Earth Station Neural Network Control Methodology and Simulation
Renewable energy resources are inexhaustible, clean as compared with conventional resources. Also, it is used to supply regions with no grid, no telephone lines, and often with difficult accessibility by common transport. Satellite earth stations which located in remote areas are the most important application of renewable energy. Neural control is a branch of the general field of intelligent control, which is based on the concept of artificial intelligence. This paper presents the mathematical modeling of satellite earth station power system which is required for simulating the system.Aswan is selected to be the site under consideration because it is a rich region with solar energy. The complete power system is simulated using MATLAB–SIMULINK.An artificial neural network (ANN) based model has been developed for the optimum operation of earth station power system. An ANN is trained using a back propagation with Levenberg–Marquardt algorithm. The best validation performance is obtained for minimum mean square error. The regression between the network output and the corresponding target is equal to 96% which means a high accuracy. Neural network controller architecture gives satisfactory results with small number of neurons, hence better in terms of memory and time are required for NNC implementation. The results indicate that the proposed control unit using ANN can be successfully used for controlling the satellite earth station power system.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1071674Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1582
 Bruce R. Elbert, "The Satellite Communication Ground Segment and Earth Station Handbook", Artech House, Inc., London, 2001.
 Kamilo Feher, "Digital Communications: Satellite/Earth Station Engineering", Noble Publishing Classic, 1997.
 Kalogirou Soteris, "Artificial Intelligence in Energy and Renewable Energy Systems", Nova Publisher, 2007.
 Ali Al-Alawi, Saleh M Al-Alawi, and Syed M Islam, "Predictive Control of an Integrated PV-diesel Water and Power Supply System Using an Artificial Neural Network", Renewable Energy Journal , Vol. 32, pp. 1426-1439, 2007.
 Felix A. Farret, And M. Godoy Sim├Áes, "Integration of Alternative Sources of Energy", John Wiley & Sons, Inc., 2006.
 H. S. Rauschenbach, "Solar Cell Array Design Handbook", Litton Educational Publishing, 1980.
 C. Hua, and C. Shen, "Study of Maximum Power Tracking Techniques and Control of DC/DC Converters for Photovoltaic Power System", Proceedings of the 29th Annual IEEE Power Electronics Specialists Conference, 1998.
 G.J. Yu, et al., "A Novel Two-mode MPPT Control Algorithm Based on Comparative Study of Existing Algorithms", Solar Energy, Vol. 76 , pp.455-463, 2004.
 Bogdan, S. B. and Salameh, Z. M., "Methodology for Optimally Sizing the Combination of a Battery Bank and PV Array In a Wind/PV Hybrid System", IEEE Transactions on Energy Conversion, Vol. 11, No. 2, pp. 367-375, 1996.
 Bin, A., Hongxing, Y., Shen, H., Xianbo, L., "Computer Aided Design for PV/Wind Hybrid System", Renewable Energy, Vol. 28, pp. 1491- 1512, 2003.
 B. Chuco Paucar, J.L. Roel Ortiz, K.S. Collazos L., L.C.Leite, and J.O.P Pinto, "Power Operation Optimization of Photovoltaic Stand Alone System with Variable Loads Using Fuzzy Voltage Estimator and Neural Network Controller," IEEE Power Tech. , 2007.
 Adel Mellita, Mohamed Benghanemb, "Sizing of Stand-alone Photovoltaic Systems Using Neural Network Adaptive Model", Desalination Journal,Vol. 209, PP. 64-72, 2007.