Comparison Between Perturb &amp; Observe, Incremental Conductance and Fuzzy Logic MPPT Techniques at Different Weather Conditions

Selman, Nasir Hussein

doi:10.15680/ijirset.2016.0507069

Cited by 31 publications

(6 citation statements)

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“…Based on the five different fuzzy levels, input parameters are used with the names NB (Negative Big), NS (Negative Small), ZE (Zero), PS (Positive Small), and PB (Positive Big). According to Table 3, the Fuzzy Type-2 system is governed by 25 different rules [14].…”

Section: Fuzzy Type-2 Methodsmentioning

confidence: 99%

MPPT Full Bridge Converter using Fuzzy Type-2

Prastyawan

Efendi

Murdianto

2021

JAREE

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Renewable energy application using Photovoltaic (PV) is developed as a conversion from solar energy into electrical energy. PV produces output power according to irradiation and temperature conditions. PV has a Maximum Power Point or MPP based on P-V characteristic curve. In certain conditions, PV has an unstable output power then the accuracy of the power generated is not maximum. MPPT method with conventional control is not optimal to resolves power inaccuracies in the system. When the system has a circuit problem, the conventional power converter will be damage. To achieve accuracy and maximize PV output, the Maximum Power Point method will find MPP. Using MPPT Fuzzy Type-2 method on the converter can reliably overcome the inaccuracies and tracking speed of PV power. Full Bridge Converter topology is used as a safety circuit with a high-frequency isolation transformer. Implemented on MATLAB/Simulink software, Simulation results in Model 1 show that the average power accuracy with Fuzzy Type-2 is 91.40% compared to Fuzzy Type-1 with an average power accuracy of 80.64%. In Model 2, Fuzzy Type-2 is 87.63% compared to Fuzzy Type-1 of 77.93%. MPPT method using fuzzy type-2 is better than using fuzzy type-1 in terms of power accuracy.Keywords: full bridge converter, fuzzy type-2, MATLAB/Simulink, maximum power point tracking, photovoltaic.

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Section: Fuzzy Type-2 Methodsmentioning

confidence: 99%

MPPT Full Bridge Converter using Fuzzy Type-2

Prastyawan

Efendi

Murdianto

2021

JAREE

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“…For the load is used lead-acid battery with nominal voltage 24 V, rated capacity 100 Ah, for initial stateof-charge is set 60% and battery response time is set 60 s (see Figure 12). For comparison reasons, the same Simulink model like in Figure 7, but instead of using the fuzzy logic controller is used MPPT charge controller block (see Figure 13) [7]. As can be seen in obtained results in Figure 11 (green response), the disadvantage of the P&O algorithm is that the operating point constantly oscillates around the reached maximum power of the solar panel.…”

Section: Implementation and Simulation Of The Proposed Systemmentioning

confidence: 99%

“…Artificial intelligence methods such as fuzzy logic controllers are widely used in industrial settings. Fuzzy logic IOP Publishing doi:10.1088/1757-899X/1298/1/012024 2 controllers are more efficient, reliable, and simple to design than pure MPPT controllers [7]. The second part of the paper describes the design of a fuzzy logic-controlled buck-boost DC-DC converter.…”

Section: Introductionmentioning

confidence: 99%

Using a fuzzy logic controller in photovoltaic power systems for maximizing PV array efficiency

Drakulić,

Blanuša

2023

IOP Conf. Ser.: Mater. Sci. Eng.

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The goal of this work is to design a high-efficiency photovoltaic system (PV) with a fuzzy logic controller. This PV system consists of a PV panel, DC/DC Buck-Boost converter with a 24V DC load, and a fuzzy logic controller. Because the efficiency of normal solar PV modules is very low, we implemented the fuzzy logic controller. In this paper, we have proposed a system that is designed and simulated in MATLAB Simulink. In the fourth section of the paper are shown obtained results which show that using the fuzzy logic controller for photovoltaic systems has better performance. This controller can facilitate the PV array to reach the MPPT faster and provide more stable output power.

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“…The following is how this article is structured: Section 2 discusses the circuit model of a photovoltaic cell, Section 3 discusses conventional P&O MPPT, and Section 4 discusses proposed MPPT. ANFIS based maximum PowerPoint tracking and Section 5 deals with simulation results and inference of output [6] [7].…”

Section: ░ 1 Introductionmentioning

confidence: 99%

Modelling and Simulation of MPPT Based Solar Photovoltaic System for Voltage Lift LUO Converter

Ananth

Singaravelu

2023

IJEER

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Several maximum PowerPoint tracking techniques have been proposed to achieve optimal matching between solar photovoltaic arrays to load for extracting maximum available power from photovoltaic source. These techniques differ in terms of tracking speed, complexity, cost, accuracy of tracking, number of sensors used etc., This article proposes an Adaptive Neuro Fuzzy-Inference System (ANFIS) based maximum PowerPoint tracking for positive-output voltage lift LUO converter based on solar (PV)system for resistant load application. Performance analysis of proposed (MPPT) technique has been evaluated in MATLAB/Simulink environment for four different cases of input radiation and temperature patterns. These test input patterns have been chosen based on European standard EC50530.Simulation results revealed that proposed ANFIS maximum PowerPoint tracking technique effectively captures the maximum PowerPoint in a faster way with good accuracy under both steady state and dynamic climatical conditions. Moreover, a comparative study has been presented between conventional (P & O) -technique and proposed ANFIS MPPT technique. Comparative study reveals that ANFIS MPPT technique has higher tracking efficiency with lesser tracking time without any oscillation around maximum power point.

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Comparison Between Perturb & Observe, Incremental Conductance and Fuzzy Logic MPPT Techniques at Different Weather Conditions

Cited by 31 publications

References 0 publications

MPPT Full Bridge Converter using Fuzzy Type-2

MPPT Full Bridge Converter using Fuzzy Type-2

Using a fuzzy logic controller in photovoltaic power systems for maximizing PV array efficiency

Modelling and Simulation of MPPT Based Solar Photovoltaic System for Voltage Lift LUO Converter

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