Implementation of Maximum Power Point Tracking on Photovoltaic Using Fuzzy Logic Algorithm

Lubis, Arton Johan; Susanto, Erwin; Sunarya, Unang

doi:10.12928/telkomnika.v13i1.131

Cited by 11 publications

(9 citation statements)

References 13 publications

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“…Many advantages can be achieved by using Fuzzy logic such as nonlinearities, not needing a mathematical modeling and working with imprecise inputs [15,16,21]. Thus, it has received increased interest in MPPT applications [22][23][24][25].…”

Section: Fuzzy Logic Mppt Algorithmmentioning

confidence: 99%

“…Thus, artificial intelligence MPPT algorithms such as an artificial neural networks (ANN), fuzzy logic (FL), and genetic algorithms (GA) are used to improve the efficiency of PV panel [15]. Fuzzy logic MPPT algorithm represented a general technique of expressing linguistic rules, thus it can provide a fast response with simple mathematical non-linear characteristics [16].…”

Section: Introductionmentioning

confidence: 99%

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Improved dynamic performance of photovoltaic panel using fuzzy Logic-MPPT algorithm

Nasser

Yaqoob

Hassoun

2021

IJEECS

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The nonlinear characteristics and intense credence dependence of photovoltaic (PV) panel on the solar irradiance and ambient temperature demonstrate important challenges for researchers in the PV panel topic. To overcome these problems, the maximum power point tracking (MPPT) controller is needed which can improve the PV panel efficiency. In other words, for maximum efficiency, the MPPT controller can help to extract the optimal and overall available output power from the PV panel at different output load conditions. Fuzzy logic (FL) is one of the strongest techniques in the extracting of MPP in the PV panel since it has several advantages; robust; no requirement to have an accurate mathematical model, and works with imprecise inputs. Therefore, in this paper, fuzzy logic (FL-MPPT) has been designed and simulated to improve dynamic performance PV panel at different solar irradiance and then increased the efficiency. Therefore, "MATLAB/Simulink software" has been used to build the proposed algorithm and the simulation results have been adequate as well. Besides, a robust FL-MPPT algorithm has been presented with high dynamic performance under different weather conditions. Finally, the proposed algorithm has a quicker response and less oscillatory comparison of the conventional algorithms in the subject of extracting the maximum PV power.

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Section: Fuzzy Logic Mppt Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improved dynamic performance of photovoltaic panel using fuzzy Logic-MPPT algorithm

Nasser

Yaqoob

Hassoun

2021

IJEECS

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“…When the switch is off, the current will flow from the inductor through the resistor, capacitor and diode. This will discharge the inductor and to be charged again in the next cycle [16][17]. Figure 2 illustrates the operation of the buck boost converter with its input coming from a 150W monocrystalline solar panel.…”

Section: Buck-boost Convertermentioning

confidence: 99%

Fuzzy logic-based maximum power point tracking solar battery charge controller with backup stand-by AC generator

Madrigal

Cuevas

Hora

et al. 2019

IJEECS

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<span>This paper presents a Fuzzy-based Maximum Power Point Tracking Solar Battery Charge Controller with backup stand-by AC generator. This study is developed to provide a maximum power point tracking battery charge controller using fuzzy logic algorithm for isolated areas that uses solar panels and AC generators. Fuzzy Logic Toolbox in MATLAB and Arduino IDE were used in implementing fuzzy logic algorithm. Fuzzy logic is a mathematical system where something can be represented in continuous values between 0 and 1. It basically represents systems based on human reasoning. The hardware comprises of four components – the switched mode power supply, the source switching circuit, buck-boost converter and the diversion load controller. The pre-testing conducted based on the methodology indicates that the proposed charge controller is efficient in maximizing the input power that enters the charge controller under different conditions. The current efficiency rate of the charge controller is 96.02%. The average battery charging time for a fully-discharged 12V Lead-Acid Battery using AC source, DC source and both AC and DC sources are 2 hours and 30 minutes, 8 hours and 15 minutes and 5 hours and 30 minutes, respectively, while discharging took 3 hours and 40 minutes with two 30-watt floodlight load.</span>

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“…There are many ways to increase the efficiency of solar cells. One of them is using a control technique, Maximum Power Point Tracking (MPPT), that extracts the maximum available power from the panels of solar cells [1,2] and its advanced technology of MPPT is employing fuzzy logics which is applied on the controller [3]. Beside the harvesting technology, research also developed in its materials of preparing the solar cells and the most important part of solar cells is the absorber layer which has a duty in corverting the light energy from solar radiation in to voltage of electricity.…”

Section: Introductionmentioning

confidence: 99%

Critical Condition in CuInAlSe2 Growth of Solar Cell Absorber

et al. 2016

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Implementation of Maximum Power Point Tracking on Photovoltaic Using Fuzzy Logic Algorithm

Abstract: Abstract

Cited by 11 publications

References 13 publications

Improved dynamic performance of photovoltaic panel using fuzzy Logic-MPPT algorithm

Improved dynamic performance of photovoltaic panel using fuzzy Logic-MPPT algorithm

Fuzzy logic-based maximum power point tracking solar battery charge controller with backup stand-by AC generator

Critical Condition in CuInAlSe2 Growth of Solar Cell Absorber

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