Fuzzy logic based MPPT approach in a grid connected photovoltaic system

Roy, Rajib Baran; Basher, Enamul; Yasmin, Rojoba; Rokonuzzaman, Md.

doi:10.1109/skima.2014.7083525

Cited by 21 publications

(7 citation statements)

References 13 publications

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“…Controller Stage. There are several MPPT algorithms [12][13][14][15] reported in the literature that can be used for the DC/DC boost converter. A classic perturb and observe (P&O) method has been used in this work.…”

Section: The Power Stagementioning

confidence: 99%

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Grid-Connected Photovoltaic System with Active Power Filtering Functionality

Vaquero

Vázquez

Soriano

et al. 2018

International Journal of Photoenergy

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Solar panels are an attractive and growing source of renewable energy in commercial and residential applications. Its use connected to the grid by means of a power converter results in a grid-connected photovoltaic system. In order to optimize this system, it is interesting to integrate several functionalities into the power converter, such as active power filtering and power factor correction. Nonlinear loads connected to the grid generate current harmonics, which deteriorates the mains power quality. Active power filters can compensate these current harmonics. A photovoltaic system with added harmonic compensation and power factor correction capabilities is proposed in this paper. A sliding mode controller is employed to control the power converter, implemented on the CompactRIO digital platform from National Instruments Corporation, allowing user friendly operation and easy tuning. The power system consists of two stages, a DC/DC boost converter and a single-phase inverter, and it is able to inject active power into the grid while compensating the current harmonics generated by nonlinear loads at the point of common coupling. The operation, design, simulation, and experimental results for the proposed system are discussed.

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Section: The Power Stagementioning

confidence: 99%

“…To satisfy (13) and (14) inequalities, the selected capacitor voltage v c should be much higher than the peak of the AC mains V p . To satisfy (15) and (16) inequalities, the load should be smooth.…”

Section: The Power Stagementioning

confidence: 99%

Grid-Connected Photovoltaic System with Active Power Filtering Functionality

Vaquero

Vázquez

Soriano

et al. 2018

International Journal of Photoenergy

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“…Several works are available in the literature detailing different MPPT algorithms and designs to boost PV device performance [7]. The Perturb and Observe (P&O) [8], Incremental conductance (INC) [9], Fuzzy Logic Controller (FLC) [10], P&O approach based on Particle Swarm Optimization (PSO) [11], and ANN [12] are most effective and standard algorithms. These strategies differ in oscillation across the absolute maximum power point (MPP), convergence speed, difficulty, stability, cost, and electronic equipment requirements [13].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Performance Analysis of ANN Algorithms for MPPT Energy Harvesting in Solar PV System

et al. 2021

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In this paper, artificial neural network (ANN) based Levenberg-Marquardt (LM), Bayesian Regularization (BR) and Scaled Conjugate Gradient (SCG) algorithms are deployed in maximum power point tracking (MPPT) energy harvesting in solar photovoltaic (PV) system to forge a comparative performance analysis of the three different algorithms. A comparative analysis among the algorithms in terms of the performance of handling the trained dataset is presented. The MATLAB/Simulink environment is used to design the maximum power point tracking energy harvesting system and the artificial neural network toolbox is utilized to analyze the developed model. The proposed model is trained with 1000 dataset of solar irradiance, temperature, and voltages. Seventy percent data is used for training, while 15% data is employed for validation, and 15% data is utilized for testing. The trained datasets error histogram represents zero error in the training, validation, and test phase of data matching. The best validation performance is attained at 1000 epochs with nearly zero mean squared error where the trained data set is converged to the best training results. According to the results, the regression and gradient are 1, 1, 0.99 and 0.000078, 0.0000015739 and 0.26139 for Levenberg-Marquardt, Bayesian Regularization and Scaled Conjugate Gradient algorithms, respectively. The momentum parameters are 0.0000001 and 50000 for Levenberg-Marquardt and Bayesian Regularization algorithms, respectively, while the Scaled Conjugate Gradient algorithm does not have any momentum parameter. The Scaled Conjugate Gradient algorithm exhibit better performance compared to Levenberg-Marquardt and Bayesian Regularization algorithms. However, considering the dataset training, the correlation between input-output and error, the Levenberg-Marquardt algorithm performs better.INDEX TERMS Solar photovoltaic (PV), energy harvesting (EH), maximum power point tracking (MPPT), artificial neural network (ANN), Levenberg-Marquardt (LM), Bayesian regularization (BR), scaled conjugate gradient (SCG).

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“…Several maximum power point tracking (MPPT) techniques have been developed for the solar charge controller (SCC) and studied to track this MPP of the solar cell. For example, perturb and observe (P&O) [8][9][10][11][12][13][14][15][16][17], incremental conductance (IC) [18][19][20][21][22][23], genetic neural algorithm [24][25][26] and fuzzy logic control (FLC) are studied [27][28][29] that can operate the solar PV panel at MPP with relatively higher efficiency. However, among them, the P&O algorithm is popular for its simplicity and high efficiency.…”

Section: Introductionmentioning

confidence: 99%

IoT-Enabled High Efficiency Smart Solar Charge Controller with Maximum Power Point Tracking—Design, Hardware Implementation and Performance Testing

et al. 2020

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Amid growing demand for solar photovoltaic (PV) energy, the output from PV panels/cells fails to deliver maximum power to the load, due to the intermittency of ambient conditions. Therefore, utilizing maximum power point tracking (MPPT) becomes essential for PV systems. In this paper, a novel internet of things (IoT)-equipped MPPT solar charge controller (SCC) is designed and implemented. The proposed circuit system utilizes IoT-based sensors to send vital data to the cloud for remote monitoring and controlling purposes. The IoT platform helps the system to be monitored remotely. The PIC16F877A is used as a main controller of the proposed MPPT-SCC besides implementing the perturb and observe (P&O) technique and a customized buck–boost converter. To validate the proposed system, both simulation and hardware implementation are carried out by the MATLAB/SIMULINK environment and laboratory set up, respectively. The proposed MPPT-SCC can handle the maximum current of 10 A at 12 V voltage. Results show that the efficiency of the proposed system reaches up to 99.74% during a month of performance testing duration.

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Fuzzy logic based MPPT approach in a grid connected photovoltaic system

Cited by 21 publications

References 13 publications

Grid-Connected Photovoltaic System with Active Power Filtering Functionality

Grid-Connected Photovoltaic System with Active Power Filtering Functionality

A Comparative Performance Analysis of ANN Algorithms for MPPT Energy Harvesting in Solar PV System

IoT-Enabled High Efficiency Smart Solar Charge Controller with Maximum Power Point Tracking—Design, Hardware Implementation and Performance Testing

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