Design and Implementation of a Neural Network Applied to the Maximum Power Point Tracking of a Solar Panel

Abstract:

This paper shows the design and implementation of a neural network using back propagation method in order to perform the tracking of the maximum power point of a solar panel; this can be achieved by the use of the predictive ability of the network which uses light sensors to perform angular movement of the panel to find the optimum position. Tests were performed both in artificial and real environments; the network tracking sensitivity was tested in the artificial environment and it gave a result of less than 8 degrees of error, on the other hand in terms of voltage an improvement of more than 25% was found on the tracking configuration against an static configuration. As for the real environment testing, the tracking achieved to find the value of maximum power with a difference smaller than 4% of the maximum power measurement obtained.

• Periodical: Applied Mechanics and Materials (Volume 823)
• Pages: 383-388
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.823.383
• Online since: January 2016
• Authors: Mauticio Mauledoux, Santiago Fernández Posada, Oscar Avilés Sánchez
• Keywords: Activation Function, Back Propagation, Gradient Descent, Maximum Power Point Tracking, Neural Networks, Quality Function Deployment (QFD), Radial Base Function, Training Coefficient

References

[1] H. Ravishankar Kamath, R. S Althal, P. K Singh Ashls Kumar, Sinha and Atlt R Danak, Modeling of Photovoltaic Array and Maximun Power Point Tracker Using ANN, from Website: http: /journal. esrgroups. org/jes/papers/4_3_4. pdf.

[2] A.A. Argiriou, I. Bellas-Velidis and C.A. Balaras, Development of a neural network heating controller for solar buildings, Neural Networks 13 (2000) 811-820.

DOI: 10.1016/s0893-6080(00)00057-5

[3] Simoes, M.G.; Franceschetti, N.N., Fuzzy optimisation based control of a solar array system, Electric Power Applications, IEE Proceedings - , vol. 146, no. 5, p.552, 558, Sep (1999).

DOI: 10.1049/ip-epa:19990341

[4] Naoufel Khaldi, Hassan Mahmoudi, Malika Zazi, Youssef Barradi, Modelling and Analysis of Neural Network and Incremental Conductance MPPT Algorithm for PV Array Using Boost Converter, Advances in environmental technology and biotechnology, WSEAS Proceedings, Brasov, Romania June 26-28, pp.148-153, (2014).

DOI: 10.1109/irsec.2014.7059894

[5] Moufdi Hadjab, Smail Berrah and Hamza Abid, Neural network for modeling solar panel, International journal of energy, Issue 1, Vol. 6, pp.9-16, (2012).

[6] R. Rojas, Neural Networks a Systematic Introduction, Springer-Verlag, Berlin, (1996).

[7] Bekir Karlik and A. Vehbi Olgac, Performance Analysis of Various Activation Functions in Generalized MLP Architectures of Neural Networks, International Journal of Artificial Intelligence And Expert Systems (IJAE), Volume (1): Issue (4), p.111 – 122, February (2011).

[8] Vivianne Bouchereau and Hefin Rowlands, Methods and techniques to help quality function deployment (QFD), Benchmarking: An International Journal, Vol. 7, Iss: 1, p.8 – 20.

DOI: 10.1108/14635770010314891