Ali Mahmood Jasim*
| Alaa Hussein Abdulaal | Ali Jasim Mohammed | Baraa M. Albaker | Muwafaq Shyaa Alwan
© 2026 The authors. This article is published by IIETA and is licensed under the CC BY 4.0 license (http://creativecommons.org/licenses/by/4.0/).
OPEN ACCESS
Solar panels usually encounter problems in delivering peak power when sunlight conditions are rapidly changing with low irradiance levels. Traditional Maximum Power Point Tracking (MPPT) methods generally have slow convergence speeds and low precision. To enhance the efficiency of solar energy systems, a hybrid Cuckoo Search–Incremental Conductance algorithm (CSA–INC) is proposed. It combines the strengths of the global optimization capability of the Cuckoo Search Algorithm with the fast dynamic response of the incremental conductance method. Simulation results under varying irradiance conditions show that the CSA–INC algorithm yields an impressive efficiency of 99.73%. This represents significant progress, resulting in reliable and efficient solar energy conversion under rapid weather changes and low-light conditions. Thus, it indirectly promotes the better utilization of renewable resources and supports the sustainable energy development goals.
incremental conductance, Cuckoo Search, Maximum Power Point Tracking, solar photovoltaic system
Photovoltaic systems are continuously advancing as a result of innovations in photovoltaic (PV) cell designs, improved energy conversion efficiencies and PV array architectures, deployment of advanced power-electronic interfaces, and use of effective control methods for Maximum Power Point Tracking (MPPT) [1, 2]. Nevertheless, environmental variations decrease their performance, leading to the destabilization of credible operation at large scales [3].
The characteristics of PV panels are nonlinear and time-varying current–voltage (I–V) [4-6]. These characteristics depend on the fluctuations of incident irradiance and temperature levels, which are considered difficult to predict [7]. Thus, there is a necessity for selecting a suitable control method to dynamically adjust system parameters to extract optimum output power from the system during variable environmental circumstances [8].
Since the output power of the PV panels depends on environmental factors, every unique pairing of irradiance and temperature results in a specific Maximum Power Point (MPP) [9]. A wide range of techniques has been developed to identify the MPP, differing in their circuit complexity, cost, accuracy, sensor requirements, and ease of practical deployment [10-12]. Conventional techniques, such as open-circuit voltage [13], short-circuit current [14], incremental conductance (INC) [15, 16], perturb and observe (P&O) [17-19], and hill climbing [20, 21], are straightforward to implement but often exhibit slow convergence, steady-state oscillations, and limited adaptability to rapid environmental changes.
In an effort to circumvent these limitations, some researchers explored the use of promising soft computing and metaheuristic techniques to identify MPP. Techniques such as Fuzzy Logic Control (FLC) [22, 23], Bat Algorithm (BA) [24, 25], Particle Swarm Optimization (PSO) [26, 27], Genetic Algorithm (GA) [28, 29], and Gravitational Search Algorithm (GSA) [30, 31] bring to the fore more serious search abilities and adaptability. These techniques, however, rarely come without a computational price to pay, fine synthesis of parameters, and in some cases, the solution gets stuck in a local optimum problem.
Another promising technique in this domain is the Cuckoo Search Algorithm (CSA) [32-34], which handles the nonlinear characteristics of PV systems and quickly converges in continuous changing conditions. However, the CSA suffers from steady-state oscillations and lower accuracy at low irradiance levels.
Several studies were conducted on both conventional and intelligent methods; however, none have combined fast response, global optimization, and consistent accuracy under rapidly changing environmental conditions. Traditional approaches are basically hindered by oscillation and slow adaptation, whereas metaheuristics generally lie in the domain of high computational complexity and sometimes do not provide the level of accuracy that has been relied upon in practical applications.
This paper introduces a hybrid control strategy based on the proposed CSA–INC algorithm, in which the CSA undertakes the global search while INC produces a fast response that can increase the tracking speed, stability, and accuracy under changing conditions. The effectiveness of the proposed algorithm was verified in the MATLAB/Simulink environment.
Sunlight and air temperature are major inputs for the PV module. The output parameters of the panel, namely voltage (VPV), current (IPV), along with irradiance (G) and temperature (T), are continuously monitored by the control unit [35]. These inputs are fed to different tracking algorithms. Three different approaches are evaluated:
(i) The conventional INC method
(ii) The CSA algorithm
(iii) The proposed hybrid CSA–INC algorithm
Figure 1. A solar photovoltaic system integrated with a load through a Maximum Power Point Tracking (MPPT) controller
Note: PV: photovoltaic; Vo: output voltage; Ro: load resistance; DC/DC: Direct Current to Direct Current; VPV: voltage; IPV: current
The proposed CSA-INC algorithm embeds the global search capability of CSA with the fast response provided by INC. The algorithm provides an optimal operating duty cycle as a control signal. This signal is then fed to a Pulse Width Modulation (PWM) controller, which subsequently drives the switching action of the Direct Current to Direct Current (DC–DC) boost converter. The boost converter works as an interface between the PV array and the electrical load to adjust the output voltage (Vo) and current to make the PV array operate close to its MPP. Finally, load resistance (Ro) represents the end-use device or system that actually consumes the harvested energy. The overall system configuration is shown in Figure 1.
The single-diode equivalent circuit is one of the most widely used models for simulating the current–voltage (I–V) characteristics of PV modules, as illustrated in Figure 2 [36-38]. This model captures the electrical behavior of a solar cell using a simplified circuit comprising a light-generated current source, a diode, a series resistance, and a shunt resistance. The photocurrent source (Iph) produces current in proportion to incident solar irradiance. It is connected in parallel with a diode that represents the nonlinear characteristics of the p–n junction. The single-diode model is chosen because it provides a good balance between computational simplicity and modeling accuracy. While more complex models (e.g., double- or triple-diode models) can capture additional physical effects, they require more parameters, which are often difficult to measure or estimate reliably. The single-diode model represents a good compromise between precision and computation demand for large-scale system studies and control design, such as maximum power extraction. In the equation below, (Is) is the saturation current of the diode, (Vo) denotes the terminal output voltage of the PV module, and the series resistance (Rs) accounts for the internal ohmic losses. The shunt resistance, denoted as (Rsh), models leakage currents, while (KVT) denotes the thermal voltage factor, which, unless otherwise specified, is taken as 26 mV at 300 K and depends on temperature. Individual cells in practical PV arrays are interconnected for higher output power. Cells connected in series per string (SCPS) increase the overall voltage, while the cells grouped into parallel strings (PS) boost the current available. By combining both series and parallel configurations, large PV arrays can be designed to meet particular demands for voltage and current. The total output current of the PV module (IT) is calculated using Eq. (1), which has been derived based on Kirchhoff's Current Law:
$I_T=I_{p h}-I_s\left[\exp \left(\frac{V_o+I_T R_s}{K_{V T}}\right)-1\right]-\frac{V_o+I_T R_s}{R_{s h}}$ (1)
Figure 2. Diagram of the single-parameter model
Note: photocurrent source (Iph); saturation current (Is); series resistance (Rs); shunt resistance (Rsh); diode current (Id); shunt resistance current (Ish); total current (IT)
(a) I–V characteristics
(b) P–V characteristics
Figure 3. Current–voltage (I–V) and power–voltage (P–V) characteristics of a photovoltaic (PV) array at varying irradiance levels
The total output current is the light-generated current reduced by the diode current and the current lost through the shunt path. The resistive elements further influence the relationship between voltage and current, as illustrated in Figure 3.
In high irradiance conditions, Iph becomes dominant, and the module is able to provide high output currents. Nevertheless, when the diodes are under low irradiance or high temperature, the saturation current (Is), series resistance (Rs), and shunt resistance (Rsh) become more prominent, resulting in a significant difference from the ideal I–V curve. Hence, this gives a reason for the drop in PV performance in the mentioned conditions. Given the Boltzmann's constant ($K_B$= 1.38 × 10−23 J/K), the major factor influencing PV array performance is the diode ideality factor (A). The modules' ratings are calculated based on the Standard Test Conditions (STCs) (1000 W/m², 25 ℃), which involve the operating temperature (T in Kelvin) and the electron charge (q = 1.6×10−19 C). The Iph is calculated using Eq. (2), which considers the irradiance–temperature relationship.
$I_{p h}=\frac{G_i}{G_S}\left[I_{s c . S T}+K_{s c}\left(T-T_r\right)\right]$ (2)
where, Gi is the solar irradiance that the PV surface is receiving, Gs is the standard irradiance level, and Isc.ST is the short-circuit current under STCs, and Ksc is the temperature coefficient indicating the short-circuit current’s dependence on temperature. The reference temperature (Tr) is used as a standard for measuring the effects of temperature on PV modeling. The diode's reverse saturation current (Irs) is a temperature-dependent parameter that is computed as depicted in Eq. (3), where it doubles exponentially with every 10 ℃ increases in temperature. This behavior contributes to the non-standard conditions I–V curve predictions being less accurate. The coefficient (Kov) is a scale factor of the open-circuit voltage drop with temperature, expressed in volts per Kelvin. It basically tells us how much voltage is being lost because of the increasing noise from the heat. Both parameters are necessary in order to achieve correct simulation and performance evaluation of PV modules subjected to different environmental conditions.
$I r_s=\frac{I_{s c . S T}+K_{s c}\left(T-T_r\right)}{\exp \left(V_{o c . S T}+K_{o v}\left(T-T_r\right) / A K_{V T}\right)-1}$ (3)
where, Voc.ST denotes the voltage at no load at the standard conditions. The temperature-intensity relations defined by these coefficients are important for precise PV modeling since they control the reverse saturation current, which is the main performance factor of the PV cell.
$I_s=I_{r s} \cdot\left(\frac{T}{T_r}\right)^3 \cdot \exp \left[\frac{q E_g}{A K_B}\left(\frac{1}{T_r}-\frac{1}{T}\right)\right]$ (4)
The diode saturation current (Is) is expressed as a function of temperature and energy, as presented in Eq. (4). The cubic term (T/Tr)3, along with the exponential component, signifies its large variation, nonlinear increase with temperature. The increase of (Is) with temperature is not a simple rise but rather an exponential climb that affects the I–V characteristics of the PV device. The electrical and material properties of the 1Soltech 1STH-250-WH module were employed for precise simulation and parameter extraction [37-40], with the PV parameters as listed in Table 1.
Table 1. Parameters of solar-powered
|
Photovoltaic Parameters |
Values |
|
PS |
1 |
|
SCPS |
1 |
|
PMP |
213.15 W |
|
Voc |
36.3 V |
|
Isc |
7.84 A |
|
VMP |
29 W |
|
IMP |
7.35 A |
|
Iph |
7.865 A |
|
Is |
2.9273e−10 A |
|
A |
0.98119 |
|
$n_s$ |
60 |
|
$n_p$ |
1 |
|
$R_s$ |
0.3938 Ω |
|
$R_sh$ |
313.0553 Ω |
The boost converter acts as a bridge between the PV array and the load and increases the low PV voltage to a stable level. It is evidenced by Figure 4 that it gets its control from a PWM signal, which is responsible for the switching of the Insulated Gate Bipolar Transistor (IGBT). The MPPT controller is constantly monitoring the PV voltage (VPV) and current (IPV), computing the power, and then making a comparison with the previous point. It will then set the highest possible duty cycle and will forward it to the PWM generator for the adjustment of the IGBT switching to ensure that power extraction is at its maximum. This is a closed-loop control which allows the MPPT technique to keep on adjusting the converter and thus the PV array is always at or very close to the MPP [41–42]. In order to have a power flow that is stable, the single-stage boost converter (SBC) is designed with certain passive components of utmost importance. The input capacitor (Ci), which is connected in parallel with the PV array, serves as a DC link for voltage stabilization, energy delivery support, and energy transfer prevention during those times when there is a change in the amount of light or load. The inductor (L) keeps energy when the IGBT is on and gives it away when it is off; the diode (D) prevents reverse current. The output capacitor (Co) eliminates voltage ripples, thus providing a stable power supply to the load (Ro). The converter scheme is depicted in Table 2 and Figure 4, with voltage and current relationships expressed in Eqs. (5) and (6).
$V_O=\frac{V_{P V}}{1-D}$ (5)
$I_O=\frac{I_{P V}}{1-D}$ (6)
By means of these equations, it is demonstrated that the duty cycle change brings about a shift in the voltage and current of the PV module, thereby directing it to the most power output area. Provided that there are no losses, the equivalent resistance of the load is represented by Eq. (7):
$R_O=\frac{V_O{ }^2}{P_{P V}}$ (7)
Figure 4. Relation of a photovoltaic system with a boost converter and power optimization control
Table 2. Parameters of the standard boost converter
|
Single-Stage Boost Converter Parameters |
Values |
|
Inductor, L |
6 mH |
|
Capacitor, Ci |
21 μF |
|
Capacitor, Co |
30 μF |
|
Output load resistance, Ro |
37 Ω |
|
Switching frequency, fs |
26 kHz |
As shown by Eq. (8), the voltage gain (Av) is defined as the ratio of the output voltage (Vo) to the input voltage (VPV).
$A_V=\frac{V_O}{V_{P V}}=\frac{1}{1-D}$ (8)
The output voltage can be made to greatly surpass the PV input voltage by varying the duty cycle. In this paper, the boost converter is configured to give an output voltage of around double the MPP voltage of the PV array, thus increasing the efficiency of power and energy transfer [43]. Tracking efficiency (η) refers to the proportion of power taken out of the power available at the maximum level, which is presented in Eq. (9):
$\eta=\frac{\text { Power Tracking }(\text { Po })}{\text { Maximum Power }(\text { Pin })} \times 100 \%$ (9)
The I–V characteristics of PV modules are nonlinear and vary depending on the irradiance and temperature. To ensure maximum power output, MPPT controllers monitor the voltage and current, perform power calculations, and modify the duty cycle of the DC–DC converter to follow the MPP [44-46].
MPPT techniques are divided into two main groups, which are classical methods and intelligent methods. Classical methods, including P&O, INC, and hill climbing, are simple and cheap [47, 48]. P&O has a problem with oscillations and loses its accuracy under fast changes [48, 49]. On the other hand, INC ensures stability but at the same time may lose some precision during small changes in irradiance [50].
Smart MPPT techniques, such as fuzzy logic, neural networks, genetic algorithms, and particle swarm optimization, are intelligent strategies that provide great adaptability and global search, particularly in partial shading with multiple peaks [49, 51-53]. However, these techniques have high computational requirements, need accuracy tuning, and demand additional sensors. The selection of an MPPT method is a compromise among cost, complexity, speed, and accuracy [50]. Esram et al. [2] have categorized more than 19 methods, all of which have particular advantages. The latest advancements are mainly directed towards the use of the adaptive step-size and hybrid MPPT approaches. These advancements are expected to enhance tracking precision, minimize oscillations, and increase response speed during rapid condition changes [54]. In practice, the MPPT controller adjusts a boost converter to keep the PV module at its MPP while ensuring stable power delivery [43, 51].
5.1 Incremental conductance
INC is a widely used classical MPPT algorithm that determines the optimum operating voltage of a PV module by comparing:
At the MPP, the slope of the PV power–voltage (P–V) curve is dP/dV = 0. Mathematically:
$\frac{d P}{d V}=\frac{d(V I)}{d V}=I+V \frac{d I}{d V}=0$ (10)
Which simplifies to:
$\frac{d I}{d V}=-\frac{I}{V}$ (11)
where, I is the module output current (A), and V is the module output voltage (V). The operating conditions of the INC algorithm are as follows:
By continuously applying these conditions, INC guides the PV system toward the exact MPP [43-46].
$\frac{d P}{d V}>=0=\frac{d I}{d V}>-\frac{I}{V}$ (12)
$\frac{d P}{d V}<=0=\frac{d I}{d V}<-\frac{I}{V}$ (13)
Figure 5. Flowchart of the incremental conductance Maximum Power Point Tracking (MPPT)
Figure 6. Flowchart of the Cuckoo Search Algorithm Maximum Power Point Tracking (MPPT) algorithm
The flowchart of the operational sequence of the INC algorithm is depicted in Figure 5. The controller samples PV voltage and current to calculate instantaneous and incremental conductance. The reference voltage (Vref) is then updated in the following manner:
Such a mechanism gives the INC algorithm the capability to steadily and precisely approach the MPP compared to the already mentioned methods, particularly P&O, when there is a quick change in irradiance.
5.2 Cuckoo Search Algorithm
CSA, a nature-inspired metaheuristic, is employed in MPPT to address the drawbacks encountered in classical MPP methods, even under tough environmental conditions [55]. The “cuckoo brood parasitism” concept is the basis of CSA, which employs global search with a population-based approach to locate the real MPP. This technique proves to be effective in partial shading situations where the traditional ones, like P&O or INC, might not work at all [56].
The CSA algorithm considers different duty cycles by resorting solely to the power output generated from the solar PV modules, i.e., PPV, thus pointing out:
Additionally, candidate solutions are revived through Lévy flight-driven random walks, which allow both local refinement and global exploration.
$d(i)=d$ best $+\alpha \cdot \operatorname{Lévy}(\lambda)$ (14)
where, d(i) is the current duty cycle, α is the step-size scaling coefficient, Lévy(λ) is the Lévy distribution function with λ controlling step length.
The algorithm is characterized by high computational efficiency, low execution time, and low hardware resource requirements. Thus, it is an ideal choice for PV systems with limited processing capacity [56]. The literature indicates its faster convergence and better power tracking accuracy over traditional MPPT methods [57]. Figure 6 presents the flowchart of the MPPT based on CSA. It assesses the prospective duty cycles, selects the best and worst solutions, and modifies them through Lévy flight exploration.
Despite being quite effective, CSA does have its handcuffs like the steady-state oscillations, the parameter setting sensitivity, and the possibility of early convergence to local optima [56]. These problems are related to the accuracy versus response speed dilemma that is typical in bio-inspired algorithms.
In real implementation, the CSA-based MPPT is normally combined with a DC–DC boost converter to ensure that the PV system operates close to the global MPP even during fluctuations in irradiance and temperature [55, 56]. The simulation results, particularly in MATLAB/Simulink, always prove the effectiveness of CSA, especially when the partial shading is present [57].
Both INC and CSA are intended to track the MPP, while they use different strategies for their operation. INC is considered to be a simple solution to the problem that needs very little computation and a fast reaction to slight irradiance variations. However, it has low accuracy and makes steady-state oscillations around the MPP.
5.3 Hybrid Cuckoo Search Algorithm–Incremental Conductance algorithm
Even though CSA is more computationally intensive, it efficiently manages to deal with nonlinear PV behavior and partial shading PV modules through its ability to perform a global search for MPP. On the downside, the method can be influenced by parameter variability and oscillations at steady state [58]. All these issues make the hybrid CSA–INC algorithm attractive, as a combination of global searching by CSA and fast local tracking by INC results in better MPPT performance.
The hybrid CSA–INC algorithm combines the latter’s rapid convergence with the former’s global search for the purpose of achieving efficient and adaptive MPPT for PV systems. By employing random walks and Lévy flights, CSA enables the following: global search through long jumps, thus avoiding local optima, and local refinement through short steps [59].
The effectiveness of the CSA method during partial shading is due to this dual search strategy, which prevents conventional tracking techniques from being trapped in several local maxima. The general update rule for generating new duty cycles is:
$x_i^{(t+1)}=x_i^t+\alpha \cdot \operatorname{Lévy}(\lambda)$ (15)
where, $x_i{ }^t$ denotes the solution at iteration t. This allows CSA to efficiently search the duty cycle space and move toward the global MPP. The step-size factor (α) balances exploration and exploitation:
In practice, α is tuned based on convergence and irradiance changes. Smaller values of α improve precision near the MPP, while larger ones boost exploration during shading or sudden conditions. In the hybrid method, CSA performs global exploration to locate potential MPP regions, then INC takes over for precise local tracking.
Figure 7. Hybrid Cuckoo Search Algorithm–incremental conductance-based Maximum Power Point Tracking (MPPT) control strategy implemented in the boost converter system
Figure 8. Flowchart of the proposed incremental conductance and Cuckoo Search Algorithm
Table 3. Comparative summary of Maximum Power Point Tracking (MPPT) methods
|
Method |
Advantages |
Limitations |
Complexity |
|
Perturb and observe (P&O) |
Simple, low-cost, easy to implement |
Oscillations; poor performance in fast changes |
Low |
|
Incremental conductance (INC) |
Better stability, responsive to variations |
Struggles with small irradiance changes |
Moderate |
|
Fuzzy logic |
Handles uncertainty, good adaptability |
Requires a rule-based tuning; tuning is complex |
Moderate |
|
Particle swarm optimization (PSO) |
Global optimization is good under shading |
High computation; risk of local optima |
High |
|
Cuckoo Search Algorithm (CSA) |
Fast convergence, global search capability |
May oscillate; reduced accuracy at low irradiance |
High |
|
Proposed Cuckoo Search Algorithm–Incremental Conductance (CSA–INC) |
Combines INC stability with CSA optimization, fast, accurate, robust |
Added design complexity vs. single methods |
Moderate–High |
Figures 7 and 8 illustrate the proposed control strategy of the hybrid CSA–INC MPPT for solar energy systems, showing the integration of both algorithms and how the average duty cycle controls the boost converter via PWM. As a result, the combination of the two methods allows efficient performance and adaptive maximum power point tracking, while also exceeding the performance of individual methods in varying situations. The hybrid method overcomes the weaknesses of each standalone approach through:
Combining CSA’s global search with INC’s precision, the hybrid CSA–INC algorithm offers:
Table 3 provides a comparative overview of various MPPT techniques. The hybrid CSA–INC method offers a well-balanced solution and effectively maximizes energy extraction in scenarios where individual algorithms may underperform.
The effectiveness of the proposed CSA–INC algorithm was assessed under varying irradiance conditions of 200, 600, and 1000 W/m². As depicted in Table 4, the hybrid approach consistently demonstrated superior performance over standalone INC and CSA methods in terms of power tracking accuracy, voltage regulation, and overall efficiency across all scenarios. The discussion focuses on the performance trends observed rather than isolated numerical results.
Table 4. Simulation results of Maximum Power Point Tracking (MPPT) algorithms under different irradiance levels
|
Irradiation Values W/m2 |
MPPT Methods |
Maximum Power Pin (W) |
Power Tracking Po (W) |
Voltage Vo (V) |
Current Io (A) |
Efficiency (η) |
|
1000 |
INC |
96.62 |
95.33 |
59.39 |
1.605 |
98.67 |
|
CS |
212.9 |
212.2 |
88.6 |
2.395 |
99.66 |
|
|
Hybrid |
213 |
212.5 |
88.66 |
2.396 |
99.73 |
|
|
600 |
INC |
89.15 |
87.89 |
57.03 |
1.541 |
98.58 |
|
CS |
128.8 |
127.4 |
68.66 |
1.856 |
98.95 |
|
|
Hybrid |
129.4 |
128.3 |
68.91 |
1.862 |
99.18 |
|
|
200 |
INC |
31.28 |
30.46 |
33.57 |
0.9074 |
97.4 |
|
CS |
42.46 |
41.41 |
39.14 |
1.058 |
97.52 |
|
|
Hybrid |
41.77 |
40.74 |
38.83 |
1.049 |
97.53 |
The hybrid CSA–INC approach outperforms standalone INC and CSA methods in terms of convergence speed and enhanced operational stability. At an irradiance level of 1000 W/m², the hybrid algorithm achieved the highest output power of 212.5 W, approximately 123% higher than INC (95.33 W) and marginally outperforming CSA (212.2 W), as depicted in Figures 9 and 10. Under 600 W/m², the proposed hybrid algorithm yielded 128.3 W, representing a 46% improvement over INC (87.89 W) and exhibiting greater voltage stability compared to CSA (127.4 W). Even at low irradiance (200 W/m²), where MPPT effectiveness typically declines, the hybrid algorithm delivered a 33% increase in performance relative to INC.
Figure 9. Power tracking performance at irradiance level 1 KW/m2
Figure 10. Simulation results of Maximum Power Point Tracking (MPPT) algorithms under varying irradiance levels (200, 600, and 1000 W/m²)
Therefore, the hybrid approach has kept the PV voltage closer to the theoretical MPP for the entire tested irradiance range. At 1000 W/m², the hybrid converged at 88.6 V, representing a 49% improvement over the INC approach at 59.4 V and very similar to CSA at 88.6 V, as observed in Figures 11 and 12. At an irradiance level of 600 W/m², the hybrid system sustained a voltage of 68.9 V, which was around 21% greater than the voltage (57.0 V) measured by the INC technique. Such advantages signify lower overshoot and excursion, which in turn means better energy delivery stability.
Figure 11. Voltage tracking performance at 1000 W/m²
Figure 12. Maximum power point tracking algorithm voltage comparison
The analysis of efficiency clearly showed that the proposed algorithm is the best choice. It reached 99.73% at 1000 W/m², which is 1.1% better than INC and 0.07% better than CSA. For 600 and 200 W/m², a 0.6% and 0.4% improvement over INC was recorded. The oscillations were less and the efficiency steady over the different irradiances, as shown in Figures 13 and 14.
Figure 13. Efficiency of different irradiance levels
Figure 14. Efficiency of maximum power point tracking algorithms
In Table 5, the CSA–INC algorithm reveals an efficiency of 99.73%, thus surpassing the majority of contemporary hybrid MPPT techniques. This underlines its importance and considerable promise for future photovoltaic applications.
Table 5. Comparative characteristics of various maximum power point tracking techniques, including the proposed method
|
Reference |
Year |
Method |
Efficiency |
|
[60] |
2024 |
Hybrid Incremental Conductance+ Artificial Neural Network) |
99.52% |
|
[61] |
2018 |
Hybrid Particle Swarm Optimization + Adaptive Neuro-Fuzzy Inference System |
97% |
|
[62] |
2016 |
Hybrid Perturb-and-Observe + Adaptive Neuro-Fuzzy Inference System |
85% |
|
[63] |
2018 |
Hybrid Fuzzy Logic Controller + Perturb and Observe, Fuzzy Logic Controller + Incremental Conductance |
97% |
|
[64] |
2019 |
Hybrid Fuzzy Logic Controller + Incremental Conductance |
95.3% |
|
[65] |
2022 |
Hybrid Fuzzy Logic Controller + Perturb and Observe |
99.52% |
|
Proposed |
2025 |
Hybrid Incremental Conductance+ Cuckoo Search Algorithm |
99.73% |
The research presented here suggested a hybrid MPPT approach merging CSA's global search with INC's fine convergence, thus increasing the approach's capability to adapt and precisely extract MPP under changing light conditions. The results demonstrated that the integration of both heuristic and classical tracking methods enhances the stability and responsiveness of the PV systems. While CSA prevents the system from getting stuck in local optima, INC takes care of the accurate and fast convergence needed for effective MPPT.
The approach showed excellent performance in MATLAB/Simulink; however, it also has to cope with higher computational load and complications in real-time implementation. Moreover, there is still a necessity to perform thorough testing to find out the behavior of the hybrid approach under rapid irradiance changes. Future research could be directed toward hardware-in-loop (HIL) validation and embedded deployment for large-scale photovoltaic and grid-connected systems.
To sum up, the hybrid CSA–INC approach provides remarkable MPPT efficiency of 99.73%, indicating a progressive performance enhancement for solar energy systems. When the algorithm is coupled with hardware implementation, it will not only enhance the efficiency but also be adaptive to rapidly changing environmental conditions, partially shading, and work effectively in low irradiance. Thus, contributing to the transition to effective and adaptive MPPT development for renewable energy systems.
The future direction of the hybrid CSA–INC algorithm may prioritize cutting down the computational demands for real-time implementation. This is achieved through real-time performance validation using HIL and microcontroller testing. In addition, the adoption of automatic tuning of critical parameters such as α and Pa to secure the reliable operation of the approach.
The above-said steps are crucial for the realistic development of the proposed algorithm using an actual embedded PV system. The mid-term targets are the incorporation of predictive models for the variations in solar radiation and temperature as well as the improvement of the performance in cases of partial shading and multi-peak P–V conditions.
Over the long term, the strategy for using the algorithm would be in microgrids and grid-connected PV systems. The focus should be on resilience, stability, and smart-grid compliance. Therefore, the algorithm will gradually shift from simulation to real-world application.
The authors express their gratitude to Al-Iraqia University for supporting this study.
[1] Green, M.A., Dunlop, E.D., Hohl‐Ebinger, J., Yoshita, M., Kopidakis, N., Hao, X. (2021). Solar cell efficiency tables (Version 58). Progress in Photovoltaics, 29(7): 657. https://doi.org/10.1002/pip.3444
[2] Esram, T., Chapman, P.L. (2007). Comparison of photovoltaic array maximum power point tracking techniques. IEEE Transactions on Energy Conversion, 22(2): 439-449. https://doi.org/10.1109/tec.2006.874230
[3] Blaabjerg, F., Yang, Y., Yang, D., Wang, X. (2017). Distributed power-generation systems and protection. Proceedings of the IEEE, 105(7): 1311-1331. https://doi.org/10.1109/jproc.2017.2696878
[4] Meredith, P. (2024). Solar energy for societal decarbonisation: a perspective on emerging opportunities in utility-scale solar electricity and solar in the built environment. Frontiers in Sustainable Cities, 6: 1344771. https://doi.org/10.3389/frsc.2024.1344771
[5] Logeswaran, T., SenthilKumar, A. (2014). A review of maximum power point tracking algorithms for photovoltaic systems under uniform and non-uniform irradiances. Energy Procedia, 54: 228-235. https://doi.org/10.1016/j.egypro.2014.07.266
[6] Liu, H., Tian, H.Q., Pan, D.F., Li, Y.F. (2013). Forecasting models for wind speed using wavelet, wavelet packet, time series and artificial neural networks. Applied Energy, 107: 191-208. https://doi.org/10.1016/j.apenergy.2013.02.002
[7] Messenger, R.A., Abtahi, H.A. (2018). Photovoltaic Systems Engineering. 2018. CRC Press. https://doi.org/10.1201/9781315218397
[8] Patel, H., Agarwal, V. (2008). MATLAB-based modeling to study the effects of partial shading on PV array characteristics. IEEE Transactions on Energy Conversion, 23(1): 302-310. https://doi.org/10.1109/tec.2007.914308
[9] Sera, D., Teodorescu, R., Rodriguez, P. (2007). PV panel model based on datasheet values. In 2007 IEEE International Symposium on Industrial Electronics, Vigo, Spain, pp. 2392-2396. https://doi.org/10.1109/ISIE.2007.4374981
[10] Kazem, H.A., Chaichan, M.T., Al-Waeli, A.H., Sopian, K. (2024). Recent advancements in solar photovoltaic tracking systems: An in-depth review of technologies, performance metrics, and future trends. Solar Energy, 282: 112946. https://doi.org/10.1016/j.solener.2024.112946
[11] Gasia, J., Miró, L., Cabeza, L.F. (2016). Materials and system requirements of high temperature thermal energy storage systems: A review. Part 2: Thermal conductivity enhancement techniques. Renewable and Sustainable Energy Reviews, 60: 1584-1601. https://doi.org/10.1016/j.rser.2016.03.019
[12] Karim, S., Faissal, A., Noureddine, E.B. (2022). Chapter 27–National renewable energy laboratory. ScienceDirect, 54: 228-235. https://doi.org/10.1016/B978-0-12-823764-9.00006-6
[13] Baimel, D., Tapuchi, S., Levron, Y., Belikov, J. (2019). Improved fractional open circuit voltage MPPT methods for PV systems. Electronics, 8(3): 321. https://doi.org/10.3390/electronics8030321
[14] Arsa, I.S., Sutaya, I.W. (2023). Implementation of the MPPT short circuit method in determining the number of electrical load that can be supplied by solar panels. In IConVET 2022: Proceedings of the 5th International Conference on Vocational Education and Technology, Singaraja, Bali, Indonesia, pp. 78. https://doi.org/10.4108/eai.6-10-2022.2327351
[15] Chellakhi, A., El Beid, S., Abouelmahjoub, Y., Doubabi, H. (2024). An enhanced incremental conductance MPPT approach for PV power optimization: A simulation and experimental study. Arabian Journal for Science and Engineering, 49(12): 16045-16064. https://doi.org/10.1007/s13369-024-08804-1
[16] Shang, L., Guo, H., Zhu, W. (2020). An improved MPPT control strategy based on incremental conductance algorithm. Protection and Control of Modern Power Systems, 5(2): 1-8. https://doi.org/10.1186/s41601-020-00161-z
[17] Manohara, M., Bharadwaj, D.D., Reddy, K.A.K., Madhav, D.V., Ganesh, D.V., Thrinath, B. S. (2024). Analysis of perturb & observe MPPT algorithm in partial shading for DC-DC boost converters. In 2024 Second International Conference on Smart Technologies for Power and Renewable Energy (SPECon), Ernakulam, India, pp. 1-4. https://doi.org/10.1109/specon61254.2024.10537508
[18] Ghamrawi, A., Gaubert, J.P., Mehdi, D. (2018). A new dual-mode maximum power point tracking algorithm based on the Perturb and Observe algorithm used on solar energy system. Solar Energy, 174: 508-514. https://doi.org/10.1016/j.solener.2018.09.013
[19] Abdel-Salam, M., El-Mohandes, M.T., Goda, M. (2018). An improved perturb-and-observe based MPPT method for PV systems under varying irradiation levels. Solar Energy, 171: 547-561. https://doi.org/10.1016/j.solener.2018.06.080
[20] El Alami, Y., Chetouani, E., Mokhliss, H., Ouerradi, F., et al. (2024). Optimizing solar energy efficiency with an improved hill-climbing maximum power point tracking control approach: hardware implementation. Clean Energy, 8(5): 167-176. https://doi.org/10.1093/ce/zkae061
[21] Nzoundja Fapi, C.B., Tchakounté, H., Tchatchueng, F.T., Wira, P., Louzazni, M., Kamta, M. (2023). Recent advancements in maximum power point tracking with hill climbing method for a PV system: A review. Applied Solar Energy, 59(4): 378-399. https://doi.org/10.3103/S0003701X23600224
[22] Xia, K., Li, Y., Zhu, B. (2024). Improved photovoltaic MPPT algorithm based on ant colony optimization and fuzzy logic under conditions of partial shading. IEEE Access, 12: 44817-44825. https://doi.org/10.1109/access.2024.3381345
[23] de Lima, T.M., Oliveira, J.A.C.B. (2024). FPGA-based fuzzy logic controllers applied to the MPPT of PV panels—A systematic review. IEEE Transactions on Fuzzy Systems, 32(8): 4260-4269. https://doi.org/10.1109/tfuzz.2024.3392846
[24] Eltamaly, A.M. (2021). Optimal control parameters for bat algorithm in maximum power point tracker of photovoltaic energy systems. International Transactions on Electrical Energy Systems, 31(4): e12839. https://doi.org/10.1002/2050-7038.12839
[25] Da Rocha, M.V., Sampaio, L.P., Da Silva, S.A.O. (2020). Comparative analysis of MPPT algorithms based on Bat algorithm for PV systems under partial shading condition. Sustainable Energy Technologies and Assessments, 40: 100761. https://doi.org/10.1016/j.seta.2020.100761
[26] Li, H., Yang, D., Su, W., Lü, J., Yu, X. (2018). An overall distribution particle swarm optimization MPPT algorithm for photovoltaic system under partial shading. IEEE Transactions on Industrial Electronics, 66(1): 265-275. https://doi.org/10.1109/tie.2018.2829668
[27] Hendy, M.A., Nayel, M.A., Rodriguez, J., Abdelrahem, M. (2024). Enhanced maximum power point tracking using modified PSO hybrid with MPC under partial shading conditions. IEEE Access, 12: 145318-145330. https://doi.org/10.1109/access.2024.3471829
[28] Badis, A., Mansouri, M.N., Boujmil, M.H. (201). A genetic algorithm optimized MPPT controller for a PV system with DC-DC boost converter. In 2017 International Conference on Engineering & MIS (ICEMIS), Monastir, Tunisia, pp. 1-6. https://doi.org/10.1109/icemis.2017.8273010
[29] Azad, M.A., Sarwar, A., Tariq, M., Bakhsh, F.I., Ahmad, S., Mohamed, A.S.N., Islam, M.R. (2025). Global maximum power point tracking for photovoltaic systems under partial and complex shading conditions using a PID based search algorithm (PSA). IET Renewable Power Generation, 19(1): e70005. https://doi.org/10.1049/rpg2.70005
[30] Pervez, I., Sarwar, A., Tayyab, M., Sarfraz, M. (2019). Gravitational search algorithm (GSA) based maximum power point tracking in a solar PV based generation system. In 2019 Innovations in Power and Advanced Computing Technologies (i-PACT), Vellore, India, pp. 1-6. https://doi.org/10.1109/i-pact44901.2019.8960130
[31] Ariefianto, R.M., Setyawan, I., Nurwati, T., Hasanah, R.N., Ramli, M.A.M. (2024). An MPPT strategy based on Gravitational Search Optimization (GSO) for improving PV system applications in Isolated areas. In 2024 12th Electrical Power, Electronics, Communications, Controls and Informatics Seminar (EECCIS), Malang, Indonesia, pp. 56-62. https://doi.org/10.1109/eeccis62037.2024.10840108
[32] Mariprasath, T., Basha, C.H., Khan, B., Ali, A. (2024). A novel on high voltage gain boost converter with cuckoo search optimization based MPPT Controller for solar PV system. Scientific reports, 14(1): 8545. https://doi.org/10.1038/s41598-024-58820-2
[33] Eltamaly, A.M. (2021). An improved cuckoo search algorithm for maximum power point tracking of photovoltaic systems under partial shading conditions. Energies, 14(4): 953. https://doi.org/10.3390/en14040953
[34] Benlafkih, A., El Moujahid, Y., Hadjoudja, A., El Harfaoui, N., Said, E.B., El Idrissi, M.C. (2024). Optimizing photovoltaic systems performance under partial shading using an advanced cuckoo search algorithm. International Journal of Power Electronics and Drive Systems, 15(2): 845-857. https://doi.org/10.11591/ijpeds.v15.i2.pp845-857
[35] Abou Jieb, Y., Hossain, E. (2022). Photovoltaic Systems. Springer International Publishing. https://doi.org/10.1007/978-3-030-89780-2
[36] Rawa, M., Calasan, M., Abusorrah, A., Alhussainy, A.A., et al. (2022). Single diode solar cells—Improved model and exact current–voltage analytical solution based on Lambert’s W function. Sensors, 22(11): 4173. https://doi.org/10.3390/s22114173
[37] Li, J., Qin, C., Yang, C., Ai, B., Zhou, Y. (2023). Extraction of single diode model parameters of solar cells and PV modules by combining an intelligent optimization algorithm with simplified explicit equation based on Lambert W function. Energies, 16(14): 5425. https://doi.org/10.3390/en16145425
[38] Jasim, A.M., Albaker, B.M., Jabir, H.J. (2024). Modeling and simulation of simplified quadruple diode solar PV module under influence of environmental conditions and parasitic resistance. TEM Journal, 13(1): 757-770. https://doi.org/10.18421/tem131-79
[39] Abbassi, R., Boudjemline, A., Abbassi, A., Torchani, A., Gasmi, H., Guesmi, T. (2018). A numerical-analytical hybrid approach for the identification of SDM solar cell unknown parameters. Engineering, Technology & Applied Science Research, 8(3): 2907-2913. https://doi.org/10.48084/etasr.2027
[40] Jasim, A.M. (2023). Design and implement of step-up DC-DC converter with a professional approach using MATLAB/simulink. Al-Iraqia Journal for Scientific Engineering Research, 2(4): 133-143. https://doi.org/10.58564/ijser.2.4.2023.126
[41] Babaa, S.E., El Murr, G., Mohamed, F., Pamuri, S. (2018). Overview of boost converters for photovoltaic systems. Journal of Power and Energy Engineering, 6(4): 16-31. https://doi.org/10.4236/jpee.2018.64002
[42] Hidayat, A.R., Yudaningtyas, E., Siwindarto, P. (2020). Performance comparison maximum power point tracking methods using SEPIC converter. In International Conference on Renewable Energy (ICORE) pp. 012024. https://doi.org/10.1088/1742-6596/1595/1/012024
[43] Bouadjila, T., Khelil, K., Rahem, D., Berrezzek, F. (2023). Improved Artificial neural network based MPPT Tracker for PV system under Rapid varying Atmospheric conditions. Periodica Polytechnica Electrical Engineering and Computer Science, 67(2): 149-159. https://doi.org/10.3311/ppee.20824
[44] Jasim, A.M. (2025). Design and simulation of photovoltaic system for tracking maximum power point controlled double boost converter. In AIP Conference Proceedings, International Research Conference on Engineering and Applied Sciences, p. 020003. https://doi.org/10.1063/5.0257722
[45] Amoh Mensah, A., Wei, X., Otuo-Acheampong, D., Mbuzi, T. (2024). Maximum power point tracking techniques using improved incremental conductance and particle swarm optimizer for solar power generation systems. Energy Harvesting and Systems, 11(1): 20220120. https://doi.org/10.1515/ehs-2022-0120
[46] Raouf, A.H., Yazdiniya, F.S., Ansarifar, G.R. (2024). Super-twisting ADRC for maximum power point tracking control of photovoltaic power generation system based on non-linear extended state observer. Heliyon, 10(16): e36428. https://doi.org/10.1016/j.heliyon.2024.e36428
[47] Chaibi, Y., Salhi, M., El-Jouni, A. (2019). Sliding mode controllers for standalone PV systems: Modeling and approach of control. International Journal of Photoenergy, 2019(1): 5092078. https://doi.org/10.1155/2019/5092078
[48] Rojas-Galván, R., García-Martínez, J.R., Cruz-Miguel, E.E., Álvarez-Alvarado, J.M., Rodríguez-Resendiz, J. (2024). Performance comparison of bio-inspired algorithms for optimizing an ANN-based MPPT forecast for PV systems. Biomimetics, 9(10): 649. https://doi.org/10.3390/biomimetics9100649
[49] Motahhir, S., El Ghzizal, A., Sebti, S., Derouich, A. (2018). Modeling of photovoltaic system with modified incremental conductance algorithm for fast changes of irradiance. International Journal of Photoenergy, 2018(1): 3286479. https://doi.org/10.1155/2018/3286479
[50] Abdelhamid, H., Bahi, T. (2024). Performance analysis of maximum power point tracking for grid-photovoltaic system. Journal Européen des Systèmes Automatisés, 57(3): 689. https://doi.org/10.18280/jesa.570306
[51] Solano, J., Jimenez, D., Ilinca, A. (2020). A modular simulation testbed for energy management in AC/DC microgrids. Energies, 13(16): 4049. https://doi.org/10.3390/en13164049
[52] Subudhi, B., Pradhan, R. (2012). A comparative study on maximum power point tracking techniques for photovoltaic power systems. IEEE Transactions on Sustainable Energy, 4(1): 89-98. https://doi.org/10.1109/tste.2012.2202294
[53] De Brito, M.A.G., Galotto, L., Sampaio, L.P., e Melo, G.D.A., Canesin, C.A. (2012). Evaluation of the main MPPT techniques for photovoltaic applications. IEEE Transactions on Industrial Electronics, 60(3): 1156-1167. https://doi.org/10.1109/TIE.2012.2198036
[54] Ouahab, S.A.E., Bakkali, F., Amghar, A., Sahsah, H., Mentaly, L.E., Mahfoud, L.E. (2024). Design and implementation of temperature-parametric for maximum power point tracking of photovoltaic systems: Experimental validation using PI controller. Computers and Electrical Engineering, 120: 109707. https://doi.org/10.1016/j.compeleceng.2024.109707
[55] Nouh, A., Almalih, A.H., Faraj, M.A., Almalih, A., Mohamed, F. (2024). A hybrid of Meta-Heuristic techniques based on cuckoo search and particle swarm optimizations for solar PV systems subjected to partially shaded conditions. Journal of Solar Energy and Sustainable Development, 13(1): 114-132. https://www.ajol.info/index.php/jsesd/article/view/276749.
[56] Abdulaziz, S., Attlam, G., Zaki, G., Nabil, E. (2022). Cuckoo search algorithm and particle swarm optimization based maximum power point tracking techniques. Indonesian Journal of Electrical Engineering and Computer Science, 26(2): 605-616. https://doi.org/10.11591/ijeecs.v26.i2.pp605-616
[57] Paquianadin, V., Sam, K.N., Koperundevi, G. (2024). Maximizing solar photovoltaic system efficiency by multivariate linear regression based maximum power point tracking using machine learning. Electrical Engineering & Electromechanics, 1: 77-82. https://doi.org/10.20998/2074-272x.2024.1.10
[58] Ferdiansyah, I., Sutedjo, S., Qudsi, O.A., Ramadhan, A.N. (2019). Implementation of maximum power point tracking on solar panels using cuckoo search algorithm method. In 2019 2nd International Conference on Applied Information Technology and Innovation (ICAITI), Denpasar, Indonesia, pp. 88-92. https://doi.org/10.1109/icaiti48442.2019.8982163
[59] Li, J., An, Q., Lei, H., Deng, Q., Wang, G.-G. (2022). Survey of lévy flight-based metaheuristics for optimization. Mathematics, 10(15): 2785. https://doi.org/10.3390/math10152785
[60] Jasim, A.M., Abdulaal, A.H., Albaker, B.M., Alwan, M.S. (2024). High-gain cubic boost converter analysis with hybrid ANN-incremental conductance MPPT for solar PV systems. Mathematical Modelling of Engineering Problems, 11(12): 3379-3390. https://doi.org/10.18280/mmep.111216
[61] Danandeh, M.A., Mousavi G., S.M. (2018). A new architecture of INC-fuzzy hybrid method for tracking maximum power point in PV cells. Solar Energy, 171: 692-703. https://doi.org/10.1016/j.solener.2018.06.098
[62] Khosrojerdi, F., Taheri, S., Cretu, A.M. (2016). An adaptive neuro-fuzzy inference system-based MPPT controller for photovoltaic arrays. In 2016 IEEE Electrical Power and Energy Conference (EPEC), Ottawa, ON, Canada, pp. 1-6. https://doi.org/10.1109/EPEC.2016.7771794
[63] Bataineh, K., Eid, N. (2018). A hybrid maximum power point tracking method for photovoltaic systems for dynamic weather conditions. Resources, 7(4): 68. https://doi.org/10.3390/resources7040068
[64] Abdel-Qawee, I.M., Hamed, H.G., Nashed, M.N., Yousef, A.Y., Hasaneen, K.M. (2019). Comparative study of the most efficient maximum power point tracking techniques for a photovoltaic system. International Journal of Advances in Applied Sciences, 8(1): 73-82. http://doi.org/10.11591/ijaas.v8.i1.pp73-82
[65] Mehmood, F., Ashraf, N., Alvarez, L., Malik, T.N., Qureshi, H.K., Kamal, T. (2022). Grid integrated photovoltaic system with fuzzy based maximum power point tracking control along with harmonic elimination. Transactions on Emerging Telecommunications Technologies, 33(2): e3856. https://doi.org/10.1002/ett.3856