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In this paper, the optimum design for renewable energy system powered an aquaculture pond was determined. Hybrid Optimization Model for Electric Renewable (HOMER) software program, which is developed by U.S National Renewable Energy Laboratory (NREL), is used for analyzing the feasibility of the stand-alone and hybrid system in this study. HOMER program determines whether renewable energy resources satisfy hourly electric demand or not. The program calculates energy balance for every 8760 hours in a year to simulate operation of the system. This optimization compares the demand for the electrical energy for each hour of the year with the energy supplied by the system for that hour and calculates the relevant energy flow for each component in the model. The essential principle is to minimize the total system cost while HOMER ensures control of the system. Moreover the feasibility analysis of the energy system is also studied. Wind speed, solar irradiance, interest rate and capacity shortage are the parameters which are taken into consideration. The simulation results indicate that the hybrid system is the best choice in this study, yielding lower net present cost. Thus, it provides higher system performance than PV or wind stand-alone systems.

Fuel cells have become one of the major areas of research in the academia and the industry. The goal of most fish farmers is to maximize production and profits while holding labor and management efforts to the minimum. Risk of fish kills, disease outbreaks, poor water quality in most pond culture operations, aeration offers the most immediate and practical solution to water quality problems encountered at higher stocking and feeding rates. Many units of aeration system are electrical units so using a continuous, high reliability, affordable, and environmentally friendly power sources is necessary. Aeration of water by using PEM fuel cell power is not only a new application of the renewable energy, but also, it provides an affordable method to promote biodiversity in stagnant ponds and lakes. This paper presents a new design and control of PEM fuel cell powered a diffused air aeration system for a shrimp farm in Mersa Matruh in Egypt. Also Artificial intelligence (AI) techniques control is used to control the fuel cell output power by control input gases flow rate. Moreover the mathematical modeling and simulation of PEM fuel cell is introduced. A comparison study is applied between the performance of fuzzy logic control (FLC) and neural network control (NNC). The results show the effectiveness of NNC over FLC., {"references":["Abhishek Sakhare a, Asad Davari, Ali Feliachi,'' Fuzzy logic control of\nfuel cell for stand-alone and grid connection'', Journal of Power Sources\nVol., 135, PP., 165-176, 2004.","M.J. Khan, M.T. Iqbal, ''Analysis of a small wind-hydrogen stand-alone\nhybrid energy system\", Applied Energy, Vol., 86, PP., 2429-2442, 2009.","Soteris A. Kalogirou \"Artificial neural networks in renewable energy\nsystems applications: a review\", Renewable and Sustainable Energy\nReviews Vol. 5 pp. 373-401, 2001.","Abdel Ghani Aissaoui, \"a fuzzy logic controller for synchronous\nmachine'', Journal of Electrical Engineering, Vol. 58, pp. 285-290,\n2007.","D. Driankov, H. Hellendoorn, and M. Reinfrank, ''an introduction to\nfuzzy control'', Springer-Verlag, Berlin, Heidelberg, 1993.","Mark C. Williams, ''Fuel Cell Handbook'', fifth ed., EG&G Services\nParsons, Inc., 2000.","Colleen Spiegel, \"PEM fuel cell modeling and simulation using\nMATLAB\", Academic Press, 2008.","Energy Efficiency Guide for Industry in Asia -\nwww.energyefficiencyasia.org.","Austin Hughes, ''Electric motors and drives fundamentals, types and\napplications'', Newnes, 2006.\n[10] P. Thepsatom, A. Numsomran, V. TipsuwanpoM and T. Teanthong,\n''DC motor speed control using fuzzy logic based on Lab VIEW'', SICEICASE\nInternational Joint Conference 2006.\n[11] Kalogirou SA. ''Artificial intelligence for the modeling and control of\ncombustion processes: a review'', Prog Energy Combust Sci; Vol., 29,\n515-66, 2003.\n[12] C.W. Tao, ''Design of fuzzy-learning fuzzy controllers, fuzzy systems\nproceedings'', 1998. IEEE World Congress on Computational\nIntelligence.,.\n[13] Zhan Yuedong, Zhu Jianguo , Guo Youguang , Jin Jianxun, \"Control of\nproton exchange membrane fuel cell based on fuzzy logic\", Proceedings\nof the 26th Chinese Control Conference, 2007, China, IEEE.\n[14] Christina N. Papadimitriou and Nicholas A.Vovos, \"A Fuzzy Control\nScheme for Integration of DGs into a Microgrid \", IEEE, 2010.\n[15] Ahmed M. Ibrahim, \"Fuzzy logic for embedded systems application\",\nNewnes press, 2004.\n[16] M. Azouz, A. Shaltout and M. A. L. Elshafei, \"Fuzzy logic control of\nwind energy systems\", Proceedings of the 14th International Middle East\nPower Systems Conference (MEPCON-10), Egypt, 2010.\n[17] S. Lalouni, D. Rekioua, T. Rekioua and E. Matagne, \"Fuzzy logic\ncontrol of stand-alone photovoltaic system with battery storage\", Journal\nof Power Sources, Vol. 193, PP. 899-907, 2009.\n[18] Ch. Ben Salah, M. Chaaben, M. Ben Ammar, \"Multi-criteria fuzzy\nalgorithm for energy management of a domestic photovoltaic panel\",\nRenewable Energy Vol. 33, PP. 993 -1001, 2008.\n[19] Soteris A Kalogiroua, Soa Pantelioub, Argiris Dentsoras, \"Artificial\nneural networks used for the performance prediction of a thermosiphon\nsolar water heater\", Renewable Energy, Vol., 18, PP., 87-99, 1999.\n[20] James A. Freeman, David M. Skapura, ''Neural networks algorithms,\napplications, and programming techniques'', Addison-Wesley Publishing\nCompany, Inc., Paris, 1991.\n[21] M.N. Cirstea, A. Dinu, J.G. Khor, M. McCormick, \"Neural and fuzzy\nlogic control of drives and power systems\", Replika Press Delhi , India,\n2002.\n[22] Soteris A. Kalogirou, \"Prediction of flat-plate collector performance\nparameters using artificial neural networks\", Solar Energy, Vol., 80, PP.,\n248-259, 2006.\n[23] Adnan Sozen, Tayfun Menlik, Sinan Unvar, \"Determination of\nefficiency of flat-plate solar collectors using neural network approach\",\nExpert Systems with Applications, Vol., 35, PP., 1533-1539, 2008."]}

Designing and implementing intelligent systems has become a crucial factor for the innovation and development of better products of space technologies. A neural network is a parallel system, capable of resolving paradigms that linear computing cannot. Field programmable gate array (FPGA) is a digital device that owns reprogrammable properties and robust flexibility. For the neural network based instrument prototype in real time application, conventional specific VLSI neural chip design suffers the limitation in time and cost. With low precision artificial neural network design, FPGAs have higher speed and smaller size for real time application than the VLSI and DSP chips. So, many researchers have made great efforts on the realization of neural network (NN) using FPGA technique. In this paper, an introduction of ANN and FPGA technique are briefly shown. Also, Hardware Description Language (VHDL) code has been proposed to implement ANNs as well as to present simulation results with floating point arithmetic. Synthesis results for ANN controller are developed using Precision RTL. Proposed VHDL implementation creates a flexible, fast method and high degree of parallelism for implementing ANN. The implementation of multi-layer NN using lookup table LUT reduces the resource utilization for implementation and time for execution., {"references":["Amos R. Omondi, and Jagath C. Rajapakse, \"FPGA Implementations of\nNeural Networks\", Springer publisher, 2006.","Zainalabedin Navabi, \"Digital Design and Implementation with Field\nProgrammable Devices\", Kluwer Academic Publishers, New York,\n2005.","Mehmet Tukel And Mustak Erhan Yalcin, \"A New Architecture for\nCellular Neural Network on Reconfigurable Hardware with an Advance\nMemory Allocation Method, in proc. of IEEE 2010 12th International\nWorkshop on Cellular Nanoscale Networks and their Applications\n(CNNA), 2010","Douglas L. Perry , \"VHDL: Programming by Example\", The McGraw-\nHill Companies, Inc. , 2002.","Wiley J. Larson, and James R. Wertz, Spacecraft Mission Analysis and\nDesign, Micrcosm Press, Elo, Segrund, California, 2008.","Mukund R. Patel, Spacecraft Power Systems, CRC Press, Boca Raton,\nFlorida, 2005.","Alexander Gomperts, Abhisek Ukil, and Franz Zurfluh, \"Development\nand Implementation of Parameterized FPGA-Based General Purpose\nNeural Networks for Online Applications\", IEEE TRANSACTIONS ON\nINDUSTRIAL INFORMATICS, 2010.","A. Mellit, H. Rezzouk, A. Messai, B. Medjahed, \"FPGA-based real time\nimplementation of MPPT-controller for photovoltaic Systems\",\nRenewable Energy Vol. 36, PP. 1652-1661, 2011.","Mellit A, Mekki H, Messai A, Salhi H., \"FPGA-based implementation\nof an intelligent simulator for stand-alone photovoltaic system. Expert\nSystems with Applications\", Vol. 37, No.8, PP. 6036-6051, 2010.\n[10] Deming Chen, Jason Cong, and Peichan Pan, \"FPGA Design\nAutomation: A Survey\",now Publishers Inc. , The Netherlands, 2006.\n[11] Ian Grout, \"Digital Systems Design with FPGAs and CPLDs\", Elsevier\n(Newnes press) publications, 2008.\n[12] Nasri Sulaiman, Zeyad Assi Obaid, M. H. Marhaban and M. N.\nHamidon, \"Design and Implementation of FPGA-Based Systems - A\nReview\", Australian Journal of Basic and Applied Sciences, Vol. 3, No.\n4, PP. 3575-3596, 2009.\n[13] Janardan Misra , IndranilSaha , \"Artificial neural networks in hardware:\nA survey of two decades of progress\", Journal of Neurocomputing, Vol.\n74, PP. 239-255, 2010.\n[14] David W. Bishop, VHDL-2008 Support Library, published by EDA\nIndustry Working Groups and Accellera, http://www.vhdl.org/fphdl/,\nonline 2010.\n[15] H. Mekki, A. Mellit, H.Salhi, and B. Khaled, \"Modeling and simulation\nof photovoltaic panel based on artificial neural networks and VHDLlanguage\",\nIEEE, 2007."]}