Maximum power point tracking of photovoltaic array using fuzzy logic control

Toumi, Djaafar; Benattous, Djilani; Ibrahim, Аhmed; Tarek, Bedida; Zobeidi, Messaoud; Fethi, Khelaifa; Serhoud, Hicham; Labbi, Yacine; Aboelsaud, Raef; Обухов, С. Г.; Boussabeur, Mohamed Tayeb

doi:10.11591/ijpeds.v13.i4.pp2440-2449

Cited by 4 publications

(5 citation statements)

References 10 publications

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“…The defuzzification method employs the center-of-area approach. Table 3 outlines the MPPT fuzzy logic controller's rules, totaling 25 rules, which govern its operation [23]- [25]. Enhancing of single-stage grid-connected photovoltaic system using fuzzy logic … (Mahmoud Haseeb) 2407…”

Section: Proposed Methodsmentioning

confidence: 99%

Enhancing of single-stage grid-connected photovoltaic system using fuzzy logic controller

Haseeb,

Hassan Ibrahim Mansour,

Othman

2024

IJECE

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The power generated by photovoltaic (PV) systems is influenced by environmental factors. This variability hampers the control and utilization of solar cells' peak output. In this study, a single-stage grid-connected PV system is designed to enhance power quality. Our approach employs fuzzy logic in the direct power control (DPC) of a three-phase voltage source inverter (VSI), enabling seamless integration of the PV connected to the grid. Additionally, a fuzzy logic-based maximum power point tracking (MPPT) controller is adopted, which outperforms traditional methods like incremental conductance (INC) in enhancing solar cell efficiency and minimizing the response time. Moreover, the inverter's real-time active and reactive power is directly managed to achieve a unity power factor (UPF). The system's performance is assessed through MATLAB/Simulink implementation, showing marked improvement over conventional methods, particularly in steady-state and varying weather conditions. For solar irradiances of 500 and 1,000 W/m<sup>2</sup>, the results show that the proposed method reduces the total harmonic distortion (THD) of the injected current to the grid by approximately 46% and 38% compared to conventional methods, respectively. Furthermore, we compare the simulation results with IEEE standards to evaluate the system's grid compatibility.

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Section: Proposed Methodsmentioning

confidence: 99%

Enhancing of single-stage grid-connected photovoltaic system using fuzzy logic controller

Haseeb,

Hassan Ibrahim Mansour,

Othman

2024

IJECE

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“…Hence, through the reduction of rules, it is possible to mitigate the expenses associated with the FL controller while simultaneously enhancing the feasibility of implementing this technology [26]. In contrast to previous research [17], [19], [21], [23], which identified 25 fuzzy rules, as well as other studies [18] that employed 49 rules, the current approach presents an optimised method utilising a mere seven rules. This streamlined approach significantly enhances the usability of the FL controller, as demonstrated in Table 2.…”

Section: The Hybrid Artificial Neural Network-fuzzy Maximum Power Poi...mentioning

confidence: 99%

“…Despite the relatively low levels of solar energy during these periods, it remains imperative to explore strategies for effectively utilising and harnessing solar radiation to generate electrical power. The primary motivation behind undertaking this study was the observation that a majority of the existing research papers on monitoring MPPT systems utilising FL technology [15]- [33] (primarily focused on testing these systems under conditions of high and medium solar radiation [18], [20], [29], [32]) However, there is a scarcity of studies that investigate the performance of MPPT techniques under relatively low levels of solar illumination [24].…”

Section: The Hybrid Artificial Neural Network-fuzzy Maximum Power Poi...mentioning

confidence: 99%

“…Additionally, it aims to elucidate the reasons behind the preference for the fuzzy logic control (FLC) technique over alternative algorithms. According to Toumi et al [18], the tracker utilising the FL technique exhibits enhanced performance in terms of both speed and precision when tracking the MPP in comparison to the perturb and observe (P&O) algorithm. Ibnelouad et al [19] describe a hybrid soft-computing approach to the implementation of intelligent MPPT methods in a PV system that can adjust to a wide range of possible operating situations.…”

Section: Introductionmentioning

confidence: 99%

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The effectiveness of a hybrid MPPT controller based on an artificial neural network and fuzzy logic in low-light conditions

Hichem,

Leila,

Amar

2024

Bulletin EEI

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Technological advancement and economic progress have made power consumption a big issue. Concern is growing as traditional energy sources dwindle. In the future, numerous fossil fuels will be insufficient to satisfy human requirements. This motivates research into the feasibility of using renewable energy sources. Renewable energy sources offer a multitude of advantages, including their cost-effectiveness, lack of environmental impact, and sustainable nature. Sunlight is currently the most prevalent source of energy because it is both free and readily accessible. Consequently, photovoltaic (PV) energy is gaining importance in the field of electricity generation. Tracking the maximum power point (MPP) in a solar PV system is challenging due to varying meteorological conditions (irradiance and temperature). To maximise the efficiency of a solar power installation, it is essential to monitor the PV array's optimum power point. This analysis compares the perturb and observe (PO), fuzzy logic (FL), and suggested artificial neural network (ANN)-fuzzy strategy for determining the MPP of a PV system with minimal radiation exposure. Simulation results show that at low irradiation levels, the proposed ANN-fuzzy maximum power point tracking (MPPT) unit controller is superior to the FL and PO MPPT controllers in terms of tracking maximum power.

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“…One of the main obstacles to the spread of solar cells systems is the loss of output solar energy, which results due to environmental changes. Accordingly, in order to overcome this problem, various artificial intelligence algorithms such as perturbation & observation (P&O), particle swarm optimization (PSO), artificial immune system (AIS), artificial bee colony (ABC), and artificial fish swarm algorithm (AFSA) play a pivotal role to get the maximum power point MPP [1]- [6]. In solar energy, artificial intelligence is extensively applied in predicting, controlling, and hence improving the performance of solar cell systems [7], [8].…”

Section: Introductionmentioning

confidence: 99%

Enhancing the output power of solar cell system using artificial intelligence algorithms

Ali,

El-Kammar,

Hamed

et al. 2024

IJPEDS

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The main objective of research in the field of solar cell systems is to obtain the maximum output power. In this respect, artificial intelligence (AI) is considered the current icon. Hence, in the present paper perturbation & observation (P&O) and particle swarm optimization (PSO) algorithms were used to achieve the maximum power. Solar irradiance at three different regions of Egypt was measured using a new technique based on Arduino microcontroller. The obtained experimental results of the solar irradiance were inlaid to the MATLAB simulation program to study the performance of the proposed algorithms. Many improvements were carried out in P&O and PSO algorithms to harvest maximum power for long hours daily by a continuous modulation of the duty cycle. The output maximum power and the reaching time of both improved P&O and PSO are better than the traditional one and PV array, which indicates their efficiency in harvesting the maximum power and enhancing the performance of solar cell systems. The reinforcing of the PV system by P&O improved its efficiency by 98.733%, while PSO improved its efficiency by 99.968%.

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Maximum power point tracking of photovoltaic array using fuzzy logic control

Cited by 4 publications

References 10 publications

Enhancing of single-stage grid-connected photovoltaic system using fuzzy logic controller

Enhancing of single-stage grid-connected photovoltaic system using fuzzy logic controller

The effectiveness of a hybrid MPPT controller based on an artificial neural network and fuzzy logic in low-light conditions

Enhancing the output power of solar cell system using artificial intelligence algorithms

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