### An Intelligent Maximum Power Point Tracker for Photovoltaic Systems Based on Neural Network

^{(*)}

*Corresponding author*

**DOI's assignment:**

*the author of the article can submit here a request for assignment of a DOI number to this resource!*

**Cost of the service: euros 10,00 (for a DOI)**

#### Abstract

To maximize a photovoltaic (PV) system's output power, continuously tracking the maximum power point (MPP) of the system is necessary. The MPP depends on irradiance conditions, the panel's temperature, and the load connected. In this paper, an intelligent system for attaining maximum power point tracking of PV systems is proposed. In this method, two outputs of neural network are used to provide the optimum voltage and monitor the state of health of the photovoltaic installation at the same time. If there's a difference between the calculated power and the maximum power point, the second output of the neural network is set to 1 and an alarm is triggered. The method is tested in a 1.2kw PV system under several conditions (normal operating and default). Finally, the results of simulation are included and explained to validate the proposed techniques. *Copyright © 2013 Praise Worthy Prize - All rights reserved.*

#### Keywords

#### Full Text:

PDF#### References

Khiari, B., Sellami, A., Andoulsi, R., Mami, A., A non linear MPPT control of photovoltaic pumping system based on discrete sliding mode, (2012) International Review of Electrical Engineering (IREE), 7 (6), pp. 6129-6136.

Hohm P. and Ropp M. E., Comparative Study of Maximum Power Point Tracking Algorithms,Progress in Photovoltaics: Research and Applications, Vol. 11, 2003, pp 47-62.

Liu C., Wu B., and Cheung R., Advanced Algorithm for MPPT Control of Photovoltaic Systems,1st Canadian Solar Buildings.

El Khateb, A.H., Rahim, N.A., Selvaraj, J., Novel Cuk-buck MPPT battery charger for standalone PV-inverter applications, (2012) International Review of Electrical Engineering (IREE), 7 (2), pp. 3749-3758.

T.-Y. Kim, H.-G. Ahn, S. K. Park, and Y.-K. Lee, “A novel maximum power point tracking control for photovoltaic power system under rapidly changing solar radiation,IEEE Int. Symp. Ind. Electron., 2001, pp. 1011–1014.

W. Xiao, J. Lind, W. Dunford, and A Capel, Real-Time Identification of Optimal Operating Points in PhotovoltaicPower Systems, IEEE Transactions on Industrial Electronics, vol.. 53, no. 4, August 2006.

C.-Y. Won, D.-H. Kim, S.-C. Kim, W.-S. Kim, and H.-S. Kim,A new maximum power point tracker of photovoltaic arrays using fuzzy controller, in Proc. 25th Annu. IEEE Power Electron. Spec. Conf., 1994, pp. 396–403.

C. Larbes, S.M. Aıt Cheikh, T. Obeidi, A. Zerguerras, Genetic algorithms optimized fuzzy logic control for the maximum power point tracking in photovoltaic system, ScienceDirect Renewable Energy 34, January 2009, pp.2093–2100.

M. Veerachary, T. Senjyu, and K. Uezato, Neural- network-based maximum-power-point tracking of coupled-inductor interleaved-boost converter- supplied PV system using fuzzy controller, IEEE Trans. Ind. Electron., vol. 50, no. 4, pp. 749–758, Aug. 2003.

A. Torres, F. Antunes, F. Reis, An artificial neural network-based real time maximum power tracking controller for connecting a PV system to the grid,IEEE Annual Conf. Industrial Electronics Society(IECON’98), vol.1, pp. 554-558, Aug.-Sept.1998

Hiyama T, Kouzuma S, Imakubo T. Evaluation of neural network based real time maximum power tracking controller for PV system.,IEEE Trans Energy Conv 1995; 10(3):543–8.

T. Hiyama, S. Kouzuma, and T. Imakubo, Identification of optimal operating point of PV modules using neural network for real time maximum power tracking control,IEEE Trans. Energy Convers., vol. 10, no. 2,pp. 360–367, Jun. 1995.

A. Luque and S. Hegedus, Handbook of Photovoltaic Science and Engineering, John Wiley & Sons, 2003.

J. A. Gow and C. D. Manning, Development of a photovoltaic array model for use in power electronics simulation studies, IEE Proceedings- Electric Power Applications, vol. 146, no. 2, 1999, pp. 193-199.

K. S. Narendra and K. Parthasarthy. Identification and Control of Dynamical Systems using Neural Net- works”. IEEE trans. Neurcil Networks, vol. 1, no. 1, pp. 4-27, Mar 1990.

Cybenko, Approximations by Superposition of a Sigmoidal Function. Mathematics of Contr., Signals and Syst., vol. 2, pp. 303-314, 1989.

Aksoy, S., Mühürcü, A., Recurrent neural network based nonlinear state estimation for induction motors, (2011) International Review of Electrical Engineering (IREE), 6 (2), pp. 636-645.

M. Kuchar, P. Brandstetter, M. Kaduch, Sensorless Induction Motor Drive with Neural Network, IEEE Power Electronics Specialists Conference, Aachen, Germany, 2004.

Neural Network Toolbox™ User's Guide. hhttp://www.mathworks.com/help/pdf_doc/nnet/nnet_ug.pdf

M. T. Hagan and M. B. Menhaj, “Training feed forward networks with the Marquardt algorithm,” IEEE Trans. Neural Networks, vol. 5, pp.989–993, Nov. 1994.

http://am.suntech-power.com/.

### Refbacks

- There are currently no refbacks.

Please send any question about this web site to info@praiseworthyprize.com**Copyright © 2005-2024**** Praise Worthy Prize**