Fuzzy based MPPT technique for PV system

Patil, Rajeshree

doi:10.1109/icecds.2017.8389686

Cited by 6 publications

(3 citation statements)

References 2 publications

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“…The designed system consists of solar photovoltaic, improved FLC based MPPT controller with pulse driven DC to DC converter and load. The PV simulation development of the model consists of two major classifications, one is one diode model, another one is two diode model [13]- [14]. During practical conditions the parallel diode acts as leakage current that is the reason behind to eliminate the parallel resistance for simplification [4].…”

Section: Design Consideration Of Thesystemmentioning

confidence: 99%

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Analysis of interleaved fuzzy Logic MPPT approach for PV system

Arulmurugan,

Sivakumar,

Baraneetharan

2023

J. Phys.: Conf. Ser.

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In this article, a detailed novel interleaved fuzzy logic control based Maximum Power Point Algorithm was designed. It was derived from the conventional hill climbing method, which is used to track solar Photo Voltaic (PV) systems over changing temperature and irradiance. The proposed system composed of the PV panel source, Maximum Power Point Tracking (MPPT) with designed interleaved Fuzzy Logic controller, DC to DC charge controller, and load. The designed fuzzy logic controller contains the two fuzzy subsets, one fuzzy concentrate the change in power with respect to change in voltage and another fuzzy concentrate the change in power with respect to change in current, compare with two fuzzy model uses to track the path more perfect compare to existing method as a novelty of the article. The proposed interleaved fuzzy MPPT optimally tune the parameters of the controller. The proposed system analyzed the few sub criteria, such as time response, stability and power efficiency. The performance of the presented controller has been validated with conventional controllers such as fractional open circuit approach, fractional short circuit approach and hill climbing approach. The proposed leads the highest tracking performance compared to existing. In addition, the proposed performed effectively in different environmental conditions.

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Section: Design Consideration Of Thesystemmentioning

confidence: 99%

“…The second fuzzy controller was examined with change in power with respect to the PV current [10] [11]. Based on the change in power with change in voltage and change in current the output can increase and decrease [12] [13].…”

Section: Design Consideration Of Thesystemmentioning

confidence: 99%

Analysis of interleaved fuzzy Logic MPPT approach for PV system

Arulmurugan,

Sivakumar,

Baraneetharan

2023

J. Phys.: Conf. Ser.

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“…Khurshid et al, 2019) is unable to track the maximum power for variable atmospheric and load conditions. Maximum power point tracking (R. Patil et al, 2017) is a technique of adjusting duty cycle of the converter such that maximum power can be tracked from the energy sources and also load voltage can be attained. There have been numerous tracking algorithms like incremental conductance, perturb-observe method, fractional open circuit voltage and fractional short circuit current which are very cost effective to implement and also simple in structure.…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of PI/RBFN ANN Controllers for Sensor Less BLDC Motor Control Based Fuel Cell/PV Hybrid Electric Vehicle

RAVIPATI¹,

Mani²,

Yarlagadda³

2021

JNMES

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The present society suffers with the problem of the greenhouse effect due to the emission of huge amount of carbon dioxide. And almost 70% of emission of carbon dioxide will be due to the usage of automotive vehicles. It is required to reduce the utilization of automotive vehicles to protect the life of earth for the coming years. This manuscript presents the design of electric vehicle with the utilization of renewable energy source like solar energy with the combination of fuel cell energy. It involves the design of maximum power point tracking system with intelligent fuzzy controller to track the maximum power for various weather conditions. The proposed electric vehicle drives the brush less DC motor whose speed has been monitored with the sensor less speed control technique. The speed control technique has been realized with two types of controller’s namely proportional intelligent controller and radial basis function neural network. Also the speed control technique has been analysed with the performance comparison of the two controllers in terms of speed, torque generated and also errors of speed and current to improve the performance of speed by 5% with the help of MATLAB/Simulink.

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