Neurofuzzy robust backstepping based MPPT control for photovoltaic system
Neurofuzzy robust backstepping based MPPT control for photovoltaic system
Linear maximum power point tracking (MPPT) techniques are unable to achieve the desired performance and efficiency under wide variation in atmospheric conditions (temperature and irradiance) and consequently the maximum power point (MPP). Hence, the design and implementation of a nonlinear MPPT controller is essential to address the problems associated with the variations of the MPP. In this research article, a new nonlinear robust backstepping-based MPPT control technique is proposed for a standalone PV array connected to a dynamic load, and its performance comparison with existing backstepping, integral backstepping and conventional proportional integral derivative (PID) and perturb and observe (P&O) based MPPT techniques is provided. Simulations, performed in Matlab/Simulink platform, verify the effectiveness of the proposed MPPT technique and demonstrate its superior performance to the backstepping, integral backstepping and conventional MPPT techniques under simultaneous variation in irradiance and temperature and certain faults occurring in the system
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- 1] Mohanty M, Selvakumar S, Koodalsamy C, Simon SP. Global maximum operating point tracking for PV system using fast convergence firefly algorithm. Turkish Journal of Electrical Engineering and Computer Science 2019; 27(6): 4640-4658. doi: 10.3906/elk-1805-108
- [2] Nagarani B, Nesmony J. Performance enhancement of photovoltaic system using genetic algorithm-based maximum power point tracking. Turkish Journal of Electrical Engineering and Computer Science 2019; 27(4): 3015-3025. doi: 10.3906/elk-1801-189
- [3] Subudhi B, Pradhan R. A comparative study on maximum power point tracking techniques for photovoltaic power systems. IEEE Trans. Sustainable Energy 2012; 4(1): 89-98. doi: 10.1109/TSTE.2012.2202294
- [4] Roy TK, Mahmud MA, Oo AMT, Bansal R, Haque ME. Nonlinear Adaptive Backstepping Controller Design for Three-Phase Grid-Connected Solar Photovoltaic Systems. Electric Power Components and Systems 2017; 45(20): 2275-2292. doi: 10.1080/15325008.2018.1431334
- [5] Kim, Il-Song. Sliding mode controller for the single-phase grid-connected photovoltaic system. Applied Energy 2006; 83(10): 1101-1115. doi: 10.1016/j.apenergy.2005.11.004
- [6] Mahmud MA, Pota HR, Hossain MJ, Roy NK. Robust partial feedback linearizing stabilization scheme for three- phase grid-connected photovoltaic systems. IEEE J. Photovoltaics 2013; 4(1): 423-431. doi: 10.1109/JPHO- TOV.2013.2281721
- [7] Kotsopoulos A, Duarte JL, Hendrix MAM. Predictive DC voltage control of single-phase PV inverters with small DC link capacitance. In 2003 IEEE International Symposium on Industrial Electronics (Cat. No. 03TH8692); Rio de Janeiro, Brazil; 2003. pp. 793-797. doi: 10.1109/ISIE.2003.1267921.
- [8] Fadili AE, Giri F, Magri AE. Reference voltage optimizer for maximum power point tracking in triphase grid- connected photovoltaic systems. International Journal of Electrical Power & Energy Systems 2014; 60: 293-301. doi: 10.1016/j.ijepes.2014.03.029
- [9] Kim IS. Robust maximum power point tracker using sliding mode controller for the three-phase grid-connected photovoltaic system. Solar Energy 2007; 81(3): 405-414. doi: 10.1016/j.solener.2006.04.005
- [10] Naghmash, Armghan H, Ahmad I, Armghan A, Khan S, Arsalan M. Backstepping based non-linear con- trol for maximum power point tracking in photovoltaic system. Solar Energy 2018; 159: 134-141. doi: 10.1016/j.solener.2017.10.062
- [11] Arsalan M, Iftikhar R, Ahmad I, Hasan A, Sabahat K, Javeria A. MPPT for photovoltaic system using nonlinear backstepping controller with integral action. Solar Energy 2018; 170: 192-200. doi: 10.1016/j.solener.2018.04.061
- [12] Wang J, Bo D, Ma X, Zhang Y, Li Z, Miao Q. Adaptive back-stepping control for a permanent magnet synchronous generator wind energy conversion system. International Journal of Hydrogen Energy 2019; 44(5): 3240-3249. doi: 10.1016/j.ijhydene.2018.12.023
- [13] Krstic M, Kanellakopoulos I, Kokotović PV. Nonlinear and adaptive control design. John Wiley & Sons Inc., 1995.
- [14] Errami Y, Obbadi A, Sahnoun S, Benhmida M, Ouassaid M, Maaroufi M. Design of a nonlinear backstepping control strategy of grid interconnected wind power system based PMSG. AIP Conference Proceedings, pp. 030053, 2016. doi: 10.1063/1.4959449
- [15] Krstic M, Smyshlyaev A. Boundary control of PDEs: A course on backstepping designs. Society for Industrial and Applied Mathematics, Philadelphia, Pennsylvania, United States, 2008.
- [16] Hassan, Syed Zulqadar, Hui Li, Tariq Kamal, Uğur Arifoğlu, Sidra Mumtaz, and Laiq Khan. Neuro-fuzzy wavelet based adaptive MPPT algorithm for photovoltaic systems. Energies 2017; 10(3): 394. doi: 10.3390/en10030394
- [17] Harrag A, Messalti S. Variable step size modified P&O MPPT algorithm using GA-based hybrid offline/online PID controller. Renewable Sustainable Energy Reviews 2015; 49: 1247-1260. doi: 10.1016/j.rser.2015.05.003
- [18] Erickson RW, Maksimovic D. Fundamentals of power electronics. New York, US: Springer, 2001. doi: 10.1007/b100747
- [19] Isidori A. Nonlinear control systems. London, UK: Springer-Verlag, 1993. doi: 10.1007/978-1-84628-615-5
- [20] Duman S, Yörükeren N, Altaş İH. Gravitational search algorithm for determining controller parameters in an automatic voltage regulator system. Turkish Journal of Electrical Engineering and Computer Science 2016; 24(4): 2387-2400. doi: 10.3906/elk-1404-14