MPP [65].

4.1.3. Genetic Algorithm

The Genetic Algorithm is an evolutionary optimization method inspired by natural selection [66]. It starts with a population of candidate solutions (different duty cycles) and evolves them over generations through selection, crossover, and mutation operations [66].

The fitness function is often defined as the output power of the PV system [67]:

Fitness = P_PV = V × I

(15)

4.1.4. Support Vector Machine

Support Vector Machine is a supervised learning model used for classification and regression tasks [68]. In MPPT applications, SVMs are trained to learn the boundary between operating states (power increasing or decreasing) based on input features such as voltage and current variations [69].

4.1.5. Reinforcement Learning

Reinforcement Learning is a dynamic learning technique where an agent interacts with the PV system environment and learns an optimal control policy through rewards [70]. It is particularly useful in environments with uncertainty and time-varying conditions [70].

The agent (controller) chooses actions (increment/decrement duty cycle) based on the current state (voltage, power) [71]. A reward function is designed to reinforce power increases [72]:

R_t = P_t − P_t−1

(16)

4.1.6. Decision Tree-Based Control

Decision Tree (DT) models use a tree-like structure where internal nodes represent decision rules, branches represent outcomes, and leaf nodes indicate the final control action (adjust voltage or duty cycle) [73]. The DT is trained on a dataset mapping PV inputs (irradiance, voltage, current) to desired actions [74].

Each decision node splits the data based on a condition, for example, “is dP > 0?”, forming a hierarchy of rules. The model is interpretable, and decisions can be visualized clearly.

4.2. Overview of Previous Works on Intelligent MPPT Techniques Developed Between 2015 and 2025

The

Table 4. Summary of previous works on intelligent MPPT techniques.

Intelligent MPPT Method	Year	Observations	Reference
Fuzzy Logic Controller FLC	2015	This work introduces an optimized asymmetrical fuzzy logic control (FLC)-based MPPT, enhanced using particle swarm optimization (PSO) to fine-tune membership functions. The approach significantly improves tracking accuracy and response time compared to both symmetrical FLC and conventional P&O, effectively resolving the speed–accuracy trade-off in MPPT under standard test conditions.	[75]
ANN	2015	An ANN-based MPPT approach is introduced to effectively track the global MPP under rapid irradiance changes and partial shading, especially in mobile PV applications like EVs. The method requires only voltage and current inputs, making it hardware-efficient and cost-effective. It ensures a stable tracking time and exhibits high accuracy through optimized training on P-V data scans.	[76]
Reinforcement Learning	2017	This work introduces a Reinforcement Learning-based MPPT method modeled as a Markov Decision Process, which autonomously learns to track the maximum power point without prior system knowledge. Unlike classical methods, it adapts across various PV systems with minimal setup effort and shows fast, near-optimal performance under varying conditions, outperforming traditional P&O in adaptability and efficiency.	[77]
Genetic Algorithm	2015	The GA-based MPPT method offers robust and stable MPP tracking without needing irradiance or temperature data. By optimizing a fitness function, it reduces oscillations and outperforms P&O and InCond methods in response time and stability under dynamic conditions.	[78]
FLC	2016	The paper shows that the Fuzzy Logic MPPT outperforms P&O and Incremental Conductance methods in accuracy, speed, and stability under varying conditions, proven by MATLAB Simulink simulations on a 150 W PV module.	[79]
Decision Tree Control	2022	This paper presents a decision tree ML algorithm for MPPT that predicts maximum power and voltage under varying conditions. Simulations show it achieves over 93% efficiency and outperforms other methods in dynamic environments.	[80]
Support Vector Machine	2021	This paper introduces an SVM-based MPPT method that improves tracking speed and eliminates steady-state oscillations seen in conventional P&O techniques. Implemented with a boost converter and validated via real-time hardware-in-the-loop simulation, it shows superior performance under varying irradiance and temperature conditions while maintaining low complexity and cost.	[81]
ANN	2019	This paper proposes an ANN-based MPPT method trained on extensive real-world data collected over a year, improving accuracy and reducing training errors. Compared to the traditional P&O method, the ANN approach demonstrates faster response, less oscillation, and better tracking of the maximum power point, resulting in higher power output and avoiding tracking drift.	[82]
FLC	2018	This paper presents a Fuzzy Logic MPPT method for PV panels with a boost converter, combined with a PI-controlled buck converter for battery charging. The FLC MPPT accurately tracks the maximum power point (94.8–99.4% efficiency) under varying temperature and irradiance, with fast response and robustness to circuit changes. The PI controller ensures efficient, low-loss battery charging by maintaining stable current and voltage. The system is validated via MATLAB/Simulink simulations, showing improved efficiency and battery life.	[83]
Reinforcement Learning	2020	This paper introduces deep reinforcement learning methods, DQN and DDPG, for MPPT in photovoltaic systems, handling both discrete and continuous actions. Simulations show these methods outperform traditional Perturb and Observe, especially under partial shading, offering efficient and robust power tracking.	[84]
Reinforcement Learning	2024	This study develops a recurrent deep reinforcement learning MPPT controller using PPO and LSTM for photovoltaic systems under partial shading. The approach achieves high accuracy (95–98%) in tracking the global maximum power point across various dynamic conditions, outperforming recent methods by leveraging LSTM’s memory of past states for better decision-making.	[85]
FLC	2025	This study proposes an advanced fuzzy logic controller tuned by Arctic Puffin Optimization to enhance maximum power point tracking in boost converter-based photovoltaic systems. The optimized controller adapts quickly and accurately to varying temperature and irradiance, outperforming other algorithms like particle swarm and gray wolf optimizers. Simulations show tracking efficiency above 99.8% across diverse weather conditions, highlighting improved accuracy, stability, and response speed.	[86]
ANN	2025	This study proposes a grid-connected solar PV system with a High-Gain quasi Z-Source converter and an advanced MPPT using a modified bee colony and neural network, improving power extraction and efficiency, validated by MATLAB simulations.	[87]
SVM	2025	This study uses SVM to predict solar energy in off-grid areas, optimizing system design and boosting efficiency. It supports sustainable energy by reducing fossil fuel use and improving renewable energy adoption.	[88]

bellow provides a comprehensive summary of recent research on intelligent MPPT methods published between 2015 and 2025. It highlights the growing trend of integrating artificial intelligence techniques such as fuzzy logic, neural networks, reinforcement learning, and evolutionary algorithms into MPPT control systems. These approaches have demonstrated significant improvements in tracking efficiency, response time, and adaptability under partial shading and rapidly changing environmental conditions, marking a clear evolution beyond traditional MPPT strategies.

Table 5. Comparative summary of intelligent MPPT control methods.

Method	Complexity	Adaptability	Accuracy	Real-Time Feasibility
FLC	Medium	High	Medium	High
ANN	High	Very High	High	Medium
Genetic Algorithm	High	High	High	Low
Support Vector Machine	High	High	Very High	Medium
Reinforcement Learning	Very High	Excellent	Excellent	Medium–Low
Decision Tree	Low–Medium	Medium	Medium	High

presents a comparative summary of different intelligent MPPT methods.

4.3. Comparative Summary of Intelligent MPPT Methods

Table 5 presents a comparison of different intelligent MPPT methods, showing their complexity, adaptability, accuracy, and real-time feasibility. It provides a clear overview of how each method performs and helps the reader quickly understand their strengths and limitations.

5. Optimization MPPT Control Methods

5.1. Different Optimization MPPT Methods

5.1.1. Particle Swarm Optimization

Particle Swarm Optimization is a population-based metaheuristic inspired by the social behavior of bird flocks or fish schools [89]. Each potential solution is represented as a “particle” in a multidimensional space [90]. These particles move within the search space, guided by their own experience and the swarm’s global best position. In the context of MPPT, PSO efficiently searches for the maximum power point by continuously updating each particle’s velocity and position [89,90]. Let x_i^t be the position and V_i^t the velocity of particle i at iteration t [91].

V_i^t+1 = w V_i^t + c₁ r₁(p_i − x_i^t) + c₂ r₂(g − x_i^t)

(17)

x_i^t+1 = x_i^t + V_i^t+1

(18)

where p_i: personal best; g: global best; w: inertia weight; c₁,c₂: acceleration coefficients; and r₁,r₂: random numbers ∈ [0, 1].

5.1.2. Ant Colony Optimization

Ant Colony Optimization is inspired by the foraging behavior of real ants. In nature, ants find the shortest path to food using pheromone trails [92]. Similarly, in MPPT, artificial ants explore the search space and deposit virtual pheromones to guide the search toward the global MPP [93]. ACO builds a probabilistic model to choose the next step in the path based on the pheromone concentration τ and a heuristic desirability η [94].

τ_ij(t + 1) = (1 − p)τ_ij(t) + Δτ_ij(t)

(19)

where τ_ij: pheromone on path i–j; p: evaporation rate; and Δτ_ij: new pheromone based on quality of solution.

5.1.3. Cuckoo Search Optimization

The technique is inspired by the brood parasitism of cuckoos [95]. It uses Lévy flights to generate new solutions and replaces the worse solutions in the population: a cuckoo lays an egg (solution) in a randomly chosen nest, and the best nests are carried to the next generation [95,96].

5.1.4. Differential Evolution

Differential Evolution is an evolutionary algorithm that works with mutation, crossover, and selection [97]. It perturbs solutions using scaled differences between randomly selected individuals in the population [97].

5.1.5. Harmony Search Algorithm

Inspired by the musical improvisation process, HSA mimics musicians adjusting pitches to find a harmonious state [98]. Each solution vector represents a “harmony,” and the algorithm seeks to improve it by either memory consideration, pitch adjustment, or random selection [99].

• Improvisation rule [98]:

$${{\mathrm{X}}^{\mathrm{i}}}_{\mathrm{new}}=\left\{\begin{array}{l}{{\mathrm{X}}^{\mathrm{i}}}_{\mathrm{memory}},\\ {{\mathrm{X}}^{\mathrm{i}}}_{\mathrm{memory}}\pm \mathrm{rand}.\mathrm{bw}\\ \mathrm{rand}.({\mathrm{x}}_{\max }-{\mathrm{x}}_{\min })+{\mathrm{x}}_{\min }\end{array}\right.$$

5.1.6. Firefly Algorithm

The Firefly algorithm is based on the flashing behavior of fireflies [100]. Fireflies are attracted to brighter ones, and the brightness corresponds to the fitness of a solution [100]. This technique handles complex search spaces effectively and adapts well to the nonlinearity of PV systems under rapid changes [101].

$${\mathrm{x}}_{\mathrm{i}}={\mathrm{x}}_{\mathrm{i}}+{\mathsf{\beta }}_0{e}^{-\gamma r2}({\mathrm{x}}_{\mathrm{j}}-{\mathrm{x}}_{\mathrm{i}})+\mathsf{\alpha }\mathsf{ϵ}$$

(20)

where β₀: attractiveness at distance 0; γ: light absorption coefficient; r: distance between fireflies; α: randomization factor; and ϵ: random vector.

5.1.7. Simulated Annealing

Simulated Annealing is inspired by the annealing process in metallurgy [102]. It explores the solution space by accepting not only better solutions but also worse ones, based on a probability that decreases over time [103]. This method is beneficial for escaping local optima and is effective in MPPT scenarios where shading or noise might cause false peaks [103].

5.1.8. Grey Wolf Optimization

This method mimics the hunting behavior and hierarchy of grey wolves [104]. The population is divided into alpha, beta, delta, and omega wolves, with the top three guiding the search [105]. The technique has demonstrated excellent performance in MPPT tasks due to its balance between exploration and exploitation [105].

5.2. Overview of Previous Works on Optimization MPPT Techniques Developed Between 2015 and 2025

Table 6. Summary of previous works on optimization MPPT techniques.

Intelligent MPPT Method	Year	Observations	Reference
Particle Swarm Optimization	2015	This work applies PSO to optimize PI controllers in a hybrid renewable system (wind, PV, battery, and hydrogen). Three strategies are compared: offline tuning and two online self-tuning approaches using error and the ITAE index. The online ITAE-based tuning shows superior performance under dynamic grid conditions.	[106]
PSO	2016	This study compares MPPT performance using Fuzzy TS, P&O, and PSO in a PV–buck converter system. Simulations under varying weather conditions show that the PSO-based controller significantly improves MPPT efficiency and dynamic response over the other methods.	[107]
Cuckoo Search Optimization and PSO	2022	This study compares Cuckoo Search and adaptive PSO algorithms for MPPT in various PV array configurations under changing irradiance. Simulations using a boost converter show that both techniques enhance tracking efficiency, with Cuckoo Search demonstrating robust performance across different topologies.	[108]
Differential Evolution and PSO	2016	This work models a complete PV power plant integrated into a distribution network, highlighting the use of LCL filters to manage harmonics. It compares PSO and Differential Evolution methods for tuning PI controllers within a voltage-oriented control scheme, aiming to optimize system performance and filtering efficiency.	[109]
Harmony search algorithm	2016	This study proposes a reduced-sensor two-stage PV system using the Normal Harmonic Search for MPPT and Power-Normalized Kernel Least Mean Square for grid control. NHS enhances global MPP tracking under partial shading, while PNKLMS ensures power quality without a DC-link voltage sensor, showing strong performance in varied grid conditions.	[110]
Firefly algorithm	2020	This work uses the Firefly Algorithm (FA) for MPPT under partial shading, combined with a Zeta converter and an Adaptive PID controller based on MRAC for voltage regulation. FA shows fast and accurate tracking of maximum power, while the adaptive controller ensures a stable output closely following the reference model.	[111]
Firefly algorithm	2025	This study enhances buck converter performance by using the Firefly Algorithm to optimize PI controller parameters. The FA-based tuning improves system stability and reduces oscillations, demonstrating superior control in renewable energy and EV applications.	[112]
Simulated Annealing	2016	This paper applies the Simulated Annealing algorithm for MPPT in PV arrays, using power as the objective function linked to the duty cycle. The SA approach avoids local maxima and effectively finds the global optimum, especially under partial shading, outperforming classical MPPT methods.	[113]
Grey wolf Optimization	2023	This paper proposes a Grey Wolf Optimization-based MPPT controller for PV systems, outperforming conventional and other metaheuristic methods (PSO, SA, WO) in transient and full-day conditions. The GWO-based controller achieved superior power output, efficiency, and a faster response time under varying irradiance, temperature, and load scenarios.	[114]
PSO	2025	This study presents a PSO-based MPPT algorithm for a PV system using a boost converter, demonstrating superior performance over the Perturb and Observe method in terms of faster convergence, higher tracking efficiency, and reduced steady-state oscillations through MATLAB simulations.	[115]
Colony Optimization	2017	This work applies Ant Colony Optimization for MPPT to avoid local maxima and ensure global power extraction under varying irradiance. Both simulation and hardware results confirm its superiority over conventional techniques in tracking accuracy and system efficiency.	[116]

gives an overview of recent research on optimization-based MPPT techniques for photovoltaic systems published between 2015 and 2025. It summarizes approaches based on metaheuristic and bio-inspired algorithms such as Particle Swarm Optimization (PSO), Firefly Algorithm (FA), Grey Wolf Optimization (GWO), Simulated Annealing (SA), Differential Evolution (DE), Ant Colony Optimization (ACO), Cuckoo Search Optimization (CSO), and Harmony Search Algorithm (HAS). The table highlights how these methods improve performance and offer effective alternatives to classical MPPT strategies.

5.3. Comparative Summary of Optimization MPPT Algorithms

Building on the overview provided in Table 6, these optimization-based MPPT methods have demonstrated strong exploration capabilities, the ability to avoid local minima, and adaptability to changing environmental conditions such as partial shading, rapidly fluctuating irradiation, and temperature variations. These characteristics make them robust and high-performing solutions.

Table 7. Comparative summary of optimization MPPT control methods.

Method	Complexity	Adaptability	Accuracy	Real-Time Feasibility
PSO	Medium	High	High	Good
ACO	High	Medium	High	Moderate to Low
CSO	Medium	High	High	Moderate
DE	High	High	Very High	Moderate
HSA	Medium	Medium	Moderate	High
FA	Medium	High	High	Moderate
SA	Low	Medium	Moderate	High
GWO	Medium	Very High	Very High	Moderate to Good

presents a comparative summary of these techniques, showing their complexity, adaptability, accuracy, and suitability for real-time implementation.

6. Hybrid MPPT Control Methods

6.1. Different Hybrid MPPT Methods

6.1.1. Adaptive Neuro-Fuzzy Inference System (ANFIS)

ANFIS is a hybrid intelligent system that combines the learning capability of neural networks with the reasoning approach of fuzzy logic [117]. In MPPT applications, it effectively models nonlinear PV behavior and adapts to changing conditions, offering high tracking accuracy and strong generalization in complex environments [118].

6.1.2. Fuzzy Logic–Perturb and Observe (FL-P&O)

This method integrates fuzzy logic with the traditional Perturb and Observe algorithm to improve decision-making. The fuzzy controller refines the step size and direction based on input variations, making the MPPT process more stable and less oscillatory under varying irradiance [119].

6.1.3. Artificial Neural Network with Incremental Conductance (ANN-InC)

This hybrid technique leverages the learning and prediction strength of an artificial neural network to enhance the Incremental Conductance algorithm [120]. It predicts the optimal operating point with higher precision, even during fast-changing environmental conditions, thus improving convergence and stability [121].

6.1.4. Genetic Algorithm with Fuzzy Logic Controller (GA-FLC)

This hybrid approach uses a Genetic Algorithm to optimize the parameters of a Fuzzy Logic Controller [122]. The result is a self-adaptive MPPT system that balances simplicity and intelligence, capable of high-performance tracking under uncertain and dynamic weather conditions [122].

6.1.5. ANFIS with Particle Swarm Optimization (ANFIS-PSO)

In this combination, Particle Swarm Optimization is used to fine-tune the membership functions and learning parameters of the ANFIS model [123]. It enhances the learning accuracy and robustness of ANFIS, resulting in faster response and improved tracking during shading or temperature fluctuations [123].

6.1.6. Hybrid Swarm Intelligence Techniques

These techniques blend multiple swarm-based algorithms such as PSO, Ant Colony Optimization, or Grey Wolf Optimizer to leverage their complementary strengths [124]. The hybridization increases exploration and exploitation efficiency, leading to superior MPPT performance under highly dynamic and partial shading conditions [124].

6.2. Overview of Previous Works on Hybrid MPPT Techniques Developed Between 2015 and 2025

Table 8. Summary of previous works on hybrid MPPT techniques.

Intelligent MPPT Method	Year	Observations	Reference
ANFIS	2016	This paper proposes an ANFIS-based MPPT method for standalone solar systems using real weather data. It works with a Z source converter to track the best power point without needing voltage or current sensors. Simulations show that this smart controller reduces system complexity and cost while maintaining good performance.	[118]
FL-P&O	2021	This study compares a traditional P&O MPPT method with an improved version using Fuzzy Logic Control in a solar PV system. Implemented with a boost converter, the FLC adapts better to changes in sunlight and temperature, showing faster response and fewer power losses. Simulation results confirm that the FLC-based method offers better efficiency and stability than the traditional approach.	[119]
ANFIS	2018	This study presents an ANFIS-based MPPT controller for standalone PV systems, implemented on FPGA and trained with real data. Compared to traditional methods, it offers improved efficiency and faster response under changing conditions.	[125]
ANN-InC	2024	This paper evaluates three MPPT techniques—Incremental Conductance, Artificial Neural Network, and a hybrid INC-ANN—for a standalone PV system with a high-gain boost converter. Simulations show that the hybrid method outperforms the others in efficiency and response time, especially under varying weather conditions.	[120]
ANN-InC	2021	This paper presents a hybrid MPPT method, ANN-INC, that combines a neural network with incremental conductance to improve PV efficiency. The neural network provides an initial duty cycle for faster response and reduced oscillations. Simulations show excellent performance under rapidly changing irradiance.	[121]
GA-FLC	2023	This paper introduces an intelligent MPPT method that combines fuzzy logic control with genetic algorithm optimization to improve photovoltaic system performance. By tuning the fuzzy controller using GA, the method achieves better tracking accuracy and stability. Experimental validation using a dSPACE DS1104 confirms its efficiency under rapidly changing load conditions.	[122]
ANFIS-PSO	2025	An intelligent MPPT method using ANFIS optimized by PSO, enabling fast and accurate GMPP tracking under varying conditions with high efficiency and low THD, outperforming traditional techniques.	[123]
HIS	2022	A battery charging model for solar PV uses a buck-boost converter with a hybrid PSO-SSO MPPT and a FOPID-controlled buck converter. Simulations show high efficiency: 99.99% at STC and 99.52% under shading.	[124]
ANFIS	2025	A hybrid DC micro grid with PV, fuel cell, and battery is proposed. A cascaded ANFIS-based MPPT optimizes PV output. Simulations show improved voltage stability and 91% efficiency, outperforming traditional MPPT methods.	[126]

summarizes recent developments in hybrid MPPT techniques from 2015 to 2025. These approaches combine different MPPT methods to take advantage of their respective strengths, aiming to improve efficiency, adaptability, and stability under varying environmental conditions.

6.3. Comparative Summary of Hybrid MPPT Algorithms

Table 9. Comparative summary of hybrid MPPT control methods.

Method	Complexity	Adaptability	Accuracy	Real-Time Feasibility
ANFIS	High	Very High	Very High	Moderate
FL-P&O	Medium	Medium	Moderate	High
ANN-InC	High	High	High	Moderate
GA-FLC	High	High	High	Moderate to Low
ANFIS-PSO	Very High	Very High	Very High	Moderate
HIS	Very High	Very High	Excellent	Moderate to Low

shows a comparison between different hybrid MPPT methods used in photovoltaic systems. It highlights their main features, such as tracking speed, accuracy, efficiency under changing conditions, and how easy they are to implement. This comparison helps to clearly see the strengths and weaknesses of each method and choose the most suitable one for improving solar energy performance.

Based on the reviewed works, hybrid MPPT methods clearly outperform the other categories in terms of efficiency. Traditional methods like Incremental Conductance show relatively low efficiency, reaching only around 83.79% under standard conditions. Intelligent methods such as ANN and fuzzy logic improve this performance significantly, with ANN-based systems achieving around 88.94% and GA-FLC combinations showing high accuracy under varying conditions. Optimization techniques like PSO and ACO offer even better efficiency, often above 95%. However, hybrid approaches that combine intelligent and optimization methods demonstrate the highest efficiencies overall. For instance, the ANN-InC hybrid reached 97.48%, while the ANFIS-PSO method achieved zero oscillation with strong performance. The most outstanding result was obtained by the PSO-SSO hybrid algorithm, which reached 99.99% efficiency under standard test conditions and 99.52% under partial shading. These findings suggest that hybrid MPPT techniques offer the most effective and reliable performance for photovoltaic energy harvesting, especially in dynamic environments.

7. Criteria for Ranking Different MPPT Methods

To objectively assess the strengths and limitations of the various MPPT categories, a comparative analysis is conducted based on multiple performance criteria including tracking speed, efficiency, adaptability, complexity, and behavior under partial shading.

Table 10. Criteria for ranking different MPPT methods.

Criteria	Classical Methods	Intelligent Methods	Optimization-Based Methods	Hybrid Methods
Algorithm Complexity	Low	Medium to High	High	High
Hardware Requirements	Minimal	Moderate	High	High
Tracking Speed	Moderate	Fast	Fast	Very Fast
Steady State Oscillation	Moderate to High	Low	Low to Medium	Very Low
Performance under STC	Acceptable (90–95%)	Good (95–98%)	Excellent (98–99.5%)	Superior (>99.5%)
Performance under Partial Shading	Poor to Moderate	Good	Very Good	Excellent
Adaptability to Environment Changes	Low	High	High	Very High
Ease of Implementation	Very Easy	Medium	Complex	Complex
Overall Efficiency	90–95%	95–98%	98–99.5%	99.5–99.99%

, presents a synthesized ranking of Classical, Intelligent, Optimization-based, and Hybrid MPPT techniques. It reveals that while classical methods offer simplicity and low hardware demand, they suffer in dynamic and shaded conditions. Intelligent and optimization-based approaches improve tracking precision and adaptability but require more computation and tuning. Hybrid methods consistently achieve the highest efficiency, particularly under challenging scenarios, making them the most promising solutions for modern photovoltaic applications, despite their higher complexity.

8. Discussion and Recommendations for Future Research

The comparative analysis of various MPPT techniques highlights clear differences in terms of performance, complexity, and adaptability. Classical methods remain attractive due to their simplicity and low cost, but their limitations become apparent under unstable or rapidly changing sunlight conditions. In contrast, intelligent and optimization-based approaches offer improved tracking accuracy and faster response, especially in partially shaded or dynamic environments. However, these methods often require more hardware resources and careful tuning. Hybrid techniques, by combining the strengths of both approaches, currently achieve the best performance, frequently exceeding efficiencies of 99.5%, with remarkable stability and very fast response times.

That said, their complexity still poses a barrier to widespread and cost-effective implementation. Therefore, future researchers are strongly encouraged to focus on simplifying these hybrid methods while preserving their high performance. It would also be valuable to develop algorithms capable of automatically adapting to environmental variations without the need for human intervention or manual tuning. The integration of artificial intelligence, machine learning, and the Internet of Things could pave the way for smarter, predictive, and autonomous MPPT systems better suited to modern power grids. In summary, future research should aim to develop solutions that are efficient, intelligent, accessible, and ready to meet the demands of tomorrow’s photovoltaic systems.

9. Conclusions

This review has provided a detailed exploration of MPPT techniques applied in photovoltaic systems, with a focus on improving energy conversion efficiency under varying environmental conditions. Classical methods, though widely used, are often limited by steady state oscillations and slower dynamic responses. Intelligent approaches, particularly those based on fuzzy logic and neural networks, offer better adaptability but depend heavily on the quality of the training or rule-based design. Optimization-based algorithms, inspired by nature and swarm intelligence, have shown promising tracking performance, with improved accuracy and response speed. However, it is the hybrid MPPT methods that emerge as the most efficient and robust solutions, combining the advantages of multiple strategies to overcome individual limitations. These hybrid models consistently demonstrated superior performance, with some achieving efficiency levels exceeding 99.9% under standard test conditions and maintaining high effectiveness under partial shading. Looking ahead, future work should focus on the real-time implementation of hybrid MPPT controllers, their scalability in large PV systems, and reducing computational complexity while maintaining accuracy. Integration with smart grid technologies and adaptive control mechanisms based on real-time data analytics can further enhance reliability and energy yield in practical applications.