A New Controller to Enhance PV System Performance Based on Neural Network

Roshdy A AbdelRassoul, Yosra Ali, Mohamed Saad Zaghloul

Abstract


In recent years, a radical increase of photovoltaic (PV) power generators installation took place because of increased efficiency of solar cells, as well as the growth of manufacturing technology of solar panels. This paper shows the operation and modeling of photovoltaic systems, particularly designing neural controller to control the system. Neural controller is optimized using particle swarm optimization (PSO)   leads to getting the best performance of the designed PV system. Using neural network the maximum overshoot and rise time obtained become 0.00001% and 0.1798 seconds, respectively also this paper introduce a comparison between some kind of controller for PV system.In recent years, a radical increase of photovoltaic (PV) power generators installation took place because of increased efficiency of solar cells, as well as the growth of manufacturing technology of solar panels. This paper shows the operation and modeling of photovoltaic systems, particularly designing neural controller to control the system. Neural controller is optimized using particle swarm optimization (PSO)   leads to getting the best performance of the designed PV system. Using neural network the maximum overshoot and rise time obtained become 0.00001% and 0.1798 seconds, respectively also this paper introduce a comparison between some kind of controller for PV system.

Keywords


Particle Swarm Optimization, neural network and photovoltaic

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References


Pandiarajan, N.; Muthu, Ranganath. Mathematical modeling of photovoltaic module with Simulink. In: Proceeding of International Conference on Electrical Energy System. 2011. pp. 3-5.‏

Qi, Chen; Ming, Zhu. Photovoltaic module Simulink model for a stand-alone PV system. physics procedia, 2012, 24: pp. 94-100.‏

Sahin, Erol; Ayas, Mustafa Sinasi; Altas, Ismail Hakki. A PSO optimized fractional-order PID controller for a PV system with DC-DC boost converter. In:Power Electronics and Motion Control Conference and Exposition (PEMC), 2014 16th International. IEEE, 2014. pp. 477-481.‏

Chowdhury, Ahmed Sony Kamal; Salam, K. M. A.; Razzak, M. Abdur. Modeling of MATLAB-Simulink based photovoltaic module using flyback converter. In: Strategic Technology (IFOST), 2014 9th International Forum on. IEEE, 2014. pp. 378-381.‏

Brigitte Hauke, “Low power DC-DC application/Basic calculation of a boost converter’s power stage”, Texas Instrument application report, July, 2010.

L.Y. Chang, H.C. Chen, “Tuning of fractional PID controllers using adaptive genetic algorithm for active magnetic bearing system”, WSEAS Transactions on Systems, Vol. 8, 2009, pp. 226-236.

A. Biswas, S. Das, A. Abraham, S. Dasgupta, “Design of fractionalorder P IλDµ controllers with an improved differential evolution”, Engineering Applications of Artificial Intelligence, Vol. 22, 2009, pp. 343- 350.

M. Zamania, M. Karimi-Ghartemanib, N. Sadatib, M. Parnianib,, “Design of a fractional order PID controller for an AVR using particle swarm ptimization”, Control Engineering Practice, Vol. 17, 2009, pp. 1380-1387.

Tu, Jack V. Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. Journal of clinical epidemiology 49, no. 11 (1996): 1225-1231.

SETTLES, Matthew. An introduction to particle swarm optimization. Department of Computer Science, University of Idaho, 2005, 1-8.‏

Roshdy A AbdelRassoul, Yosra Ali and Mohamed Zaghloul, Genetic Algorithm-Optimized PID controller for Better Performance of PV System Int. Conf. Conference on Artificial Intelligence (ICCAI’ 2016), 2016 World Symposium on Computer Applications & Research, WSCAR 2016, Cairo, Egypt, 12-14 March, 2016.




DOI: http://dx.doi.org/10.21622/resd.2017.03.2.224

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