Intelligent Techniques for MPPT Control in Photovoltaic Systems: A Comprehensive Review

El-Telbany, Mohammed E.; Youssef, Ayman; Zekry, Abdelhalim

doi:10.1109/icaiet.2014.13

Cited by 30 publications

(18 citation statements)

References 34 publications

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“…For example in [13,14,23,24] the descriptions of perturbation and observation, increment conductance, constant voltage and fractional open-circuit voltage MPPT methods are presented. However, papers [25][26][27][28] propose alternative MPPT techniques by means of artificial computational intelligence methods.…”

Section: Thermoelectric Panel With Evolutionary Mpptmentioning

confidence: 99%

Evolutionary Maximization of Energy Amount Harvested by Means of Panel of Thermoelectric Modules

Golonek

2022

Applied Sciences

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This work proposes the use of a specialized algorithm based on evolutionary computation to the global MPPT regulation of panel of thermoelectric modules connected serially in numerous string sections. Each section of the thermovoltaic panel is equipped with local DC/DC converter controlled by the proposed algorithm and finally this allows the optimization of the total efficiency of conversion. Evolutionary computations adjust PWM signals of switching waveforms of DC/DC sectional simple boost converters, which have outputs configured in parallel. It gives the chance to obtain the highest level of electric energy harvested, i.e., thanks to boost converting operational points precise adaptation to the system temperature profile as well as electric load level. The simulation results of the proposed evolutionary technique confirmed the high speed of the MPPT process that is much better than for perturbation and observation, as well as incremental conductance methods, and it assures concurrent optimization of numerous PWM signals. Next, the work shows practical optimization results achieved by the proposed algorithm implemented to microcontroller module controlling the DC/DC converter during thermal to electric conversion experiment. A laboratory thermovoltaic panel was constructed from a string of Peltier modules and radiator that assured passive cooling. The measurements obtained once more proved the MPPT evolutionary regulation properness and its adaptation effectiveness for different resistive test loads.

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Section: Thermoelectric Panel With Evolutionary Mpptmentioning

confidence: 99%

Evolutionary Maximization of Energy Amount Harvested by Means of Panel of Thermoelectric Modules

Golonek

2022

Applied Sciences

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“…Both adaptive step size and fixed step size P&O methods have been simulated and tested. Telbany et al [19] provides a comprehensive review of intelligent methods in MPPT control design. A large number of hybrid MPPT algorithms are surveyed and compared.…”

Section: Related Workmentioning

confidence: 99%

Designing Localized MPPT for PV Systems Using Fuzzy-Weighted Extreme Learning Machine

Yan

Ren

et al. 2018

Energies

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A maximum power point tracker (MPPT) should be designed to deal with various weather conditions, which are different from region to region. Customization is an important step for achieving the highest solar energy harvest. The latest development of modern machine learning provides the possibility to classify the weather types automatically and, consequently, assist localized MPPT design. In this study, a localized MPPT algorithm is developed, which is supported by a supervised weather-type classification system. Two classical machine learning technologies are employed and compared, namely, the support vector machine (SVM) and extreme learning machine (ELM). The simulation results show the outperformance of the proposed method in comparison with the traditional MPPT design.

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“…Extensive research has been done during last decade. Dozens of algorithms have been investigated [4,5], from the simplest ones [6] to the most complex [7,8]. Many studies are dedicated to comparative analysis.…”

Section: Introductionmentioning

confidence: 99%

“…Many studies are dedicated to comparative analysis. With a few exceptions [9], most are based on a review of previous works [4,7] or, at most, on simulation [6,10]. Some previous works are not conclusive about the benefits of intelligent algorithms compared to the simplest ones, considering their drawbacks [7].…”

Section: Introductionmentioning

confidence: 99%

Analyses of MPPT Algorithms in Real Test Conditions

Guimaraes

Leite

2020

2020 9th International Conference on Renewable Energy Research and Application (ICRERA)

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Maximum Power Point Tracking (MPPT) algorithms are of major importance for optimized yield in gridconnected PV systems. Many algorithms have been investigated. However, most of the works compare these algorithms based on a literature review or on simulation. This paper presents an experimental analysis of MPPT techniques: two of the simplest (Perturb & Observe and Incremental Conductance) and two of the most complex (Fuzzy Logic Controller and Particle Swarm Optimization). The results are carried out in real test conditions, with and without shadow. The power converter is based on a boost converter and a voltage source inverter. The control is implemented using Simulink® and dSPACE 1103 real-time controller board. Moreover, the MPPT techniques of three commercial string inverters are also analysed.

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Intelligent Techniques for MPPT Control in Photovoltaic Systems: A Comprehensive Review

Cited by 30 publications

References 34 publications

Evolutionary Maximization of Energy Amount Harvested by Means of Panel of Thermoelectric Modules

Evolutionary Maximization of Energy Amount Harvested by Means of Panel of Thermoelectric Modules

Designing Localized MPPT for PV Systems Using Fuzzy-Weighted Extreme Learning Machine

Analyses of MPPT Algorithms in Real Test Conditions

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