تعداد نشریات | 27 |
تعداد شمارهها | 565 |
تعداد مقالات | 5,819 |
تعداد مشاهده مقاله | 8,131,349 |
تعداد دریافت فایل اصل مقاله | 5,444,834 |
Maximum Power Point Tracking Using State-dependent Riccati equation based Model Reference Adaptive Control | ||
International Journal of Industrial Electronics Control and Optimization | ||
مقاله 2، دوره 3، شماره 2، مرداد 2020، صفحه 115-124 اصل مقاله (976.43 K) | ||
نوع مقاله: Research Articles | ||
شناسه دیجیتال (DOI): 10.22111/ieco.2019.28544.1133 | ||
نویسندگان | ||
Mostafa Rahideh![]() ![]() ![]() | ||
1Faculty of Electrical and Computer Engineering University of Kashan Kashan, Iran | ||
2Faculty of Electrical and Computer Engineering University of Kashan Kashan, Iran | ||
چکیده | ||
In this paper, an adaptive control method is proposed for maximum power point tracking (MPPT) in photovoltaic (PV) systems. For improving the performance of an MPPT, this study develops a two-level adaptive control structure that can decrease difficulty in system control and efficiently handle the uncertainties and perturbations in the PV systems and the environment. The first control level is a ripple correlation control (RCC), and the second level is a model reference adaptive control (MRAC). This paper emphasizes mainly on designing the MRAC algorithm, which improves the underdamped dynamic response of the PV system. The original state-space equation of PV system is time-varying and nonlinear, and its step response contains oscillatory transients that damp slowly. Using the extended state-dependent Riccati equation (ESDRE) approach, an optimal law of the controller is derived for the MRAC system to remove the underdamped modes in PV systems. A algorithm of scanning the P-V curve of the PV array is proposed to seek the global maximum power point (GMPP) in the partial shading conditions (PSCs). It is shown that the proposed control algorithm enables the system to converge to the maximum power point in milliseconds in partial shading conditions . | ||
کلیدواژهها | ||
PV systems؛ Ripple correlation control؛ Model Reference Control؛ State-dependent Riccati equation؛ Partial shading conditions | ||
مراجع | ||
[1] S. L. Brunton, C. W. Rowley, S. R.Kulkarni, and C. Clarkson, “Maximum power point tracking for photovoltaic optimization using ripple-based extremum seeking control,” IEEE Trans. Power Electron., vol. 25, no. 10, pp. 2531–2540, Oct. 2010. [2] R. A.Mastromauro, M. Liserre, T.Kerekes, and A. Dell’Aquila, “A single-phase voltage-controlled grid- connected photovoltaic system with power quality conditioner functionality,” IEEE Trans. Ind. Electron., vol. 56, no. 11, pp. 4436–4444, Nov. 2009. [3] A. K. Abdelsalam, A. M. Massoud, S. Ahmed, and P. N. Enjeti, “High-performance adaptive perturb and observe MPPT technique for photovoltaic-based microgrids,” IEEE Trans. Power Electron., vol. 26, no. 4, pp. 1010– 1021, Apr. 2011. [4] M. A. Elgendy, B. Zahawi, and D. J. Atkinson, “Assessment of perturb and observe MPPT algorithm implementation techniques for PV pumping applications,” IEEE Trans. Sustainable Energy, vol. 3, no. 1, pp. 21–33, Jan. 2012. [5] G. Petrone, G. Spagnuolo, and M. Vitelli, “A multivariable perturb-and-observe maximum power point tracking technique applied to a single-stage photovoltaic inverter,” IEEE Trans. Ind. Electron., vol. 58, no. 1, pp. 76–84, Jan. 2011. [6] S. Jain and V.Agarwal, “A new algorithm for rapid tracking of approximate maximum power point in photovoltaic systems,” IEEE Power Electron. Lett., vol. 2, no. 1, pp. 16–19, Mar. 2004. [7] N. Femia, G. Petrone, G. Spagnuolo, and M. Vitelli, “Optimization of perturb and observe maximum power point tracking method,” IEEE Trans. Power Electron., vol. 20, no. 4, pp. 963–973, Jul. 2005. [8] M. A. S. Masoum, H. Dehbonei, and E. F. Fuchs,“Theoretical and experimental analyses of photovoltaic systems with voltage and current-based maximum power- point tracking,” IEEE Trans. Energy Convers., vol. 17, no. 4, pp. 514–522, Dec. 2002. [9] Yilmaz, U., Kircay, A., & Borekci, S. (2018). PV system fuzzy logic MPPT method and PI control as a charge controller. Renewable and Sustainable Energy Reviews, 81, 994-1001. [10] Duman, S., Yorukeren, N., & Altas, I. H. (2018). A novel MPPT algorithm based on optimized artificial neural network by using FPSOGSA for standalone photovoltaic energy systems. Neural Computing and Applications, 29(1), 257-278. [11] Oshaba, A. S., Ali, E. S., & Abd Elazim, S. M. (2016). PI controller design using artificial bee colony algorithm for MPPT of photovoltaic system supplied DC motor‐ pump load. Complexity, 21(6), 99-111. [12] Wu, Z., Yu, D., & Kang, X. (2018). Application of improved chicken swarm optimization for MPPT in photovoltaic system. Optimal Control Applications and Methods, 39(2), 1029-1042. [13] Priyadarshi, N., Azam, F., Sharma, A., Bhoi, A. K., & Kumar, M. (2018). A Particle Swarm Optimization based Fuzzy Logic Control for Photovoltaic System. International Journal of Engineering & Technology, 7(3.24), 491-496. [14] Farzaneh, J., Keypour, R., & Karsaz, A. (2019). A novel fast maximum power point tracking for a PV system using hybrid PSO-ANFIS algorithm under partial shading conditions. International Journal of Industrial Electronics, Control and Optimization, 2(1), 47-58. [15] Abdalla, O., Rezk, H., & Ahmed, E. M. (2019). Wind driven optimization algorithm based global MPPT for PV system under non-uniform solar irradiance. Solar Energy, 180, 429-444. [16] Assahout, S., Elaissaoui, H., El Ougli, A., Tidhaf, B., & Zrouri, H. (2018). A Neural Network and Fuzzy Logic based MPPT Algorithm for Photovoltaic Pumping System. International Journal of Power Electronics and Drive Systems, 9(4), 1823. [17] Duman, S., Yorukeren, N., & Altas, I. H. (2018). A novel MPPT algorithm based on optimized artificial neural network by using FPSOGSA for standalone photovoltaic energy systems. Neural Computing and Applications, 29(1), 257-278. [18] Haddouche, A., Mohammed, K., & Farah, L. (2018). Maximum Power Point Tracker Using Fuzzy Logic Controller with Reduced Rules. International Journal of Power Electronics and Drive Systems, 9(3), 1381. [19] P. T. Krein, “Ripple correlation control, with some applications,” in Proc. IEEE Int. Symp. Circuits Syst., 1999, vol. 5, pp. 283–286. [20] D. L. Logue and P. T. Krein, “Optimization of power electronic systems using ripple correlation control: A dynamic programming approach,” in Proc. IEEE 32nd Annu. Power Electron. Special. Conf., 2001, vol. 2, pp. 459–464. [21] J. W. Kimball and P. T. Krein, “Discrete-time ripple correlation control for maximum power point tracking,” IEEE Trans. Power Electron., vol. 23, no. 5, pp. 2353– 2362, Sep. 2008. [22] T. Esram, J. W. Kimball, P. T. Krein, P. L. Chapman, and P. Midya, “Dynamic maximum power point tracking of photovoltaic arrays using ripple correlation control,” IEEE Trans. Power Electron., vol. 21, no. 5, pp. 1282– 1291, Sep. 2006. [23] D. E. Miller, “A new approach to model reference adaptive control,” IEEE Trans. Autom. Control, vol. 48, no. 5, pp. 743–757, May 2003. [24] S. Sastry and M. Bodson, Adaptive Control: Stability, Convergence and Robustness. New York: Dover Publications, 2011. [25] Y. Batmani, H. Khaloozadeh, “On the design of human immunodeficiency virus treatment based on a non-linear time-delay model,” IET Systems Biology, 2013,10.1049/iet-syb.(2013).0012. [26] E. V. Solodovnik, S. Liu, and R. A. Dougal, “Power controller design for maximum power tracking in solar installations,” IEEE Trans. Power Electron., vol. 19, no. 5, pp. 1295–1304, Sep. 2004. [27] A. D. Rajapakse and D. Muthumuni, “Simulation tools for photovoltaic system grid integration studies,” in Proc. Electr. Power Energ. Conf. (EPEC 2009), Oct., pp. 1–5. [28] A. D. Rajapakse and D. Muthumuni, “Simulation tools for photovoltaic system grid integration studies,” in Proc. Electr. Power Energ. Conf. (EPEC 2009), Oct., pp. 1–5. [29] Rim, C., Joung, G. B., & Cho, G. H. (1988). A state space modeling of non-ideal DC-DC converters. In PESC 88 Record 19th Annual IEEE Power Electronics Specialists Conference (1988) (pp. 943-950). IEEE. [30] Chatrenour, N., Razmi, H., & Doagou-Mojarrad, H. (2017). Improved double integral sliding mode MPPT controller based parameter estimation for a stand-alone photovoltaic system. Energy Conversion and Management, 139, 97-109. | ||
آمار تعداد مشاهده مقاله: 285 تعداد دریافت فایل اصل مقاله: 217 |