Photovoltaic module simulation by neural networks using solar spectral distribution

Piliougine, Michel; Elizondo, David; Mora‐López, Llanos; Sidrach‐de‐Cardona, M.

doi:10.1002/pip.2209

Cited by 17 publications

(12 citation statements)

References 29 publications

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“…Therefore, we are increasing the weight of the non-frequent samples on the trained MLP. The preprocessing process using the SOM is essential in order to achieve a usable model in all cases, not only under frequent atmospheric conditions [16,29].…”

Section: Selection Of the Training Datasetmentioning

confidence: 99%

“…This has an improvement effect in performance and training time. Finally, Piliougine et al [16] used a Self-Organised Map (SOM) to classify the original samples in a reduced number of clusters, considering including in the training dataset only those samples nearest to the centroid of each cluster. In that paper the performance of the MPL is analysed, comparing results using random selection versus the ones using SOM based selection.…”

Section: Selection Of the Training Datasetmentioning

confidence: 99%

“…In the literature, there are some works which use ANN models to obtain the IeV curve [11e15]. Additional parameters, as the solar spectrum, can be included as inputs to improve the accuracy [16].…”

Section: Introductionmentioning

confidence: 99%

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CPV module electric characterisation by artificial neural networks

et al. 2015

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a b s t r a c tConcentrating photovoltaic (CPV) is a relatively new technology with promising future expectations. However, it is at an early stage of development and it has much room for improvement. In order to gain knowledge about CPV technology, outdoor measurements are necessary to adjust models and to study the influence of the atmospheric conditions on the modules performance. In this work, multilayer perceptron models are applied to generate IeV characteristic curves of one of the most extended commercial module of concentrating photovoltaic technology, using the influential atmospheric variables as inputs to the networks. To train these networks an experiment with real measurements was carried out in Ja en, Spain, from July 2011 to June 2012. In addition to a model based on IeV curves expressed as a list of points in Cartesian coordinates, we present an alternative model trained with curves represented in polar coordinates. A previous selection of the most representative samples from the initial dataset was performed using a Kohonen self-organising map. This procedure allows the simulation of the curves even under non-frequent atmospheric conditions. Using the proposed models, it is possible to obtain the characteristic curve of other CPV modules under different meteorological conditions, with high accuracy and fidelity.

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Section: Selection Of the Training Datasetmentioning

confidence: 99%

Section: Selection Of the Training Datasetmentioning

confidence: 99%

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CPV module electric characterisation by artificial neural networks

et al. 2015

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“…For example, Ref. [14] proposed a method based on artificial neural networks as an alternative to algebraic procedures to determine the I-V characteristic of a PV module. Also, aiming to overcome the problem of trapping in local minima, Ref.…”

Section: Introductionmentioning

confidence: 99%

A simple but accurate procedure for solving the five-parameter model

Mares

Paulescu

Badescu

2015

Energy Conversion and Management

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“…This approach is more flexible because the inputs considered for building the model can be easily modified. Several examples can be found in the literature where ANNs are used to generate I-V curves of PV modules [6][7][8][9][10][11][12][13]. In these works, ANNs are used in order to reconstruct the I-V curve of a module under given values of irradiance and module temperature.…”

Section: Introductionmentioning

confidence: 99%

Modelling photovoltaic modules with neural networks using angle of incidence and clearness index

Piliougine

Elizondo

Mora‐López

et al. 2014

Progress in Photovoltaics

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A neural network for modelling photovoltaic modules using angle of incidence and clearness index is proposed. Engineers require methods to estimate the output of a photovoltaic plant depending on meteorological conditions. Therefore, models for the grid inverter and the generator must be provided, and their outputs must be combined. The connection between both models is related to the maximum power point of the generator and how it is tracked by the inverter. That maximum power point under specific conditions of irradiance and module temperature is determined by the I-V curve of the module, which must be simulated under those conditions. Algebraic procedures were used to simulate the I-V curve. Recently, neural networks have been used for the same purpose. Previous methods only take into account the irradiance and the module temperature. The model proposed is based on neural networks, and it uses not only the irradiance and the module temperature but also the angle of incidence and the instantaneous clearness index as additional inputs. The normalised clearness replaces the standard clearness index because it allows the removal of the hourly trend found in this last index. This new model improves the results obtained with previous ones as it can distinguish amongst samples with the same solar irradiance and temperature values but with different angle of incidence and instantaneous clearness index. Results show that this model could be used to improve the accuracy of the tools used to forecast the output of photovoltaic plants.

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Photovoltaic module simulation by neural networks using solar spectral distribution

Cited by 17 publications

References 29 publications

CPV module electric characterisation by artificial neural networks

CPV module electric characterisation by artificial neural networks

A simple but accurate procedure for solving the five-parameter model

Modelling photovoltaic modules with neural networks using angle of incidence and clearness index

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