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K. KARABACAK et al./ ISEM2016 Alanya-Turkey Conclusions For clean energy, dependence of fossil fuels must be reduced. For this purpose, renewable energy sources are in the limelight. For power generation of rural areas, photo voltaic power system applications are growing in whole world. Photovoltaic power system’s worst disadvantage is voltage drops or waves due to environmental conditions. This issue effects power quality. For this reason, many control technics are using for stabilizing output voltage of PV panels to gain maximum power from PV panels. In this study, an ANN controlled off-grid type photovoltaic power inverter is designed and experimented. The inverter consists of two stages. The first stage is boost converter for rising the input voltage up to desired value. The second stage is push-pull inverter circuit for dc/ac conversion for feeding ac off-grid loads. The system has 5kW power capacity. In result of the study, it’s shown that the proposed ANN controller has better performance then conventional controllers when changing environment conditions such as cloudy weathers on PV panels. References [1] Global Trends in renewable energy investment 2015, http://www.fs-unep-centre.org (Frankfurt am Main). [2] Femia N, Petrone G, Spagnuolo G, Vitelli M, Optimization of perturb and observe maximum power point tracking method, IEEE Transactions On Power Electronics, Vol. 20, No. 4, July 2005. [3] Abdalla I, Corda J, Zhang L, Optimal control of a multilevel DC-link converter photovoltaic system for maximum power generation, Renewable Energy, Volume 92, July 2016, Pages 1-11. [4] Mahmoud Y, Abdelwahed M, El-Saadany E, An Enhanced MPPT Method Combining Model- Based and Heuristic Techniques, IET Renewable Power Generation, Vol.10, No.4, Pages 551-560. [5] Belkaid A, Gaubert JP, Gherbi A, An Improved Sliding Mode Control for Maximum Power Point Tracking in Photovoltaic Systems, Control Engineering and Applied Informatics, Vol.18 Is.1, pages 86-94. [6] Saravanan S, Babu NR, Maximum power point tracking algorithms for photovoltaic system - A review, Renewable & Sustainable Energy Reviews, Vol.57, pages 192-204. [7] Ghaffarzadeh N, Bijani S, Dual surface sliding mode controller for photovoltaic systems enhanced by a ripple domain search maximum power point tracking algorithm for fast changing environmental conditions, IET Renewable Power Generation, Vol.10, No.5, pages 611-622. [8] Seyedmahmoudian M, Horan B, Rahmani R, AMT Oo, Stojcevski A, Efficient Photovoltaic System Maximum Power Point Tracking Using a New Technique, Energies, Vol.9, No.3. [9] Enany MA, Farahat MA, Nasr A, Modeling and evaluation of main maximum power point tracking algorithms for photovoltaics systems, Renewable & Sustainable Energy Reviews, Vol.58, pages 1578-1586. [10] Jena D, Ramana VV, Modeling of photovoltaic system for uniform and non-uniform irradiance: A critical review, Renewable & Sustainable Energy Reviews, Vol.52, pages 400-417. [11] Makhloufi S, Comparative study between classical methods and genetic algorithms for sizing remote PV systems, International Journal Of Energy And Environmental Engineering, Vol. 6, 584
K. KARABACAK et al./ ISEM2016 Alanya-Turkey No.3, pages 221-231. [12] Karabacak K, Cetin N, Artificial neural networks for controlling wind-PV power systems: A review, Renewable and Sustainable Energy Reviews, Vol. 29, pages 804-827. [13] Elobaid LM, Abdelsalam AK, Zakzouk EE, Artificial neural network-based photovoltaic maximum power point tracking techniques: a survey, IET Renewable Power Generation, Vol.9, No.8, pages 1043-1063. [14] Mutlag AH, Mohamed A, Shareef H, A comparative study of artificial intelligent-based maximum power point tracking for photovoltaic systems, International Conference On Advances In Renewable Energy And Technologies (ICARET 2016), Putrajaya, Malaysia, Vol. 32. [15] Khanaki R, Radzi MAM, Marhaban MH, Artificial neural network based maximum power point tracking controller for photovoltaic standalone system, International Journal of Green Energy, Vol. 13, No.3, pages 283-291. [16] Ljung L, Yuan ZD, Black-box Identification of Transfer Functions: Asymptotic Results for Increasing Model Order and Data Records, Ima Journal of Mathematical Control and Information, Vol. 1, No. 4, pages 323-338. 585
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