Characterisation of Si-crystalline PV modules by artificial neural networks

Almonacid, Florencia; Rus, C.; Hontoria, L.; Fuentes, M.; Nofuentes, G.

doi:10.1016/j.renene.2008.06.010

Cited by 115 publications

(45 citation statements)

References 30 publications

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“…As the analytical and mathematical approach of the problem results in a cumbersome procedure, an artificial intelligence-based approach is a viable option as a function approximation problem [10][11][12][13][14], and the ANFIS can be used to estimate these 3 equivalent circuit parameters. A MPPT controller can be implemented based on this model.…”

Section: Solar Cell Characteristicsmentioning

confidence: 99%

ANFIS-based estimation of PV module equivalent parameters: application to a stand-alone PV system with MPPT controller

Kulaksız¹

2013

Turk J Elec Eng & Comp Sci

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Abstract:The performance and system cost of photovoltaic (PV) systems can be improved by employing high-efficiency power conditioners with maximum power point tracking (MPPT) methods. Fast implementation and accurate operation of MPPT controllers can be realized by modeling the characteristics of PV modules, obtaining equivalent parameters.In this study, adaptive neuro-fuzzy inference systems (ANFISs) have been used to obtain 3 of the parameters in a singlediode model of PV cells, namely series resistance, shunt resistance, and diode ideality factor. The input parameters of ANFISs are a material-type of PV modules, short circuit current, open circuit voltage, and unit area under the I-V curve of the PV module. The advantage of the proposed method is that the equivalent parameters can be obtained for a wide range of PV modules of different types (monocrystalline, multicrystalline, and thin-film) using easily obtainable electrical parameters. To demonstrate the accuracy of the proposed model, MPPT control is implemented in a PV system with a battery charge application for 3 different types of PV modules. The obtained results suggest that the ANFIS model appears to be a useful tool for estimating the equivalent parameters of PV modules.

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Section: Solar Cell Characteristicsmentioning

confidence: 99%

ANFIS-based estimation of PV module equivalent parameters: application to a stand-alone PV system with MPPT controller

Kulaksız¹

2013

Turk J Elec Eng & Comp Sci

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“…Various studies have been conducted on PV plant performance and its different elements [4][5][6][7]: the influence of PV module technology [8,9], inclination [10,11], inverter and control systems [12], sun-tracker system [13] and wiring [14] have been determined for experimental and real facilities,. They underline the relatively important role of all these elements in overall system performance.…”

Section: Introductionmentioning

confidence: 99%

Performance of grid-tied PV facilities: A case study based on real data

Díez-Mediavilla

Dieste-Velasco

Rodríguez-Amigo

et al. 2013

Energy Conversion and Management

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A new procedure is presented to analyse the performance of grid-tied PV facilities. It needs limited amounts of data that are easily sourced and is based on knowledge of the analyzed system and its mode of operation. The procedure is applied, in a case study, to compare real PV production at two 100 kWp grid-connected PV installations. Located in the same geographical region, the installation of these two facilities followed the same construction criteria -PV panels, panel support system and wiring-and the facilities were exposed to the same atmospheric temperature and solar radiation. They differ with regard to their inverter technology: one facility uses an inverter with an integrated transformer system and the other uses a transformerless inverter. The results show that the transformerless inverter system performed better than the isolated system by a factor of 1.2%, which, in economic terms, represents more than 2000 €/year.

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“…Other methods apply curve fitting method to some assumed functional relationship or employ integration procedures based on the computation of the area under the I-V curves or use linear regression. It is also possible to predict PV module performance under illumination through a several numerical or algebraic methods [13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%