Improved particle swarm optimization based on buck-boost converter IPSO-BBC for photovoltaic system
* Author to whom correspondence should be addressed.
Recent Advances in Science and Engineering 2022, Vol. 2, Issue 2, pp. 42-48; doi.org/10.14744/rase.2022.0007
Abstract
Keywords: Buck-Boost converter; efficiency; particle swarm optimization; photovoltaics
Introduction
In recent decades, the terrestrial globe is influenced by the effects of climate change issues caused by some natural factors. An example of fossil fuel [1]. The high increase in raw oil and fossil fuels are some of the main issues related to the environment’s adverse outwardness. That is primarily understood through disruptions produced by climatical inconsistency involving global warming, depletion of ozone layers, carbon emissions rise, high wind, high temperature, and other unknown disasters. On the other hand, The PV solar system is expanding rapidly across the world, where-
as the solar power flexibility delays its infiltration into the power grid [2]. Besides as the world’s energy segment moves away from fossil fuels toward renewable energy sources. Therefore, computational optimization techniques can be used to drastically increase the economics and decrease the environmental impact of the energy technologies by using the carbon dioxide capture and storage (CCS) method. The CCS system accounts for the irregularity and inconsistency of renewable power production resulting in reduced cost of capture compared to non-optimized methods. The optimization has been applied to many technologies. In the work of [3], the renewable energy source incorporations are
*Corresponding author. *E-mail address: bakin@yildiz.edu.tr Published by Yıldız Technical University, İstanbul, Türkiye This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Recent Adv Sci Eng, Vol. 2, No. 2, pp. 42-48, December, 2022
designated as the electric power production (EPP) mitigation methodology.[4] defines the tabu search optimization (TSO) technique for removing the parasitic parameters of PV solar modules under various climate change conditions. [5] Proposed a multi-adaptive PSO technique in order to find the global peak of PV modules under partial shading conditions. In complete radiation variation conditions, the traditional algorithms effectively track the MPP of the PV array, but they did not succeed to track the MPP under partial shading or varying environmental conditions. The design [6] is appropriate for solving uninterrupted optimization problems with high performance among the PSO variants.
from the PV array is transferred through the buck-boost converter which plays the role of an impedance matching device, and a booster system called the MPPT technique which increased the power level to that of load requirements when less power is provided from the PV solar panels side. The whole system is presented in Figur 1 below. For the study organization, the paper content consists of the introduction section, literature review section, methodology section ( PV array, buck-boost converter, and the proposed technique), results and discussion section, and finally the conclusion section.
Literature Review
The literature [8] observed that the buck-boost converter is appropriate for any load resistance connected to the PV system. In order to harvest the produced power of the PV system and optimally exploit the captured solar energy, [9] treats with a novel artificial neural network particle swarm optimization approach (ANN-PSO). In [10], a review is developed on the potential of particle swarm optimization for solving various types of optimizations in chemometrics. Moreover, another review is introduced by [11], where the origin, background, and theoretical analysis of PSO were carried out. [12] presented a master-slave swarm shuffling evolution algorithm combined with self-adaptive particle swarm optimization (MSSE-SPSO) where its results amazingly improve the calculation accuracy and show an efficient approach for the hydrological model calibration. However, the PSO performance is drastically impacted by the control parameters selection and the velocity implementation strategies. Therefore, a self-adaptive PSO with various velocity approaches is proposed by [13] to improve the performance of PSO. This paper [14] introduced several mutation operators to enhance the PSO performance. [15] developed an MPPT mechanism based on the novel hybrid particle swarm optimization algorithm without losing the efficiency of the battery obtained from the source.[16] proposed a novel hybrid series salp particle Swarm optimization (SSPSO) which is highly required for suburb area applications where the partial shading conditions are frequent. [17] has explored an improved salp swarm algorithm based on particle swarm optimization for maximum power point tracking of optimal photovoltaic systems.[7] The PSO traditional technique was supposed to be the most efficient method of tracking the maximum power in the pattern shading situations.
1.1. System Description
In order to clearly investigate the system working principle and components details, the circuit diagram below Figure 1 is required.
1.2. Solar Cell Circuit Description
By the electrical model, the solar cell is characterized as shown in Figure 2. In the following Eq. (1) the current-voltage characteristics are termed as: (1) From the above Eq. (1) I output current and V output voltage, dark saturation current I_O, electron charge q, Boltzmann constant k, the diode quality or ideality factor A, the absolute temperature T, the solar cell series and shunt resistances RS and RSH are all necessary parameters for PV solar circuit. Through, the semiconductor material and solar panel, the RS resistance is offered. The complexity exists in describing the shunt resistance RSH. For the p-n junction of non-ideal nature, a short circuit path is given around the junction due to the presence of the impurities located near the cell edges. In an ultimate case, RS is 0 whereas RSH is
Methodology
In this paper, we realized a compound system made of PV solar panels, a DC-DC buck-boost converter, the proposed MPPT technique, and the end-use ( load). The power
Figure 2. Circuit of Solar panel and its equivalent panels series.
Recent Adv Sci Eng, Vol. 2, No. 2, pp. 42-48, December, 2022
infinite. To improve the products, the manufacturers try to minimize the resistance effect. Similarly, the ultimate set-up is not possible. The RSH outcome is not considered. RSH is infinite for simplification, thus Eq. (1) last term is abandoned.
1.3. Higher Power-Point, Short Circuit Current, and
Open-Circuit Voltage Dual significant current-voltage points are the short circuit current ISC and open-circuit voltage VOC. The power generation is 0 at both points. From (1) VOC can be approximated when I= 0, the RSH is discarded shown in Eq. (2) the ISC is at V= 0 and nearly same as the light generated current IL as shown in Eq. (3). (2) (3) By means of solar circuit, the maximum power is generated at MPP and the current-voltage characteristics which are distinctive at various temperatures are shown in following Figure 3.
1.4. Effect of Temperature and Irradiance
The temperature and irradiation are the significant factors to be noted. It leads to the module characteristics which are getting affected. During the day times, the MPP varies and that’s why MPP is tracked constantly and assured about the maximum available power received from the panel. On voltage-power (V-P) and voltage-current (V-I), the irradiance effect is shown in Figure 4, the current and voltage which are using ISC and, VOC normalized respectively. The irradiation increment resulting in the maximum current generated by the photovoltaic system is related directly to the irradiance level. Furthermore, a short circuit is not an operating point (no generating power), the PV current is termed as Eq. (1). With irradiation, the characteristics of voltage-current vary. Figure 4 depicted that by comparing with voltage, the change in current is greater. The dependency of voltage on irradiation is frequently abandoned. When the irradiation rises from the positive value of the current and the voltage, the power is also positive. Thus, more power is generated when more irradiation results.
Figure 3. Solar panel characteristic curves at constant irradiation and varying temperatures.
Figure 4. At constant temperature and four variation isolation values of V-I and V-P curves. On contrary, the voltage is affected mostly by temperature. On the temperature, the open-circuit voltage is linearly dependent as shown in Eq. (5). (4) Based on Eq. (4) the temperature effect on VOC shows negative since the voltage decreases when the temperature increases. The little increase with current doesn’t compensate for voltage decrease due to the rise in temperature and thus a reduction in power is seen. When the temperature changes, the higher power point, short circuit current, and open-circuit voltage vary.
2. Buck-Boost Converter
The switch-mode power supply (SMPS) converters are mostly used in recent electronics applications. The inverting buck-boost (IBB) converter is one type of SMPS converter where the voltage range can be regulated up or down based on the load requirements. However, it provides a negative output voltage, the IBB is simple in design and has a cost-effective tradeoff (inexpensive). The applications such as batteries, home electronic consumers, DC-DC regulated buses for communication or industries often rely on IBB converters. This converter has a PMOS switch connected to the inductor which acts as an energy storage reservoir providing power to the load when the PMOS switch is off. Moreover, a diode acts as a current path in reverse biased operation. Figure 5 shows a simplified schematic of the switching inverting converter and its different components.
Figure 5. Configuration of a conventional inverting buckboost (IBB) converter.
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The input voltage and output voltage relationship with the duty cycle (5) The peak-to-peak inductor ripple current is deduced as following (6) The input current and output current relationship (7) The maximum peak-to-peak inductor ripple current (8)
3. Proposed IPSO-BBC Algorithm
The particle swarm optimization (PSO) technique is a very well-known bio-inspired metaheuristic type algorithm. Its concept is developed based on the nature and motion of the flock of birds in the real world. As a famous metaheuristic optimization algorithm, PSO is recognized as a swarm intelligence strategy used to solve research problems. It is the most appreciated algorithm due to its simplicity and flexible implementation. The present agent location is found by the velocity expression linked with each particle in PSO. Figure 6 below represents the movement of the particles in the search region. Generally, in PSO, each particle is represented by its velocity and position. The velocity can be updated by the following formula:
The Flowchart of the proposed IPSO-BBC algorithm is presented in Figure 7.
Simulation Results And Discussions
For this work, Solarex-MXS 60 watts is selected for simulation purposes. The paper aims to optimize the PV solar power by taking two inputs of PV to efficiently supply the load. The proposed IPSO-BBC approach encompasses the PV solar module, buck-boost converter, an improved particle swarm optimization-based Buck-Boost converter MPPT controller, and the load mounted in the block diagram is developed in MATLAB/Simulink (Figure 8). The improved efficiency is calculated by the following formula [11]. The buck-boost converter switch gate signal or duty cycle is depicted in Figure 9. (11) In Table 1, We used the load power and the PV power based on the formula (11) to demonstrate the performan-
(9) Where g is denoted by the current best position, xi is defined as the individual best solution of the particle i. C1 and C2 are random values taken from the interval of [0,1]. The position can be updated by the following expression: (10)
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Figure 8. MATLAB/Simulink configuration of the proposed technique. ce of the proposed IPSO-BBC approach by considering the input parameters. The IPSO-BBC approach presents many advantages such as reducing the number of iterations, optimizing the input power, and giving an excellent efficiency. Based on the different simulations, we have observed that a series of irradiation changes (1000 W/m2, 700W/m2, 500W/m2, and 300 W/m2) at a temperature of 25 ºC presents 98% base efficiency and with
Figure 9. Buck boost converter switch gate signal plot around 1.3 seconds.
smooth powers follow-up Figures 10 and 11. Figures 1213 (zoomed efficiency and non-zoomed efficiency using IPSO-BBC algorithm under varying irradiation (500 W/ m2 to 1000W/m2) at 25 ºC. Figure 14 (PV solar voltage and current using IPSO-BBC algorithm under varying ir-
Figure 10. Output and Input powers using IPSO-BBC algorithm under varying irradiation (500 W/m2 to 1000W/ m2) at 25 ºC.
Figure 11. Output and Input powers using IPSO-BBC algorithm at Standard Test Condition (1000W/m2 and 25 ºC).
Recent Adv Sci Eng, Vol. 2, No. 2, pp. 42-48, December, 2022
radiation [500 W/m2 to 1000W/m2] at 25 ºC) shows that the PV voltage does not change for varying irradiance but only the PV current is slightly affected. Figure 15 (Load voltage and current using IPSO-BBC algorithm under varying irradiation (500 W/m2 to 1000W/m2) at 25 ºC, shows the resistive load of 50 Ω verified the Ohms relationship of ( V=R×I). The powers and efficiencies results are presented in Table 2.
Figure 12. Efficiency using IPSO-BBC algorithm under varying irradiation (500 W/m2 to 1000W/m2) at 25 ºC.
Figure 15. Load voltage and current using IPSO-BBC algorithm under varying irradiation (500 W/m2 to 1000W/ m2) at 25 ºC. Table 2. Simulation results of powers with improved efficiencies for various irradiations at 25 ºC Irradiation (W/m2)
IPSO: improved particle swarm optimization; BBC: buck-boost converter.
Figure 13. Amplified Efficiency using IPSO-BBC algorithm under varying irradiation (500 W/m2 to 1000W/m2) at 25 ºC within time interval of 2 to 4 seconds.
Figure 14. PV solar voltage and current using IPSO-BBC algorithm under varying irradiation (500 W/m2 to 1000W/ m2) at 25 ºC.
Conclusion
This paper presented the performance of metaheuristics swarm-based algorithm, improved particle swarm optimization-based buck-boost converter (IPSO-BBC) MPPT controller to track efficiently and accurately PV solar power under various environmental change conditions. This work enhanced the PV power to be tracked with a reduced number of iterations of the system. The Particle swarm optimization algorithm monitors the duty cycle of the buck-boost converter in order to match input (PV) power to the output (load) power and also controls PV solar model SOLRAEX-MXS60 parameters. The simulation results of the new IPSO-BBC algorithm under MATLAB/Simulink show an average efficiency of the proposed IPSO-BBC approach which is 98.24% and calculated by the ratio of the measured output power by PV actual power at different irradiation levels within the time of 6 seconds. The efficiency of the study is slightly greater than that of similar work efficiency which is 97% [9]. The IPSO-BBC has some advantages of high tracking speed and no oscillations over that of [9] under atmospheric change conditions.
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Data Availability Statement
The authors confirm that data that supports the findings of this study are available within the article. The graphs or raw data that support the findings of this study can be requested from the corresponding author.
Conflict Of Interest
The authors declared no potential conflicts of interest with regards to research, authorship, and/or publication of the article.
Ethics
There are no ethical issues with the publication of this manuscript.
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DAGAL, I.; AKIN, B. Improved particle swarm optimization based on buck-boost converter IPSO-BBC for photovoltaic system. Recent Advances in Science and Engineering 2022, Vol. 2, pp. 42-48. https://doi.org/10.14744/rase.2022.0007