Hybridization of CSA and PSO improves the efficacy of MPPT for solar photovoltaic array with partial shading

Ali Falih Challoob; Nur Azzammudin Bin Rahmat; Vigna K. A/L Ramachandaramurthy; Amjad J. Humaidi

doi:10.1556/1848.2024.00751

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International Review of Applied Sciences and Engineering

Volume/Issue:: Volume 15: Issue 3

Hybridization of CSA and PSO improves the efficacy of MPPT for solar photovoltaic array with partial shading

Authors:

Ali Falih Challoob

Ali Falih Challoob Middle Technical University, Iraq

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Bcc0025@mtu.edu.iq

Nur Azzammudin Bin Rahmat

Nur Azzammudin Bin Rahmat Universiti Tenaga Nasional, Malaysia

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azzammudin@uniten.edu.my

Vigna K. A/L Ramachandaramurthy

Vigna K. A/L Ramachandaramurthy Universiti Tenaga Nasional, Malaysia

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Vigna@uniten.edu.my

, and

Amjad J. Humaidi

Amjad J. Humaidi University of Technology, Iraq

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amjad.j.humaidi@uotechnology.edu.iq https://orcid.org/0000-0002-9071-1329

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Pages:: 323–337

Online Publication Date:: 22 Apr 2024

Publication Date:: 25 Oct 2024

Article Category:: Research Article

DOI:: https://doi.org/10.1556/1848.2024.00751

Keywords:: partial shading; GMPP; cuckoo search; particle swarm optimization; photovoltaic; complex partial shading; hybrid CSA-PSO MPPT

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Abstract

One critical issue in the tracking systems based on photovoltaic (PV) is how to harvest highest power of the photovoltaic array; particularly when the system is operating in partially shaded conditions (PSCs) or varying irradiances. This study proposes particle swarm optimization (PSO) hybridization and cuckoo search algorithm (CSA) methods for maximum power point tracking (MPPT). The effectiveness of the proposed algorithm is validated and examined under various irradiance patterns. A comparison study in performance has been conducted between the proposed hybrid CSA-PSO method with the conventional P&O and PSO techniques. Several tests have been performed based on numerical simulations utilizing the programming software MATLAB/Simulink. The results demonstrated that the suggested hybrid technique yields smaller tracking time, higher power and greater efficiency than those of other traditional algorithms.

Abstract

1 Introduction

The photovoltaic (PV) technology has reached a higher level of maturity during the last two decades, and it is now being implemented on a vast scale as a safe and dependable source of energy [1]. Even though the individual components of PV systems have shown considerable cost reductions, there is still a pressing need to optimize their energy harvesting to improve competitiveness and efficiency [2]. These systems are required to include optimizing power point tracking (MPPT) controllers to get optimum MPP under various environmental conditions. However, achieving MPPT is still a difficult issue because the generated power by the PV array is highly influenced by the circumstances of the surrounding environment. This problem gets much more complicated when conditions of partial shading (PSCs) are present. In the case of PSCs, the PV modules are not getting uniform solar insolation during the operation. This may be caused by conditions such as trees, buildings, clouds, or even dust [3]. Photovoltaic system will be switched from the best operating mode if the control system is unable to recognize and respond to this circumstance. The characteristic of P–V array in PSC includes many peak spots because every module has a parallel arrangement of bypass diodes. In PSC, the traditional MPPT methods often follow local peaks rather than the point of maximum power globally (GMPP). Thus, it is essential to develop new MPPT methods to cope with problems of PSC [4]. In addition, the characteristics of the PV panel's power-voltage (P–V) will display multiple power maxima in the presence of PSCs. Utilizing bypass diodes protects PV cells that are shaded from the hot spot's damage phenomena due to these power maxima [5]. The PV hotspots come up when a cell or collection of cells functions in reverse bias, wasting energy rather than supplying it and resulting in functioning at high temperatures. The fundamental reason for fast aging and occasionally irreparable damage to the PV panels is the presence of PV hotspots. The most well-known MPPTs utilized in many PV applications include the traditional MPPT algorithms like perturb and observe (P & O), hill-climbing and incremental conductance (INC) [6–9]. The traditional MPPT methods are both simple and efficient at tracking the MPP in some circumstances of homogeneous insolation. These methods have numerous flaws, making it impossible to properly function in the case of PSCs. Conventional algorithms struggle to determine the GMPP in PSCs and even when they obtain the GMMPT, they may take a lot of time and have poor tracking precision and high oscillation. Moreover, under PSCs, the majority of popular MPPT methods do not distinguish between local and global MPPs (GMPPs and LMPPs). This occurs due to the fact that they concentrate on MPP which makes initial contact with almost always local MPP. Additionally, these traditional algorithms lack the ability to track MPPs, which causes power losses with rapidly varying irradiance. Therefore, to extract the best GMPP in PSCs, robust algorithms are required.

The tracking performance of MPP suffers degradation due to the weak capability of algorithms to cope with changes in power due to intentional disturbance and changes in irradiance. A number of academic researchers addressed this “drift” phenomenon and recommended changes to HC algorithms to enhance their tracking ability [10, 11]. As a result, optimal energy harvesting is impossible to obtain and there is a large energy loss which may reach up to 70%. Alternatively, several improvements have been made to traditional MPPT techniques in order to account for PSCs in the PV modules. Additionally, hotspots produced by partial shade have had disastrous electrical and thermal effects. These MPPT approaches may be characterized as either topology-based or algorithm-based, depending on how the advances were implemented. In order to obtain GMPP, new topologies may be proposed and more electrical circuits are needed [12, 13]. This results in a decrease of the overall system efficiency as well as in a rise in the overall cost. Despite the fact that MPPT techniques based on algorithms, including fuzzy logic with optimization algorithms, neural network technique, and sequential extremum seeking (SES) method, need extensive computational and greater complexity for the practical stage, these methods are still widely used [14, 15]. As a result of their efficacy in treatment difficult issues for the nonlinearity of PV characteristics, optimization algorithms that are inspired by natural phenomena have recently gained a lot of interest and popularity [16].

In addition, the simplicity of execution makes them suitable for resolving the MPPT problem in PV systems which is exposed by PSCs [17]. However, the development of MPPT controllers has also made use of optimization techniques that are based on swarm intelligence (SI) [18, 19], like ant colony optimization (ACO) and particle swarm optimization (PSO) [20, 21]. Despite the fact that these techniques offer significant benefits, such as lower computation work and autonomy from the parameters of the system, they lack flexibility. Additionally, the beginning location of the agents (individuals or solutions) has a significant impact on how they converge. The artificial bee colony (ABC) and cuckoo search algorithm (CSA) belong to the SI family [22, 23]. These approaches were employed in numerous research studies in order to treat partial shading difficulties in PV applications. Their results reveal that both algorithms are simple and versatile since they only need two design variables and their GMPP convergences are independent of each other on the beginning circumstances of the search strategy. Regarding comparison with the PSO method, the MPPT technique based on ABC has a significant disadvantage, represented by sluggish convergence to GMPP under variable solar irradiation and power loss due to oscillation of operating point around MPP. Hybridization of these algorithms, such as genetic algorithm (GA)-based fuzzy logic (FL) (GA-FL), Perturb and Observe Grey Wolf Optimization (GWO-P&O) [24, 25], PSO-based neural network controller (PSO-NN) and PSO-based FL increase the efficacy of MPPT algorithm in the presence of PSCs in PV plants [26, 27]. Even though these hybrid approaches provide better results than the original method, their design is complicated and time-consuming due to the complexity of the developed algorithms. CSA-based FL was proposed to improve the performance of CSA under PSCs [28]. The study found that the CSA-based FL technique is able to overcome the drawbacks that have been addressed in the conventional techniques, such as slow convergence, occurrence of oscillations, and poor tracking performance. Hybridization strategies have been used which mixed grey wolf optimization (GWO-PSO) and tunicate swarm algorithm (TSA-PSO), respectively [29, 30]. It has been shown that the hybrid CSA-PSO approach performed better characteristics than well-known SI-based algorithms in terms of their ease of calculation and independence from beginning conditions, high accuracy of solution, and high efficiency in managing local minima. By 2050, around 85% of the energy generated will come from renewable sources, as anticipated by the International Renewable Energy Agency (IREA) [31].

In the literature, many researches have been pursued in order to solve the partial shading issue in order to enhance photovoltaic systems while keeping the stability of maximum power at different pattern irradiance [20–23]. However, utilization of combining two optimization techniques is to have high efficiency in power with short tracking time. In this study, a novel hybrid optimization method has been presented based on fusing both CSA and PSO algorithm for MPPT of PV system.

The CSA optimization approach, introduced in 2010, is inspired by the movement of cuckoo birds and it is widely used in many engineering applications. Using very few control parameters is the most important advantage of the CSA and it is also characterized by balanced mixing and efficient random walk of Lévy flights [32]. On the other hand, the PSO is a well-known and very efficient global search algorithm. It is used for solving many complex optimization problems due to ease of implementation, fast convergence, and its ability to work as parallel algorithm, which can organize the parts of programming steps to work concurrently in order to reduce the time of solution and increase the performance [33, 34]. The comparison of the ANN-based ARV approach with traditional techniques, P&O, and INC in [35] was carried out using PI-clamping and traditional PI controllers in four separate simulated studies. The cuckoo search algorithm (CSA) and voltage scanning combining (VSC) techniques were suggested to create a novel hybrid MPPT algorithm to get most maximum possible power out of a photovoltaic system under PSCs [36]. The hybridization of both CSA and PSO will utilize the benefits of both techniques and result in robust and efficient optimization method.

Furthermore, the proposed MPPT method results in PV systems that can operate in circumstances of both uniform and irregular lighting irradiance. The main objectives of the suggested hybrid approach are to get the following points:

Superior tracking efficiency together with a high accuracy.
Convergence to GMPP that is unaffected by the start condition of the lookup procedure.
Minimum power losses with high extracted energy from the PV array.

The contribution addressed by this study can be highlighted by the following points:

Development of a hybrid algorithm to improve the performance of MPPT based on CSA-PSO. This proposed technique has more advantages like accurate maximum power, smaller tracking time, high efficiency and high power when the PV system is under partial shadow.
Conducting a study that compares the proposed methods, P&O and PSO. Also, this suggested method is more desirable and suitable for current trends since it is used under changing irradiance and gives high power with low fluctuation and low tracking speed.
Obtaining robust and transient characteristics of PV systems under circumstances of partial shading.

2 Effect of partial shading conditions (PSCs)

The solution of the PSC effect on the PV cells or modules is one of the key contributions to the power losses that occur in solar PV systems. An uneven distribution of sunlight over a PV cell will lead particular cells to produce less power and degeneration, despite the fact that certain cells have the capacity to generate more energy than others. Figure 1 shows the PV array, where some PV cells undergo PSCs, while the others are exposed to normal irradiance (normal condition) with blocking and bypass diodes. The consequence of this circumstance is the several local peaks occurring at once in the P–V curve's (LMPP). In this case, the photovoltaic panels do not produce sufficient power and this leads to the effect of hot spots and hence to the damage of the entire photovoltaic panel. The photovoltaic panel is connected in parallel to address this issue with a by-pass diode as indicated in Fig. 1 to protect the panel from the shading effect while the purpose of the blocking diode is to block the reverse current to the PV panel. On the other hand, the effect of PSC can be reflected by several peaks of power in the PV curve as seen in Fig. 2. The power output of partially shaded panels is generally reduced, which is an undesired consequence. Using traditional MPPT algorithm may not be able to locate a global peak and a local peak may be stuck instead [37, 38]. The task is how to improve the MPPT algorithm such as to find the peak power.

Fig. 1.

A PV array structure panels SP connected under PSCs

Citation: International Review of Applied Sciences and Engineering 15, 3; 10.1556/1848.2024.00751

Fig. 2.

PV array characteristics under identical and PSCs operations

Citation: International Review of Applied Sciences and Engineering 15, 3; 10.1556/1848.2024.00751

In this study, the used PV array is made of four KC200GT PV modules linked in an SP configuration. Also in this work, focus is on the four partially shading patterns scenarios to validate the proposed method for getting maximum power from PV array under these conditions. The PV array consists of four bypass diodes with two blocking diodes. This work used a 5 × 4 PV array configuration as shown in Fig. 3 which provides 4,000 W at uniform conditions of irradiation G = 1,000 W m⁻² and ambient temperature T = 25 °C. Table 1 lists the electrical PV characteristics. The PV array was simulated under MATLAB/Simulink to study the performance under different PSCs patterns.

Fig. 3.

Proposed PV array configuration

Citation: International Review of Applied Sciences and Engineering 15, 3; 10.1556/1848.2024.00751

Table 1.

Technical parameters of KC200GT PV module

Description	Value
Maximum power $P_{M P}$	$200 W$
Maximum voltage $V_{M P}$ at $P_{M P}$	$26.3 V$
Maximum current $I_{M P}$ at $P_{M P}$	$7.61 A$
Open-circuit voltage $V_{O C}$	$32.9 V$
Short-circuit current $I_{S C}$	$8.21 A$
Temperature coefficient at $I_{S C}$	$3.18 \times 10^{- 3} A / ℃$
Temperature coefficient at $V_{O C}$	$- 1.23 \times 10^{- 1} V / ℃$
Panel's cells, $N_{S}$	$54$

3 Proposed CSA-PSO under PSCs

Figure 4 displays the proposed study of solar power system based on the suggested technique CSA-PSO MPPT. The proposed PV system consists of four MSX-200 W PV modules, a boost DC/DC converter, the CSA-PSO MPPT of control algorithm, and a resistive output load for the four patterns (irradiance profile with varying solar irradiation).

Fig. 4.

The entire PV system with proposed CSA-PSO MPPT method

Citation: International Review of Applied Sciences and Engineering 15, 3; 10.1556/1848.2024.00751

4 Description of photovoltaic system PSCs

To convert the radiation coming from the sun into electricity, the structure of the photovoltaic module has to be considered. The photovoltaic module is designed either connected in series or parallel in order to provide the needed level of energy and to achieve desired output quantities like power, current and output voltage [39, 40]. The features of the generated output power by the array of photovoltaic cells are based on the voltage that it produces. The voltage output of the photovoltaic array is controlled by a DC-DC converter. Maintaining a constant DC-DC converter output is done by adjusting the voltage to DC–DC pulse width modulation in a connection. For the purpose of tracking the optimal power of a PV panel, the gate of a DC-DC converter is switched using a PWM signal [41]. The proposed controller (CSA-PSO) receives inputs voltage and current resulting in an optimal actuation ratio corresponding to the PV module that is able to generate the highest amount of power.

4.1 Modelling of photovoltaic system array

The PV model is an important component in MPPT. The model of an electric circuit of photovoltaic model is described in Fig. 5. The characteristic of the current-voltage (I–V) curve is generated based on PV model. In order to model the incident solar irradiance or describe I–V characteristics, the solar cell has to be first represented by the following equations [42, 43].

I = I_{p h} - I_{D} - I_{s h}

(1)

I = I_{s c r} - I_{o} [\exp (\frac{V + I R_{s}}{V_{t} A}) - 1] - \frac{V + I R_{s}}{R_{s h}}

(2)

Fig. 5.

Equivalent circuit of Solar PV cell

Citation: International Review of Applied Sciences and Engineering 15, 3; 10.1556/1848.2024.00751

The thermal voltage of cell is

V_{t} = \frac{N_{s} a K T}{q}

(3)

where

I_{p h}

denotes the photovoltaic panel current,

I_{o}

is the diode or the diode's leakage current, q is the electron charge, V is the voltage of single PV panel's output, K is the Constant Boltzmann (1.38 ×

10^{- 23}

J/K), T is the temperature in K, α a denotes ideality factor of the diode (1< α < 1.6),

N_{s}

is the number of cell in series,

R_{s}

is the equivalent resistance of series array, and

R_{s h}

represents equivalent array parallel resistance.

4.2 Modelling of boost converter

Figure 6 displays the boost converter's electrical schematic diagram of the proposed MPPT technique. PV output terminal and the output load are linked to the boost converter and it is employed to modify the input equivalent resistance. The controller MPPT has to regulate the switching duty cycle in order to make the necessary adjustments [44]. According to this concept, the unbalance between the PV system and the load will be fixed, which permits the PV panel to operate as efficiently as possible. Also, there are two types of converter: isolated and non-isolated converter [45].

Fig. 6.

DC/DC Boost converter circuit of proposed system

Citation: International Review of Applied Sciences and Engineering 15, 3; 10.1556/1848.2024.00751

To create the boost converter design, the following equations are used to extract the required values of inductance L, output capacitance $C_{o}$ , input capacitance $C_{i n}$ in the mode of continuous conduction [46–48].

The inductance of the boost conductor is described by:

L = \frac{V_{i n} D}{f_{s} ∆ I_{i n}}

(4)

where,

V_{i n}

is the input voltage of boost converter, D is duty ratio of boost converter,

f_{s}

is the switching frequency,

{∆ I}_{i n}

is the ripple of inductor current which is defined by

{∆ I}_{i n}

= K

I_{i n}

, where K takes the values in the range [0.2–0.4].

The following expression can be used to determine the output capacitance value

C_{o} = \frac{I_{o (\max)} D}{f_{s w} ∆ V_{o}}

(5)

where,

V_{o}

represents voltage at terminal output, the output voltage ripple is defined as

∆ V_{o} = 0.01 V_{o}

. The input capacitor can be expressed as follows

C_{i n} \geq \frac{d}{{8 \times f}_{s}^{2} \times L \times 0.01}

(6)

5 Mathematical modelling of PSO and CSA algorithms

Figure 7 presents the control block diagram of proposed hybrid CSA-PSO based MPPT method that has been described in this work. In the PSO algorithm, the best particle, $P_{b e s t}$ (the best solution) is obtained by overall population particles. The PSO algorithm controls several cooperating particles to search the full search space using swarm intelligence. Each particle's distinct position, $x_{i}$ and velocity, $Ф_{i}$ could be represented as a potential solution. A particle's position is influenced during the search process by its best location inside a neighborhood, $P_{b e s t, i}$ .

Fig. 7.

The Proposed flowchart CSA-PSO based MPPT

Citation: International Review of Applied Sciences and Engineering 15, 3; 10.1556/1848.2024.00751

Best location of all particles across the entire population,

G_{b e s t}

. As a result, both the particle's location and the velocity can be written in the following equations [49–51].

x_{i}^{k + 1} = x_{i}^{k} + Ф_{i}^{k + 1}

(7)

{Ф_{i}^{k + 1} = w Ф}_{i}^{k} + c_{1} r_{1} {P_{b e s t} - x_{i}^{k}} + c_{2} r_{2} {G_{b e s t} - x_{i}^{k}}

(8)

where, w is the weight of the inertia,

c_{1}

c_{2}

are the coefficients acceleration,

r_{1}

r_{2}

are uniformly distributed random variables belonging to range (0,1).

The position in the PSO algorithm depicts the actual duty cycle and the rate change of the duty cycle represents the velocity variable in the PSO algorithm. The duty cycle

d_{i}^{k + 1}

equation in the PSO optimization is updated according to the following equation:

d_{i}^{k + 1} = d_{i}^{k} + Ф_{i}^{k + 1}

(9)

The Cuckoo search (CS) algorithm was introduced in 2009 based on the search behaviors of Cuckoos birds. The CS algorithm uses random steps based on Lévy flight to make a long and random jump in its search [52]. The cuckoos are members of a family of birds with a unique reproductive technique and they are more aggressive than other species of birds. The CSA is one of the fast converging algorithms and it is based on Lévy flight and cuckoo search behavior as follows:

Lévy flight: A Lévy flight is a random walk with a steady distribution of step-lengths, a heavy-tailed probability distribution probability distribution [53].
Cuckoo search (CS): The nature of cuckoos inspires the choice of the best solution. The egg of cuckoos is considered the new best solution, while the solutions represent the eggs already in the nest. Three nature rules of cuckoos can synthesize this optimization [54]:
- Every cuckoo lays one egg in turn, in a nest selected at random.
- The eggs in that nest in the best possible quality will give rise to the following generations.
- A host bird will almost certainly find the cuckoo's egg since there is a fixed number of accessible host residences (nests). In order to improve this algorithm, one may assume that the produced eggs (solutions) are of the maximum quality achievable; this will be transmitted to future generations. The quantity of host nests that can be accessed to the maximum is predetermined, and the probability of finding the nests to lay egg lies within [0 1]. In this case, the host bird has two options: either toss the egg out of the nest or leave the nest and construct a new nest somewhere else. The Lévy flight is described mathematically using:

x_{i}^{(t + 1)} = x_{i}^{(t)} + α \oplus L e v y (λ)

(10)

where,

x_{i}^{(t + 1)}

is the new solutions for cuckoo, α is related to the step size of optimization problems and takes the values within (0,1),

\oplus

represents the entry-wise multiplications, just arbitrary walks are the Lévy flights, the length step obeys the distribution of probabilities and these steps are expressed in terms of step length. The step length in Lévy distribution is described according to:

L e v y (β) \sim u = L^{- β},

(11)

where, L is the step size length, β is constant (1< β < 3).

6 Results and discussion

The suggested MPPT is verified using the MATLAB/Simulink programming tool under normal and four patterns of irradiance with partial shading conditions PSCs. The Simulink modelling of proposed PV scheme which included 4 kW PV array, DC/DC boost converter, MPPT method block of suggested CSA-PSO algorithm and electric vehicle as load that represents the resistance as illustrated in Fig. 8. Table 2 shows the obtained results of the suggested PV array under the fourth PSC patterns.

Fig. 8.

MATLAB/Simulink Modelling of proposed PV system

Citation: International Review of Applied Sciences and Engineering 15, 3; 10.1556/1848.2024.00751

Table 2.

Extracted power from PV array under PSCs

Pattern No.	$P_{G M P P}$ ( $W$ )	$P_{L M P P 1}$ ( $W$ )	$P_{L M P P 2}$ ( $W$ )	$P_{L M P P 3}$ ( $W$ )
Irradiation Pattern 1	4,000	–	–	–
Irradiation Pattern 2	3,034	2,352	–	–
Irradiation Pattern 3	2,396	2,276	1,523	–
Irradiation Pattern 4	1,730	1,730	680	472

The parameters of proposed CSA and PSO algorithms are listed in Table 3 and Table 4, respectively.

Table 3.

Parameters of cuckoo search algorithm (CSA)

Parameter.	Value
Beta of step size length (β)	$1.5$
Step size of optimization problem (α)	Range $(0, 1)$
Number of population	20

Table 4.

Parameters of particle swarm optimization (PSO)

Parameter.	Value
Coefficients acceleration, $r_{1}$ , $r_{2}$	Range (0,1)
Uniformly distributed random variables ( $c_{1}$ , $c_{2})$	1.2
Step size of optimization problem (α)	Range (0,1)
Population size	20
Max no of iteration	200
Weight of the inertia (w)	0.1
Function Tolerance	$1 \times 10^{- 10}$

A comparison study has been conducted among the suggested CSA-PSO-based MPPT algorithm and that based on P&O, and PSO, and several tests and scenarios are presented, which address the situations of normal and shading conditions. Further, a comparison is presented between the suggested CSA-PSO approach and the traditional approaches.

Case I: Partial shading effect on PV array

Using a PV array, the proposed MPPT is tested and the precision and overall performance are evaluated. In this sense the following elements are utilized: a DC–DC boost converter, an MPPT algorithm, and array of 4,000 W PV panels. The switching frequency $f_{s}$ is 5 KHz and its effect on duty cycle leads to affect on pulse width modulation of boost converter, therefore it will impact the switch controller of boost converter through a specific on and off period of time. Also, when we increase sampling time there are no changes in the results so we identify the sufficient sampling simulation time as 1e-6 s. Four complex patterns on PV array under various partial shading circumstances are illustrated in Fig. 9. All these patterns perform on the system with changing amount of irradiance for different time periods to see the capability of the proposed MPPT to obtain high power from PV array under four pattern scenarios.

Fig. 9.

Different patterns applied on the suggested PV system.

Citation: International Review of Applied Sciences and Engineering 15, 3; 10.1556/1848.2024.00751

Accordingly, the P–V&I–V PV panel curves are displayed in Fig. 10 under these variable conditions of solar irradiance. As seen in these figures, the multi points and unique point for each curve are observed. The PV array presents 1 peak when the irradiation is uniform at 1,000 W/ $m^{2}$ and the PV system has a 4,000 W output power. In the second irradiance pattern, there are two peaks on the P–V & I–V curves, which happened as a result of PSCs on the most recent rows of configuration (700 W/ $m^{2}$ ). Here, the power generated is decreased (3,034 W).

Fig. 10.

P–V & I–V curves of the proposed PV configuration under PSCs

Citation: International Review of Applied Sciences and Engineering 15, 3; 10.1556/1848.2024.00751

The PSCs at the third pattern have considerable effect on the Photovoltaic array such as the power has decreased from 4,000 to 2,396 W with three peaks, represented by one GMPP, and two LMPPs. Finally, four peaks occurred on the PV curves when the amount of generation was decreased to 1,730 W at GMPP.

Figure 11 presents the four irradiation patterns for the proposed CSA-PSO, PSO, and P&O, respectively. As illustrated in Fig. 11, the proposed approach has a large capacity to control the MPP without any fluctuations or delay time when the circumstances include partly darkened conditions and constant temperature.

Fig. 11.

Different step change in irradiance profile

Citation: International Review of Applied Sciences and Engineering 15, 3; 10.1556/1848.2024.00751

The obtained power of the solar array under CSA-PSO, PSO, and P&O based on the forth above irradiances that are mentioned in Fig. 11 are displayed in Fig. 12. Figure (12-a) demonstrates that the PV power at MPP is equal to 4,000 W. It is clear that the proposed methods stabilize and reach their maximum value in the shortest amount of time. Also in Fig. (12-d) after 1.2 s of time, the power becomes stabilize and smooth and there are no longer any fluctuations as compared to other traditional techniques. This indicates that the control algorithm based on hybrid MPPT will govern the duty cycle according to the varying weather conditions. On the other hand, the results due to PSO and P&O-based MPPT are shown in Fig. (12-b and 12-c). It is obvious from the figure that the PSO-based MPPT gives greater initial power as compared to that delivered by the conventional P&O, which provides less power at step change in irradiation. Although the PSO method offers a good output power, it shows high oscillations as indicated in Fig. (12-d). Accordingly, the considered method presents good accuracy and high power at MPP. As such, the proposed MPPT offers good results and this makes the CSA-PSO algorithm more efficient than the conventional algorithms. Moreover the data obtained demonstrate that the CSA-PSO method achieved small tracking time (0.018 s) with high MPP (3,500 W) and high tracking efficiency (97.72%). Also the computational efficiency is an important factor and can be calculated from the maximum power of the PV cell and the average power that the MPPT extracted from the array and we notice that the proposed method (CSA-PSO) gives high power for four patterns. In addition, the applied MPPT shows less oscillation in PV power as compared to higher power oscillations shown by other techniques.

Fig. 12.

Obtained results under complex PSCs (a) irradiance pattern 1 (b) irradiance pattern 2 (c) irradiance pattern 3 (d) irradiance pattern 4

Citation: International Review of Applied Sciences and Engineering 15, 3; 10.1556/1848.2024.00751

Moreover, Fig. (12-d) shows that the CSA-PSO method could achieve MPP of 3,500 W, whereas the conventional techniques provide this value of power for MPP.

Figure 13 displays the results of voltage and current at all cases under the supervision of the CSA-PSO method and other conventional techniques. As can be observed, the proposed MPP technique performs with little oscillation of cases 13 a, 13-b, 13-c and this gives good behavior of this technique as compared to traditional techniques.

Fig. 13.

Voltage and current of PV under complex PSCs (a) irradiance pattern 1 (b) irradiance pattern 2 (c) irradiance pattern 3 (d) irradiance pattern 4

Citation: International Review of Applied Sciences and Engineering 15, 3; 10.1556/1848.2024.00751

Figure 14 illustrates the behavior of the powers and duty cycles for four patterns of partial shading. As indicated in the figure, the PV system gives better power for the first and fourth patterns, while the power will decrease for the second and third patterns. In these suggested patterns, the algorithm of the suggested CSA-PSO approach has been applied to adjust the duty cycle switching of the boost converter. When comparing the results obtained from the proposed CSA-PSO with other techniques like VSC-CSA and ANN-based ARV in the literature mentioned above, we notice that the tracking MPPT efficiency is approximately the same and in some scenarios the suggested method is superior to other techniques and an important key of comparison is tracking time so CSA-PSO required very short tracking time (0.0018 s) to reach MPPT as compared with other techniques. Also we notice in the results demonstrated that the VSC-CSA methods have more fluctuations and need more time to reach maximum power under PSCs.

Fig. 14.

Power curves of the PV system and duty cycle of boost converter with (a) irradiance pattern 1 (b) irradiance pattern 2 (c) irradiance pattern 3 (d) irradiance pattern 4

Citation: International Review of Applied Sciences and Engineering 15, 3; 10.1556/1848.2024.00751

In summary, the hybrid CSA-PSO algorithm was able to recognize the partially shaded areas and track the GMPP smoothly and steadily. In fact, traditional algorithms are incapable of coping with shading circumstances. Both conventional algorithms displayed unpredictable and odd behavior with shading variations. In addition, the traditional algorithms exhibited undesirable transient behavior with a great deal of oscillation. Also while comparing the proposed hybrid MPPT with other MPPT techniques we noticed that it has more scalability, generalizability and more benefit analysis due to carrying more features related to high efficiency, small tracking speed and high power. Table 5 reports the performances of the proposed technique and other traditional techniques in terms of efficiency, speed of tracking and settling time below various types of shading. One can conclude that the suggested technique outperforms its counterparts.

Table 5.

Comparison between the CSA-PSO, PSO and P&O based MPPT methods under different shading patterns

Pattern No.	Tracking algorithm	Mean power (W) ( $P_{A v g, D C}$ )	Tracking speed (s)	Optimal power (W)	MPPT efficiency (%)	State settling time (s)
1	P&O	2,454.5	0.002	4,000	61.3	1.9
	PSO	3,827	0.0019	4,000	95.67	1.92
	CSA-PSO	3,890	0.0018	4,000	97.25	1.85
2	P&O	1,479	0.4	1,600	92.43	1.8
	PSO	1,343	0.01	1,600	83.93	1.75
	CSA-PSO	1,546	0.008	1,600	96.62	1.35
3	P&O	2,250	0.065	2,396	93.9	1.4
	PSO	2,012.5	0.0023	2,396	83.99	1.3
	CSA-PSO	2,341.5	0.0022	2,396	97.72	1.25
4	P&O	2,052	0.005	2,200	93.29	1.9
	PSO	1,781	0.002	2,200	80.9	1.88
	CSA-PSO	2,094	0.0017	2,200	95.18	1.85

Recommendations:

One may suggest the following recommendation to improve the proposed study:

Using other modern algorithms of MPPT techniques like Whale Optimization Algorithm (WOA), Grey-Wolf Optimization (GWO), or hybrid optimization based on GWO and PSO to enhance the performance of MPPT [55–58].
Artificial Intelligence based on conventional Artificial Neural Network (ANN), or Deep Learning Models can be applied to improve the capabilities of MPPT [59–61].
Other modified optimization techniques like Variable Step Perturb and Observe (VSP&O) can be utilized to get high maximum power under partial shading effect of PV System.
Control schemes can be designed to improve the robustness characteristics of boost DC/DC converter to have stabilized DC bus voltage, and consequently leading to stable PV performance against variable shading in photovoltaic cells [62–70].
Real-time of proposed study may take a long time and it is difficult to implement currently. However, the plan is to use either Embedded System Design based on FPGA, Raspberry Pi microcontroller, or Lab-View-Based implementations to realize the proposed MPPT algorithm. All these points have been mentioned in the recommendations [71–77].

7 Conclusion

This research provides a new GMPPT control algorithm based on hybrid CSA-PSO. The main objective of this study is to obtain fast achieving of global MPPT for PV system at both constant and irregular irradiance levels. The proposed approach hybridizes both PSO and CSA to control boost converter based on MPPT algorithms. The efficacy of the algorithm is verified and examined in various irradiance patterns based on numerical simulation. As compared to conventional PSO and P&O, the proposed hybrid optimization techniques based on PSO and CSA show better performance in terms of efficiency, accuracy and tracking time. Numerically speaking, the proposed technique showed a smaller tracking time (0.018 s), higher power (3,500 W) and higher efficiency (97.72 %) as compared to other traditional methods. Accordingly, one can conclude that the proposed hybrid CSA-PSO method could give better performance characteristics under changing shading conditions and it is strongly recommended for PV systems. As future extension of this study, one can use other modern optimization techniques found in the literature either for improving the performance of PV system or to conduct a comparison study to the proposed hybrid method. There are many emerging optimization methods like Grey-Wolf Optimization, Butterfly optimization, Spotted Hyena Algorithm, Genetic Algorithm, etc. Also, one can suggest the implementation of the proposed control algorithm in real-time environment. Moreover, investigation of other convertor topologies and different sizes of PV will be the next scope of this study.

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A. J. Humaidi, S. Hasan, and A. A. Al-Jodah, “Design of second order sliding mode for glucose regulation systems with disturbance,” Int. J. Eng. Technology (UAE), vol. 7, no. 2, pp. 243–247, 2018. https://doi.org/10.14419/ijet.v7i2.28.12936.
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International Review of Applied Sciences and Engineering

Print ISSN:: 2062-0810

Online ISSN:: 2063-4269

Editorial Board

Mohammad Nazir AHMAD, Institute of Visual Informatics, Universiti Kebangsaan Malaysia, Malaysia

Murat BAKIROV, Center for Materials and Lifetime Management Ltd., Moscow, Russia

Nicolae BALC, Technical University of Cluj-Napoca, Cluj-Napoca, Romania

Umberto BERARDI, Toronto Metropolitan University, Toronto, Canada

Ildikó BODNÁR, University of Debrecen, Debrecen, Hungary

Sándor BODZÁS, University of Debrecen, Debrecen, Hungary

Fatih Mehmet BOTSALI, Selçuk University, Konya, Turkey

Samuel BRUNNER, Empa Swiss Federal Laboratories for Materials Science and Technology, Dübendorf, Switzerland

István BUDAI, University of Debrecen, Debrecen, Hungary

Constantin BUNGAU, University of Oradea, Oradea, Romania

Shanshan CAI, Huazhong University of Science and Technology, Wuhan, China

Michele De CARLI, University of Padua, Padua, Italy

Robert CERNY, Czech Technical University in Prague, Prague, Czech Republic

Erdem CUCE, Recep Tayyip Erdogan University, Rize, Turkey

György CSOMÓS, University of Debrecen, Debrecen, Hungary

Tamás CSOKNYAI, Budapest University of Technology and Economics, Budapest, Hungary

Anna FORMICA, IASI National Research Council, Rome, Italy

Alexandru GACSADI, University of Oradea, Oradea, Romania

Eugen Ioan GERGELY, University of Oradea, Oradea, Romania

Janez GRUM, University of Ljubljana, Ljubljana, Slovenia

Géza HUSI, University of Debrecen, Debrecen, Hungary

Ghaleb A. HUSSEINI, American University of Sharjah, Sharjah, United Arab Emirates

Nikolay IVANOV, Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia

Antal JÁRAI, Eötvös Loránd University, Budapest, Hungary

Gudni JÓHANNESSON, The National Energy Authority of Iceland, Reykjavik, Iceland

László KAJTÁR, Budapest University of Technology and Economics, Budapest, Hungary

Ferenc KALMÁR, University of Debrecen, Debrecen, Hungary

Tünde KALMÁR, University of Debrecen, Debrecen, Hungary

Milos KALOUSEK, Brno University of Technology, Brno, Czech Republik

Jan KOCI, Czech Technical University in Prague, Prague, Czech Republic

Vaclav KOCI, Czech Technical University in Prague, Prague, Czech Republic

Imre KOCSIS, University of Debrecen, Debrecen, Hungary

Imre KOVÁCS, University of Debrecen, Debrecen, Hungary

Angela Daniela LA ROSA, Norwegian University of Science and Technology, Trondheim, Norway

Éva LOVRA, Univeqrsity of Debrecen, Debrecen, Hungary

Elena LUCCHI, Eurac Research, Institute for Renewable Energy, Bolzano, Italy

Tamás MANKOVITS, University of Debrecen, Debrecen, Hungary

Igor MEDVED, Slovak Technical University in Bratislava, Bratislava, Slovakia

Ligia MOGA, Technical University of Cluj-Napoca, Cluj-Napoca, Romania

Marco MOLINARI, Royal Institute of Technology, Stockholm, Sweden

Henrieta MORAVCIKOVA, Slovak Academy of Sciences, Bratislava, Slovakia

Phalguni MUKHOPHADYAYA, University of Victoria, Victoria, Canada

Balázs NAGY, Budapest University of Technology and Economics, Budapest, Hungary

Husam S. NAJM, Rutgers University, New Brunswick, USA

Jozsef NYERS, Subotica Tech College of Applied Sciences, Subotica, Serbia

Bjarne W. OLESEN, Technical University of Denmark, Lyngby, Denmark

Stefan ONIGA, North University of Baia Mare, Baia Mare, Romania

Joaquim Norberto PIRES, Universidade de Coimbra, Coimbra, Portugal

László POKORÁDI, Óbuda University, Budapest, Hungary

Roman RABENSEIFER, Slovak University of Technology in Bratislava, Bratislava, Slovak Republik

Mohammad H. A. SALAH, Hashemite University, Zarqua, Jordan

Dietrich SCHMIDT, Fraunhofer Institute for Wind Energy and Energy System Technology IWES, Kassel, Germany

Lorand SZABÓ, Technical University of Cluj-Napoca, Cluj-Napoca, Romania

Csaba SZÁSZ, Technical University of Cluj-Napoca, Cluj-Napoca, Romania

Ioan SZÁVA, Transylvania University of Brasov, Brasov, Romania

Péter SZEMES, University of Debrecen, Debrecen, Hungary

Edit SZŰCS, University of Debrecen, Debrecen, Hungary

Radu TARCA, University of Oradea, Oradea, Romania

Zsolt TIBA, University of Debrecen, Debrecen, Hungary

László TÓTH, University of Debrecen, Debrecen, Hungary

László TÖRÖK, University of Debrecen, Debrecen, Hungary

Anton TRNIK, Constantine the Philosopher University in Nitra, Nitra, Slovakia

Ibrahim UZMAY, Erciyes University, Kayseri, Turkey

Andrea VALLATI, Sapienza University, Rome, Italy

Tibor VESSELÉNYI, University of Oradea, Oradea, Romania

Nalinaksh S. VYAS, Indian Institute of Technology, Kanpur, India

Deborah WHITE, The University of Adelaide, Adelaide, Australia

International Review of Applied Sciences and Engineering
Address of the institute: Faculty of Engineering, University of Debrecen
H-4028 Debrecen, Ótemető u. 2-4. Hungary
Email: irase@eng.unideb.hu

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2023
Scimago
Scimago H-index	11
Scimago Journal Rank	0.249
Scimago Quartile Score	Architecture (Q2) Engineering (miscellaneous) (Q3) Environmental Engineering (Q3) Information Systems (Q4) Management Science and Operations Research (Q4) Materials Science (miscellaneous) (Q3)
Scopus
Scopus Cite Score	2.3
Scopus CIte Score Rank	Architecture (Q1) General Engineering (Q2) Materials Science (miscellaneous) (Q3) Environmental Engineering (Q3) Management Science and Operations Research (Q3) Information Systems (Q3)
Scopus SNIP	0.751

International Review of Applied Sciences and Engineering
Publication Model	Gold Open Access Online only
Submission Fee	none
Article Processing Charge	1100 EUR/article
Regional discounts on country of the funding agency	World Bank Lower-middle-income economies: 50% World Bank Low-income economies: 100%
Further Discounts	Limited number of full waivers available. Editorial Board / Advisory Board members: 50% Corresponding authors, affiliated to an EISZ member institution subscribing to the journal package of Akadémiai Kiadó: 100%
Subscription Information	Gold Open Access

International Review of Applied Sciences and Engineering
Language	English
Size	A4
Year of Foundation	2010
Volumes per Year	1
Issues per Year	3
Founder	Debreceni Egyetem
Founder's Address	H-4032 Debrecen, Hungary Egyetem tér 1
Publisher	Akadémiai Kiadó
Publisher's Address	H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Responsible Publisher	Chief Executive Officer, Akadémiai Kiadó
ISSN	2062-0810 (Print)
ISSN	2063-4269 (Online)