Parameters estimation of photovoltaic modules: comparison of ANN and ANFIS

Salem, Fawzan; Awadallah, Mohamed A.

doi:10.1504/ijied.2014.059230

Cited by 17 publications

(8 citation statements)

References 12 publications

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“…A solar cell can be modelled with a current source in parallel with a diode, a shunt resistor to represent ground leakage, and a series resistor to account for power loss associated with cell current (Figure 2) (Salem and Awadallah, 2014). So we can mathematically express the current produced by the solar cell as (Khireddine et al, 2014;Eckstein, 1990;Salem and Awadallah, 2014;:…”

Section: Pv Modulementioning

confidence: 99%

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Fuzzy logic MPPT control for a solar PV module applied to water pumping

Lazizi

Kesraoui

Chaib

2016

IJIED

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Abstract:In this paper, the fuzzy logic control for maximum power point tracking (MPPT) of a solar photovoltaic (PV) module in water pumping application is presented. A PV system including a solar module, a DC/DC boost converter, a fuzzy MPP tracker and a resistive load is modelled and simulated for various atmospheric conditions. The P-V and I-V characteristics are first obtained for various values of solar irradiation and temperature. Also, the increased efficiency of the photovoltaic system using MPPT is computed and plotted. The fuzzy control method is compared with the perturb and observe (P&O) method in one hand and with an improved P&O technique using artificial neural network (ANN) in the other. The fuzzy MPP tracker is then applied to the water pumping system composed of a solar panel, DC/DC boost converter, DC/AC inverter, induction motor and centrifugal pump.Keywords: photovoltaic modules; DC/DC boost converter; maximum power point tracking; MPPT; fuzzy logic; perturb and observe; P&O; water pumping system. This paper is a revised and expanded version of a paper entitled 'Fuzzy logic maximum power point tracking control for a solar PV

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Section: Pv Modulementioning

confidence: 99%

“…So we can mathematically express the current produced by the solar cell as (Khireddine et al, 2014;Eckstein, 1990;Salem and Awadallah, 2014;:…”

Section: Pv Modulementioning

confidence: 99%

Fuzzy logic MPPT control for a solar PV module applied to water pumping

Lazizi

Kesraoui

Chaib

2016

IJIED

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“…ese soft-computing techniques are problem solvers using trial and error metaheuristic or stochastic optimization procedures such as pattern search (PS) [41], genetic algorithm (GA) [42], simulated annealing (SA) [43,44], particle swarm optimization [45,46], artificial fish swarm algorithm (AFSA) [47], fuzzy logic [48], and artificial neural networks (ANNs) [49][50][51]. ese methods have disadvantages, either in terms of sophistication and accuracy or in terms of convergence and speed of execution.…”

Section: Introductionmentioning

confidence: 99%

A Fast and Accurate Analytical Method for Parameter Determination of a Photovoltaic System Based on Manufacturer’s Data

Ndegwa

Simiyu

Ayieta

et al. 2020

Journal of Renewable Energy

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Modeling and simulation of a photovoltaic solar system play a significant role in understanding its behavior in various environmental conditions. Utilization of the datasheet information in modeling and simulation of the PV system correlates the experimental data and the theory that instigate the mathematical predictions of an actual system. A single-diode model gives a simple, fast, and straightforward way of depicting the PV system performance. We have developed a new approach of determining the five unknown parameters of a single-diode model using manufacturer’s data at three main points: the open circuit point (OCP), short circuit point (SCP), and the maximum power point (MPP) of the IV and PV curves. The ideality factor (A) and the diode saturation current (Io) are the key unknown parameters that greatly affect the reduplication of the three main points. The purpose of this study is to evaluate the ideality factor using simple calculation procedure starting from its optimal value (Ao) and other values within the proximity of Ao. The optimal value is obtained by assumptions of negligible series resistance (Rs) and very large shunt resistance (Rsh). Therefore, the choice of the other ideality factors in the neighborhood of its optimal value gives rise to different values of Rs, Rsh, and Iph that are more realistic in an experimental setup. Positive values of Rsh and Rs have been iteratively obtained by utilizing data at maximum power point combined with open and short circuit data. The five unknown parameters have been determined in the proximity of Ao and have been used to plot the PV curve with accuracy and precision of less than 0.5% error of maximum power and less than 0.1% error of Voc of manufacturer’s data. The proposed method has been implemented using fast, simple, and accurate procedures using GNU Octave programming software to calculate Ao, Io, Rs, Rsh, and Iph and to execute both Rs-Rsh and PV characteristic equations of BP3235T, KC200GT, BP-SX 150, and MSX60 PV modules. The reduced steps employed in the algorithm improve execution speed, thereby reducing the computation time.

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“…On the other hand, analytical and optimisation methods could be combined yielding a hybrid parameter estimation routine which can improve convergence behaviour and modelling accuracy (Chin et al, 2017). Moreover, an innovative estimation technique based on the learning, nonlinear mapping, and pattern recognition capabilities of artificial intelligence tools is presented in Salem and Awadallah (2014). Intelligent modules based on artificial neural networks and adaptive neuro-fuzzy inference systems are trained using data generated by the mathematical model to identify the best parameter set of PV cells.…”

Section: Introductionmentioning

confidence: 99%

Optimisation-based parameter estimation of photovoltaic modules

Awadallah¹,

Venkatesh²

2018

IJIED

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This paper presents a methodology for parameter estimation of photovoltaic (PV) modules based on global search and optimisation algorithms. A nonlinear optimisation problem is formulated to search the best set of equivalent circuit parameters which minimises a certain objective function. The objective function measures the discrepancy between computed and targeted performance. The proposed technique is flexible on the source of the targeted performance, which could be datasheet information or experimental measurements. The method is also flexible on the nature and number of sought parameters according to the equivalent circuit representation and available information. The nonlinear optimisation problem is solved using three different global search routines, namely, genetic algorithms (GA), particle swarm optimisation (PSO) and bacterial foraging (BF). Effectiveness of the proposed methodology is shown through three case studies on different PV modules of various ratings and manufacturers. The extracted parameters could accurately simulate the performance of PV modules under normal operation as well as low irradiance and partial shading conditions.

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Parameters estimation of photovoltaic modules: comparison of ANN and ANFIS

Cited by 17 publications

References 12 publications

Fuzzy logic MPPT control for a solar PV module applied to water pumping

Fuzzy logic MPPT control for a solar PV module applied to water pumping

A Fast and Accurate Analytical Method for Parameter Determination of a Photovoltaic System Based on Manufacturer’s Data

Optimisation-based parameter estimation of photovoltaic modules

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