Improvement of Microgrid Dynamic Performance under Fault Circumstances using ANFIS for Fast Varying Solar Radiation and Fuzzy Logic Controller for Wind System

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Abstract

The microgrid (MG) technology integrates distributed generations, energy storage elements and loads. In this paper, dynamic performance enhancement of an MG consisting of wind turbine was investigated using permanent magnet synchronous generation (PMSG), photovoltaic (PV), microturbine generation (MTG) systems and flywheel under different circumstances. In order to maximize the output of solar arrays, maximum power point tracking (MPPT) technique was used by an adaptive neuro-fuzzy inference system (ANFIS); also, control of turbine output power in high speed winds was achieved using pitch angle control technic by fuzzy logic. For tracking the maximum point, the proposed ANFIS was trained by the optimum values. The simulation results showed that the ANFIS controller of grid-connected mode could easily meet the load demand with less fluctuation around the maximum power point. Moreover, pitch angle controller, which was based on fuzzy logic with wind speed and active power as the inputs, could have faster responses, thereby leading to flatter power curves, enhancement of the dynamic performance of wind turbine and prevention of both frazzle and mechanical damages to PMSG. The thorough wind power generation system, PV system, MTG, flywheel and power electronic converter interface were proposed by using Mat-lab/Simulink.

[1] Strauss P., Engler A., AC coupled PV hybrid systems and MGs-state of the art and future trends. Proceedings, IEEE 3rd World Conference on Photovoltaic Energy Conversion, Osaka, Japan, pp. 2129-2134 (2003).

[2] Gao D., Jiang J., Qiao S.H., Comparing the use of two kinds of droop control under microgrid islanded operation mode. Archives of Electrical Engineering 62(2): 321-331 (2013).

[3] Lasseter R.H., Piagi P., MicroGrid: a conceptual solution. Power Electronics Specialists Conference, IEEE 35th Annual, Aachen, Germany; 6: 4285-4290 (2004).

[4] Xiao Zh., Wu J., Jenkins N., An Overview Of Microgrid Control. Intelligent Automation & Soft Computing 16: 199-212 (2010).

[5] Hayrettin C., Model of a photovoltaic panel emulator in MATLAB-Simulink. Turk. J. Elec. Eng. & Comp. Sci. 21: 301-308 (2013).

[6] Salas V., Olias E., Barrado A., Lazaro A., Review of the maximum power point tracking algorithms for stand-alone photovoltaic systems, Solar Energy Materials and Solar Cells 90: 1555-1578 (2006).

[7] Villalva M.G., Gazoli J.R., Filho E., Comprehensive Approach to Modeling and Simulation of Photovoltaic Arrays. Power Electronics IEEE Transactions on 24: 1198-1208 (2009).

[8] Chu C.C., Chen C.L., Robust maximum power point tracking method for photovoltaic cells: a sliding mode control approach. Solar Energy 83: 1370-1378 (2009).

[9] Liu FF., Duan S., Liu B., Kang Y.A., Variable step size INC MPPT method for PV systems. IEEE Trans. Industrial Electronics 55: 622-2628 (2008).

[10] Altin N., Type-2 Fuzzy Logic Controller Based Maximum Power Point Tracking in Photovoltaic Systems. Advances in Electrical and Computer Engineering 13: 65-70 (2013).

[11] Gasbaoui B., Abdelkader C.H., Adellah L., Multi-input multi-output fuzzy logic controller for utility electric vehicle. Archives of Electrical Engineering 60: 239-256 (2011).

[12] Veerachary M., Senjyu T., Uezato K., Neural-network-based maximum-power-point tracking of coupled inductor interleaved-boost-converter-supplied PV system using fuzzy controller. IEEE Transactions on Industria lElectronics 50: 749-758 (2003).

[13] Rai A., K, Kaushika N.D., Singh B., Agarwal N., Simulation model of ANN based maximum power point tracking controller for solar PV system. Solar Energy Materials and Solar Cells 95: 773-778 (2011).

[14] Cernazanu C., Training Neural Networks Using Input Data Characteristics. Advances in Electrical and Computer Engineering 8: 65-70 (2008).

[15] Esram T, Chapman P.L., Comparison of photovoltaic array maximum power point tracking techniques. IEEE Transactions on Energy Conversion 22(2): 439-449 (2007).

[16] Lee S., Kim J., Cha H., Design and Implementation of Photovoltaic Power Conditioning System using a Current-based Maximum Power Point Tracking. Journal of Electrical Engineering & Technology 5: 606-613 (2010).

[17] Hiyama T., Kitabayashi K., Neural Network Based Estimation of Maximum Power Generation from PV Module Using Environment Information, IEEE Transaction on Energy Conversion 12: 241-247 (1997).

[18] Aldobhani A.M.S., John R., Maximum power point tracking of PV system using ANFIS prediction and fuzzy logic tracking. IEEE Proceedings of the international multi conference of engineer is and computer scientists IMECS, Hong Kong, pp. 19-21 (2008).

[19] Abu-Ruba H., Iqbalbc A., Ahmeda Sk. M., Adaptive neuro-fuzzy inference system-based maximum power point tracking of solar PV modules for fast varying solar radiations, International Journal of Sustainable Energy 31: 383-398 (2012).

[20] Afghoul H., Krim F., Chikouche S., Increase the photovoltaic conversion efficiency using Neurofuzzy control applied to MPPT. IEEE Renewable and Sustainable Energy Conference IRSEC, Ouarzazate, pp. 348-353 (2013).

[21] Hayatdavudi M., Saeedimoghadam M., Nabavi M.H., Adaptive Control of Pitch Angle of Wind Turbine using a Novel Strategy for Management of Mechanical Energy Generated by Turbine in Different Wind Velocities. Journal of Electrical Engineering & Technology 8: 863-871 (2013).

[22] Pourfar I., Shayanfar H.A., Shanechi H.M., Naghshbandy A.H., Controlling PMSG-based wind generation by a locally available signal to damp power system inter-area oscillations. International Transactions On Electrical Energy Systems 23: 1156-1171 (2013).

[23] Yuan Lo K., Chen Y., Chang Y., MPPT Battery Charger for Stand-Alone Wind Power System, IEEE Transactions on Power Electronics 26:1631-1638 (2011).

[24] Gaurav N., Kaur A., Performance Evaluation of Fuzzy Logic and PID Controller by Using MATLAB/Simulink, International Journal of Innovative Technology and Exploring Engineering (IJITEE) 1: 84-88 (2012).

[25] Lingfeng X., Xiyun Y., Xinran L., Daping X., Based on adaptive fuzzy sliding mode controller. IEEE in Intelligent Control and Automation WCICA 7th World Congress on China, Chongqing, pp. 2970-2975 (2008).

[26] Amendola C.A.M., Gonzaga D.P., Fuzzy-Logic Control System of a Variable-Speed Variable-Pitch Wind-Turbine and a Double-Fed Induction Generator. IEEE Intelligent Systems Design and Applications, Seventh International Conference, Brazil, pp. 252-257 (2007).

[27] Senjyu T., Sakamoto R., Urasaki N., Funabashi T., Sekine H., Output power leveling of wind farm using pitch angle control with fuzzy neural network. IEEE Power Engineering Society General Meeting, Japan, pp. 1-8 (2006).

[28] Yao X., Guo Ch., Xing Z., Li Y., Liu Sh., Variable Speed Wind Turbine Maximum Power Extraction Based on Fuzzy Logic Control. IEEE International Conference on Intelligent Human-Machine Systems and Cybernetics, China, pp. 202-205 (2009).

[29] Gaonkar D.N., Patel R.N., Pillai G.N., Dynamic Model of MTG Generation System for Grid-connected/Islanding Operation. IEEE International Conference, India, pp. 305-310 (2006).

[30] Qi H.Y., Yi F.B., Feng S.J., Simulation research on the microgrid with flywheel energy storage system. Power System Protection and Control 39: 83-87 (2011).

[31] Li W., Yun-li ZH., Wei-dong L., Simulation System of Flywheel Energy Storage. Power System and Clean Energy 26: 102-106 (2010).

[32] Zhaoxia X., Chengshan W., Shouxiang W., Small-signal Stability Analysis of MG Containing Multiple Micro Sources. Automation of Electric Power Systems 33: 81-85 (2009).

[33] Kanellos F., Tsouchnikas A.I., Hatziargyriou N.D., Micro-grid simulation during grid-connected and islanded modes of operation. IPST Presented at the Int. Conf. Power Systems Transients (IPST), Montreal, Canada, IPST, pp. 105-113 (2005).

[34] Lopes J., Moreira C.L., Madureira A.G., Defining control strategies for micro grids islanded operation, IEEE Trans. Power Syst. 21: 916-924 (2006).

[35] Katiraei F., Irvani M., Lehn P., Micro-grid autonomous operation during and subsequent to islanding process, IEEE Trans. Power Del. 20: 248-257 (2005).

[36] Zamora R., Srivastava A.K., Controls for Micro-grids with Storage: Review, Challenges, and Research Needs. Elsevier 14: 2009-2018 (2010).

[37] Moradian M., Tabatabaei F.M., Moradian S., Modeling, Control & Fault Management of MGs. Smart Grid and Renewable Energy 4: 99-112 (2013).

[38] Kamel R.M., Chaouachi A., Nagasaka K., Detailed Analysis of Micro-Grid Stability during Islanding Mode under Different Load Conditions, Engineering 3: 508-516 (2011).

[39] Vincheh R.M., Kargar A., Markadeh Gh.A., A Hybrid Control Method for Maximum Power Point Tracking (MPPT) in Photovoltaic Systems, Arabian J. Sci. Eng. 39:4715-4725 (2014).

[40] Ramaprabha R., Mathur B.L., Intelligent Controller based Maxi-mum Power Point Tracking for Solar PV System. Intern. J. Comp. Appl. 12(10): 37-41 (2011).

[41] Yang J, Honavar V., Feature subset selection using a genetic algorithm. IEEE Intelligent Systems 13 (2): 44-49 (1998).

[42] Oguz Y., Guney I., Adaptive neuro-fuzzy inference system to improve the power quality of variable-speed wind power generation system. Turk. J. Elec. Eng. & Comp. Sci. 18: 625-645 (2010).

[43] Arifujjaman Md., Modeling, Simulation and Control of Grid Connected Permanent Magnet Generator (PMG)-based Small Wind Energy Conversion System. IEEE Electrical Power & Energy Conference, Canada, pp. 1-6 (2010).

[44] Rosyadi M., Muyeen S.M., Takahashi R., Tamura J., Transient stability enhancement of variable speed permanent magnet wind generator using adaptive pi-fuzzy controller, Proceedings, Trondheim Power Tech. Conf., Germany, pp. 1-6 (2011).

[45] Blaabjerg F., Teodorescu R., Liserre M., Tim-bus A.V., Overview of Control and Grid Synchronization for Distributed Power Generation Systems. IEEE Transactions on Industrial Electronics 53: 1398-1409 (2006).

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CiteScore 2016: 0.71

SCImago Journal Rank (SJR) 2016: 0.238
Source Normalized Impact per Paper (SNIP) 2016: 0.535

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