NDLI: Optimal operation of photovoltaic/utility grid interconnected electrical power system using neural network

Content Provider	IEEE Xplore Digital Library
Author	El-Tamaly, H.H. Elbaset Mohammed, A.A.
Copyright Year	2006
Description	Author affiliation: Fac. of Eng., Univ. Elminia, Elminia, Egypt (El-Tamaly, H.H.; Elbaset Mohammed, A.A.)
Abstract	The performance of photovoltaic power system interconnected with utility grid can be improved through an application of advanced control method. This paper introduces an application of an artificial neural network on the operation control of the photovoltaic/utility grid to improve system efficiency and reliability. The generated power from photovoltaic solar cells array has been calculated by a computer program under known insolation and temperature. The computer program which proposed here and applied to carry out these calculations is based on the minimization of the power purchase from grid. This paper focus on a hybrid system consists of photovoltaic system accompanied with battery storage interconnected with utility grid taking into account the variation of solar radiation and load demand during the day. Different feed forward neural network architectures are trained and tested with data containing a variety of operation patterns. A simulation is carried out over one year using the hourly data of the load demand, insolation and temperature at El'Zafranna site, Egypt as a case study. The results show that the selected neural network architecture (5+9+4) gives reasonably accurate operation of photovoltaic/utility grid accompanied with battery storage.
Starting Page	317
Ending Page	322
File Size	2551136
Page Count	6
File Format	PDF
ISBN	9781424451111
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-12-19
Publisher Place	Egypt
Access Restriction	Subscribed
Rights Holder	El-Minia University
Subject Keyword	Photovoltaic systems Temperature Utility grid Operation strategy Artificial neural networks Photovoltaic system Control systems Batteries Neural network Solar power generation Application software Power system interconnection Neural networks Power system reliability
Content Type	Text
Resource Type	Article

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