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Artificial Neural Network Model of a 25 kW (Peak) Grid Connected Solar Photovoltaic Power Plant

M. Rizwan Khan*, Atif Iqbal**, Imtiaz Ashraf***

*Research Scholar&Lecturer,Department of Electrical Engg,Alligarh Muslim University,Aligarh,India.

**Reader ,Department of Electrical Engg,Alligarh Muslim University,Aligarh,India

***Associate Professor,Department of Electrical Engg,Alligarh Muslim University,Aligarh,India.

Periodicity:January - March'2008
DOI : https://doi.org/10.26634/jee.1.3.415

Abstract

This paper present an artificial neural network (ANN) approach for forecasting the electric energy output from a 25-kWp grid connected solar photovoltaic power plant (SPVPP) installed at Vibhuti Khand, Lucknow, Utter Pardesh, India. The main aim is to develop a model of the system using artificial neural network (ANN) that can accurately forecast electrical energy output generated from the grid connected solar PV system. The ANN interpolates among the solar PV generation output and relevant parameters such as average solar insolation, average module temperature and average humidity . In this study, an ANN model is implemented and validated with reasonable accuracy on real electric energy generation output data. The physical layout and salient features of the power plant is also reported. The proposed ANN method can be extended to any solar photovoltaic power plant (SPVPP) for forecasting energy generation.

Keywords

Neural network, Photovoltaic, Energy.

How to Cite this Article?

M. Rizwan Khan, Atif Iqbal and Imtiaz Ashraf (2008). Artificial Neural Network Model of a 25 kW (Peak) Grid Connected Solar Photovoltaic Power Plant. i-manager’s Journal on Electrical Engineering, 1(3), Jan-Mar 2008, Print ISSN 0973-8835, E-ISSN 2230-7176, pp. 19-24. https://doi.org/10.26634/jee.1.3.415

References

[1] , Annual Report 2002-2003, Ministry of Non- conventional Energy sources. Government of India. Http://www. mnes. nic. in.

[2] . Imtiaz Ashraf, A. Chandra, Application of Artificial Neural Network in Forecasting Electrical Energy Generation for Grid Connected Solar PV system. Proceedings of 6th International Conference of the Association of Asia-Pacific Operation Research Societies within International Federation of Operation Research Societies (APORS 2003), on Operational Research: Emerging Paradigms for Information Technology, The Operation Research Society of India, New Delhi, India, Dec. 08-11,2003.

[3] . M. Ayyub, Seema Kaushik, Atif Iqbal and M. Rizwan Khan, Modeling of a lOOkW (peak) Kalyanpur Solar Photovoltaic Power Plant using Artificial Neural Network. Proceedings of the twelfth National Power Systems Conference vol 1, Dec 27-29,2002,, p. 418-420.

[4] . Imtiaz Ashraf, A. Chandra, Artificial Neural Network Based Forecasting of Performance for Grid Connected Solar PV system. Proceedings of the HiPC Workshops 2003, International Conference on High Performance Computing, Hyderabad, India, Dec.07,2003, pp. 1 -8.

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[6] . D.C. Park, R.J. El-Sharkawi and RJ. Marks II. Electric load forecasting using an Artificial Neural Network . IEEE transaction on Power System, Vol. 6, No. 2,1999, pp, 442- 449.

[7] . G.E. Nasr, E.A Badr and M.R. Younes, Neural Network in forecasting electrical energy consumption: univariate and multivariate approaches. International Journal of Energy Research, Vol. 26,2002, pp. 67-78.

[8] . A. Sfetsos and A.H. Coonick, Univariate and multivariate forecasting of hourly solar radiation with artificial intelligent techniques. Solar Energy, Vol. 68, No. 2, 2001, pp. 169-178

[9] . I.Ashraf, Atif Iqbal, M.S.J.Asghar, Performance Evaluation of an Experimental 1 OOkw (peak) Kalyanpur Solar Photovoltaic Power Plant-a case study. Proceedings of IEEE conference on PEDES, vol 2, Jan7-10, 1996, pp. 107-113.

Artificial Neural Network Model of a 25 kW (Peak) Grid Connected Solar Photovoltaic Power Plant

Abstract

Keywords

How to Cite this Article?

References

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