Extraction of maximum power from a solar PV system using fuzzy controller based MPPT technique

Samal, Sarita; Barik, Prasanta Kumar; Sahu, Sandeep

doi:10.1109/icsesp.2018.8376721

Cited by 23 publications

(7 citation statements)

References 9 publications

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“…The maximum power point voltage will be compared with the actual output of the hybrid system which generates the error. The error between the maximum power point voltage (S. Samal et al, 2018) and actual output of the hybrid system will be then sent to the fuzzy logic controller generating the processed error. The processed error from the controller will be compared with the carrier waveform producing gate pulse for the switch of the high gain interleaved boost converter.…”

Section: Perturb and Observe Mpptmentioning

confidence: 99%

“…The intelligent fuzzy controller has been chosen with MPPT controllers (S. Samal et al, 2018) as it possess several advantages like cost effective to implement, covering wide range of operating conditions, robust operation, increased reliability and higher precision with huge number of membership functions. The controlled output from the MPPT based distributed renewable system will be directed to brush less DC motor through inverter for driving the vehicle.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Perturb and Observe Mpptmentioning

confidence: 99%

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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show abstract

“…The membership functions are significant in affecting the speed and accuracy of FLC [31]. Figure 3 depicts that E rr and ΔE rr are two major FLC input variables while D of a DC-DC converter is the output variable to be manipulated by FLC [32]. The input variables are assigned to several linguistic variables which are denoted by negative big (NB), negative small (NS), zero (ZE), positive small (PS) and positive big (PB) [33].…”

Section: A Flcmentioning

confidence: 99%

Artificial Intelligence Based MPPT Techniques for Solar Power System: A review

Yap

Sarimuthu

Lim

2020

Journal of Modern Power Systems and Clean Energy

194

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In the last decade, artificial intelligence (AI) techniques have been extensively used for maximum power point tracking (MPPT) in the solar power system. This is because conventional MPPT techniques are incapable of tracking the global maximum power point (GMPP) under partial shading condition (PSC). The output curve of the power versus voltage for a solar panel has only one GMPP and multiple local maximum power points (MPPs). The integration of AI in MPPT is crucial to guarantee the tracking of GMPP while increasing the overall efficiency and performance of MPPT. The selection of AI-based MPPT techniques is complicated because each technique has its own merits and demerits. In general, all of the AI-based MPPT techniques exhibit fast convergence speed, less steady-state oscillation and high efficiency, compared with the conventional MPPT techniques. However, the AI-based MPPT techniques are computationally intensive and costly to realize. Overall, the hybrid MPPT is favorable in terms of the balance between performance and complexity, and it combines the advantages of conventional and AI-based MPPT techniques. In this paper, a detailed comparison of classification and performance between 6 major AI-based MPPT techniques have been made based on the review and MATLAB/Simulink simulation results. The merits, open issues and technical implementations of AI-based MPPT techniques are evaluated. We intend to provide new insights into the choice of optimal AI-based MPPT techniques. Index Terms-Maximum power point tracking (MPPT), artificial intelligence (AI), fuzzy logic control (FLC), artificial neural network (ANN), genetic algorithm (GA), swarm intelligence (SI), machine learning (ML).

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“…The flowchart of P&O MPPT method is shown in Fig. 3 which generates the required pulses for the boost converter [20][21][22][23][24]. The Fig.…”

Section: Modeling Of Solar Pvmentioning

confidence: 99%

Performance Improvement of a Distributed Generation System Using Unified Power Quality Conditioner

Samal

Hota

Barik

2020

Technol Econ Smart Grids Sustain Energy

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Extraction of maximum power from a solar PV system using fuzzy controller based MPPT technique

Cited by 23 publications

References 9 publications

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

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

Artificial Intelligence Based MPPT Techniques for Solar Power System: A review

Performance Improvement of a Distributed Generation System Using Unified Power Quality Conditioner

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