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Modeling and simulation of a stand-alone photovoltaic system using an adaptive artificial neural network: Proposition for a new sizing procedure

Author

Listed:
  • Mellit, A.
  • Benghanem, M.
  • Kalogirou, S.A.

Abstract

This paper presents an adaptive artificial neural network (ANN) for modeling and simulation of a Stand-Alone photovoltaic (SAPV) system operating under variable climatic conditions. The ANN combines the Levenberg–Marquardt algorithm (LM) with an infinite impulse response (IIR) filter in order to accelerate the convergence of the network. SAPV systems are widely used in renewable energy source (RES) applications and it is important to be able to evaluate the performance of installed systems. The modeling of the complete SAPV system is achieved by combining the models of the different components of the system (PV-generator, battery and regulator). A global model can identify the SAPV characteristics by knowing only the climatological conditions. In addition, a new procedure proposed for SAPV system sizing is presented in this work. Different measured signals of solar radiation sequences and electrical parameters (photovoltaic voltage and current) from a SAPV system installed at the south of Algeria have been recorded during a period of 5-years. These signals have been used for the training and testing the developed models, one for each component of the system and a global model of the complete system. The ANN model predictions allow the users of SAPV systems to predict the different signals for each model and identify the output current of the system for different climatological conditions. The comparison between simulated and experimental signals of the SAPV gave good results. The correlation coefficient obtained varies from 90% to 96% for each estimated signals, which is considered satisfactory. A comparison between multilayer perceptron (MLP), radial basis function (RBF) network and the proposed LM–IIR model is presented in order to confirm the advantage of this model.

Suggested Citation

  • Mellit, A. & Benghanem, M. & Kalogirou, S.A., 2007. "Modeling and simulation of a stand-alone photovoltaic system using an adaptive artificial neural network: Proposition for a new sizing procedure," Renewable Energy, Elsevier, vol. 32(2), pages 285-313.
    • Handle: RePEc:eee:renene:v:32:y:2007:i:2:p:285-313
    DOI: 10.1016/j.renene.2006.01.002
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    References listed on IDEAS

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    1. Mellit, A. & Benghanem, M. & Arab, A. Hadj & Guessoum, A., 2005. "An adaptive artificial neural network model for sizing stand-alone photovoltaic systems: application for isolated sites in Algeria," Renewable Energy, Elsevier, vol. 30(10), pages 1501-1524.
    2. Sukamongkol, Y. & Chungpaibulpatana, S. & Ongsakul, W., 2002. "A simulation model for predicting the performance of a solar photovoltaic system with alternating current loads," Renewable Energy, Elsevier, vol. 27(2), pages 237-258.
    3. Koutroulis, Eftichios & Kalaitzakis, Kostas, 2003. "Development of an integrated data-acquisition system for renewable energy sources systems monitoring," Renewable Energy, Elsevier, vol. 28(1), pages 139-152.
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