8a07a79c-dc92-42e2-9b8c-fe55991040a020201231065111882wseamdt@crossref.orgMDT DepositWSEAS TRANSACTIONS ON COMPUTER RESEARCH1991-875510.37394/232018http://wseas.org/wseas/cms.action?id=13372352020352020810.37394/232018.2020.8http://www.wseas.org/wseas/cms.action?id=23207Recurrent Neural Network Based MPPT Control of Grid Connected DFIG for Wind TurbineMohsenDavoudiDepartment of Electrical Engineering, Imam Khomeini International University, University Blvd., Qazvin, IRANAmin KasiriFarDepartment of Electrical Engineering, Imam Khomeini International University, University Blvd., Qazvin, IRANThis paper presents a new maximum-power-point-tracking (MPPT) controller in wind power generation using artificial neural networks (ANN) in order for making the wind turbine function in optimum working point and get high efficiency of wind energy conversion at different conditions. The algorithm uses fully connected recurrent neural network and is trained online using real-time recurrent learning (RTRL) algorithm in order to avoid the oscillation problem in wind-turbine generation systems. It generates control command for speed of the rotor side converter using optimal algorithm to enable the control system in order to track the maximum power point. The rotor speed and wind-turbine torque are the inputs of the networks, and the command signal for the rotor speed of wind turbine is the output. Simulation results verify the performance of the proposed algorithm.352020352020110https://www.wseas.org/multimedia/journals/computerresearch/2020/a025118-089.pdf10.37394/232018.2020.8.1http://www.wseas.org/multimedia/journals/computerresearch/2020/a025118-089.pdf10.1109/tec.2004.827038Tan K, Islam S. Optimum control strategies in energy conversion of PMSG wind turbine system without mechanical sensors. IEEE Trans Energy Convers, Vol 19, No. 2, pp. 392–399, 2004. 10.1109/tec.2004.827032Kolhe M, Joshi JC, Kothari DP. Performance analysis of a directly coupled photovoltaic water-pumping system. IEEE Trans Energy Convers, Vol.19, No.3, pp. 613–618, 2004. 10.1109/psec.2002.1022540Andersen GK, Klumpner C, Kjaer SB, Blaabjerg F. A new green power inverter for fuel cells, IEEE 33rd annual power electron specialists conf, pp. 727–33, 2002.10.1109/isie.2004.1571949Mirecki A., Roboam X. and Richardeau F., Comparative study of maximum power strategy in wind turbines, IEEE Transactions on Energy Conversion, pp. 993–998, 2004.10.1109/tia.2005.858282H. Li, K. L Shi, and P. G. McLaren, Neural network-based sensor-less maximum wind energy capture with compensated power coefficient, IEEE Trans. Industrial Applications, vol. 41, no. 6, pp. 1548-1156, 2005. 10.1109/tie.2014.2317143Manganiello, P.; Ricco, M.; Petrone, G.; Monmasson, E.; Spagnuolo, G. Optimization of Perturbative PV MPPT Methods through Online System Identification, IEEE Trans. Ind. Electron., Vol. 61, 6812–6821, 2014.T. Shanthi, A.S. Vanmukhil, ANFIS controller based MPPT control of photovoltaic generation system, Research journal of applied sciences, Vol. 8, No. 7, pp. 375-382, 2013. 10.1109/apec.2016.7468391Youngjong Ko, Holger Jedtberg, Giampaolo Buticchi, Marco Liserre, Topology and control strategy for accelerated lifetime test setup of DC-link capacitor of wind turbine converter, IEEE Applied Power Electronics Conference and Exposition (APEC) 2016, pp. 3629-3636, 2016.10.1016/j.solener.2012.11.017Y.-H. Liu, C.-L. Liu, J.-W. Huang, J.-H. Chen, Neural-network based maximum power point tracking methods for photovoltaic systems operating under fast changing environments, Solar Energy, Vol. 89, pp. 42-53, 2013.10.1109/icaee.2015.7506838M.M. Atiqur Rahman, A.H.M.A. Rahim, An efficient wind speed sensor-less MPPT controller using adaptive neuro-fuzzy inference system, Advances in Electrical Engineering (ICAEE) 2015 International Conference, pp. 230-233, 2015.10.1109/irsec.2018.8702845Soro S. Martin, Ahmed Chebak, Abderazak El Ouafi, Mustapha Mabrouki, An Efficient Fuzzy Logic Based MPPT Control Strategy for Multi-Source Hybrid Power System, 6th International Conference on Renewable and Sustainable Energy (IRSEC), pp. 1-8, 2018.10.1016/j.egypro.2011.05.062F. Chekired, C. Larbes, D. Rekioua, F. Haddad, Implementation of a MPPT fuzzy controller for photovoltaic systems on FPGA circuit, Energy Procedia, vol. 6, pp. 541-549, 2011.10.1016/j.solener.2015.11.023R. Boukenoui, H. Salhi, R. Bradai, A. Mellit, A new intelligent MPPT method for stand-alone photovoltaic systems operating under fast transient variations of shading patterns, Solar Energy, vol. 124, pp. 124-142, 201510.1109/tec.2002.808346R. Datta and V. T Tanganathan, A method of tracking the peak power points for a variable speed wind energy conversion system," IEEE Trans. Energy Conv., Vol 18, No.1, pp. 163-168, 2003.10.1049/iet-rpg:20080065Syafaruddin; Karatepe, E.; Hiyama, T. Artificial neural network-polar coordinated fuzzy controller based maximum power point tracking control under partially shaded conditions, IET Renewable Power Generation, pp. 239–253, 2009. 10.1109/tii.2015.2489579Ahmed, J.; Salam, Z., An Improved Method to Predict the Position of Maximum Power Point during Partial Shading for PV Arrays, IEEE Trans. Ind. Inform., pp. 1378–1387, 2015. 10.1109/tie.2006.870658E. Koutroulis and K. Kalaitzakis, Design of a maximum power tracking system for wind-energy-conversion applications, IEEE Trans. Ind . Electron., Vol. 53, No. 12, pp. 486-494, 2006.10.1109/redec.2014.7038550Rana Ahmed, A. Naaman, N. K. M’Sirdi, A. K. Abdelsalam, Y. G. Dessouky, Sensorless MPPT technique for PMSG micro wind turbines based on state-flow, International Conference on Renewable Energies for Developing Countries (REDEC), pp. 26-27, 2014.Siegfried Heier, Grid Integration of Wind Energy Conversion Systems, John Wiley & Sons Ltd, 1998, ISBN 0-471-97143-X10.1109/tie.2017.2711571M. J. Z. Zadeh, S. H. Fathi, A new approach for photovoltaic arrays modeling and maximum power point estimation in real operating conditions, IEEE Transactions on Industrial Electronics, Vol. 64, pp. 9334-9343, 2017.10.2316/p.2011.756-068Ansel Barchowsky, Jeffrey P. Parvin, Gregory F. Reed, Matthew J. Korytowski, and Brandon M. Grainger, Central and Distributed MPPT Systems under Varying Weather Conditions, Power and Energy Systems and Applications (PESA 2011), USA, November 201110.1049/ip-epa:19960288R. Pena, J.C. Clare, G.M. Asher, Doubly fed induction generator using back-to-back PWM converters and its application to variable-speed wind-energy generation, IEEE Proc. Electr. Power Appl., Vol. 143, No. 3, 1996.10.1109/pes.2009.5275874Shuhui Li, Rajab Challoo, and Marty J. Nemmer, Comparative study of DFIG power control using stator-voltage and stator-flux oriented frames, IEEE power & energy society general meeting, July 2009 S. Haykin, Neural Networks and Learning Machines (3rd Edition), Prentice Hall, 2009.Mohsen Davoudi, Amin Kasiri far, Fully Connected Recurrent Neural Network MPPT Control Design For DFIG Wind Energy Conversion Systems, 2nd International Conference on Knowledge based Engineering and Innovation (KBEI), 2015. 10.1109/iecon.2012.6389333M. Tsai, C. Tseng and Y. Hung, A novel MPPT control design for wind-turbine generation systems using neural network compensator, IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society, Montreal, QC, pp. 3521-3526, 2012.10.1109/pes.2011.6039023S. Musunuri and H. L. Ginn, Comprehensive review of wind energy maximum power extraction algorithms, IEEE Power and Energy Society General Meeting, Detroit, MI, USA, pp. 1-8, 2011.10.1109/cca.2009.5281181J. S. Thongam, P. Bouchard, H. Ezzaidi and M. Ouhrouche, Artificial neural network-based maximum power point tracking control for variable speed wind energy conversion systems, IEEE Control Applications, (CCA) & Intelligent Control, (ISIC), St. Petersburg, pp. 1667-1671, 2009.10.1016/j.ijepes.2013.03.021Chih-Hong Lin, Recurrent modified Elman neural network control of PM synchronous generator system using wind turbine emulator of PM synchronous servo motor drive, International Journal of Electrical Power & Energy Systems Volume 52, Pages 143-160, 2013. Mohammad Rashed M. Altimania, MODELING OF DOUBLY-FED INDUCTION GENERATORS CONNECTED TO DISTRIBUTION SYSTEM BASED ON eMEGASim REAL-TIME DIGITAL SIMULATOR, University of Tennessee at Master of Engineering Thesis, Tennessee, May 2014. Daniil Naumetc, BUILDING THE ARTIFICIAL NEURAL NETWORK ENVIRONMENT-Artificial Neural Networks in plane control, Bachelor’s thesis, Valkeakoski Automation Engineering, HAME University of Applied Science, Fall 2016.10.24084/repqj18.406M.M. Atiqur Rahman, NEURAL NETWORK BASED MAXIMUM POWER POINT TRACKING AND CONTROL OF PMSG WIND SYSTEM, KING FAHD UNIVERSITY OF PETROLEUM & MINERALS DHAHRAN, Electrical Engineering Department, September 2014.