Linear Quadratic Integral optimal control of photovoltaic systems

Document Type : Research Article


1 Faculty of Electrical and Computer Engineering University of Kashan Kashan, Iran

2 Faculty of Electrical and Computer Engineering University of Kashan Kashan, Iran

3 Electrical and Electronic Engineering Department, Shahed University, Tehran, Iran


This study aims to report on the application of Linear Quadratic Integral (LQI) based global maximum power point tracker (GMPPT) method for transferring the available maximum power from photovoltaic (PV) systems to load in unshaded and shaded conditions. For the maximum power transmission under varying the environmental conditions and partially shaded conditions, MPPT technologies are utilized in PV systems. For the improvement of functioning MPPT, a new two-level control structure which decreases difficulty in the control process and efficiently deals with the uncertainties in the PV systems is introduced. In the proposed approach, the reference voltage at the global maximum power point (GMPP) is estimated by a new scanning algorithm. The difference between the reference voltage and the voltage of the PV array is then used by LQI controller to generate the duty cycle for a boost converter. The design process of the proposed approach is explained as step by step. The benefits of the approach are quicker tracking capability, transferring maximum deliverable power and simple implementation. To verify the proposed method, several irradiation profiles that create several peaks in the P-V curve are used. The simulation results show that the proposed method causes PV systems to track the GMPP immediately so that no oscillation around the GMPP is observed. Therefore, maximum efficiency can be derived from the PV system.


Main Subjects

[1] Esram T, Chapman PL. Comparison of photovoltaic array maximum power point tracking techniques. IEEE Trans Energy Convers 2007;22(2):439–49.
[2] De Brito MAG, Galotto L, Sampaio LP, de Azevedo e Melo G, Canesin CA. Evaluation of the main MPPT techniques for photovoltaic applications. IEEE Trans Indus Electr 2013;60(3):1156–67.
[3] Ishaquea K, Salamb Z. A review of maximum power point tracking techniques of PV system for uniform insolation and partial shading condition. Renew Sustain Energy Rev 2013;19(March):475–88.
[4] Jain S, Agarwal V. Comparison of the performance of maximum power point tracking schemes applied to single-stage grid-connected photovoltaic systems. IET Electr Power Appl 2007;1(5):753–62.
[5] Femia N, Granozio D, Petrone S, Spagnuolo G, Vittelli M. Predictive & adaptive MPPT perturb and observe method. IEEE Trans Aerosp Electron Syst 2007;43(3):934–50.
[6] Casadei D, Grandi G, Rossi C. Single-phase single-stage photovoltaic generation system based on a ripple correlation control maximum power point tracking. IEEE Trans Energy Convers 2006;21(2):562–8.
[7] Abdelsalam AK, Massoud AM, Ahmed S, Enjeti P. High-performance adaptive perturb and observe MPPT technique for photovoltaic-based microgrids. IEEE Trans Power Electron 2011;26(4):1010–21.
[8] Qiang M, Mingwei S, Liying L, Guerrero JM. A novel improved variable step-size incremental-resistance MPPT method for PV systems. IEEE Trans Industr Electron 2011;58(6):2427–34.
[9] Fangrui L, Shanxu D, Fei L, Bangyin L, Yong K. A variable step size INC MPPT method for PV systems. IEEE Trans Industr Electron 2008;55(7):2622–8.
[10] Sera D, Teodorescu R, Hantschel J, Knoll M. Optimized maximum power point tracker for fast-changing environmental conditions. IEEE Trans Industr Electron 2008;55(7):2629–37.
[11] Weidong X, Ozog N, Dunford WG. Topology study of photovoltaic interface for maximum power point tracking. IEEE Trans Industr Electron 2007;54(3):1696–704.
[12] Karlisa AD, Kottasb TL, Boutalisb YS. A novel maximum power point tracking method for PV systems using fuzzy cognitive networks (FCN). Electric Power Syst Res 2007;77(3–4):315–27.
[13] Kulaksız AA, Akkaya R. A genetic algorithm optimized ANN-based MPPT algorithm for a stand-alone PV system with induction motor drive. Sol Energy 2012;86(9):2366–75.
[14] Bahgata ABG, Helwab NH, Ahmadb GE, El Shenawyb ET. Maximum power point tracking controller for PV systems using neural networks. Renew Energy 2005;30(8):1257–68.
[15] Hiyama, Takashi, and Ken Kitabayashi. "Neural network based estimation of maximum power generation from PV module using environmental information." IEEE Transactions on Energy Conversion 12.3 (1997): 241-247.
[16] Salah CB, Ouali M. Comparison of fuzzy logic and neural network in maximum power point tracker for PV systems. Electric Power Syst Res. 2011;81(1):43–50.
[17] Elobaid LM, Abdelsalam AK, Zakzouk EE. Artificial neural network based maximum power point tracking technique for PV systems. In: Proc of 38th annual conference on IEEE industrial electronics society, IECON; 2012. p. 937–42.
[18] Chiu YH, Luo YF, Huang JW, Liu YH. An ANN-based maximum power point tracking method for fast changing environments. In: Proc of 13th international symposium on advanced intelligent systems; 2012. p. 715–20.
[19] Seo JH, Im CH, Heo CG, Kim JK, Jung HK, Lee CG. Multimodal function optimization based on particle swarm optimization. IEEE Trans Magn 2006;42(4):1095–8.
[20] Miyatake M, Veerachary M, Toriumi F, Fujii N, Ko H. Maximum power point tracking of multiple photovoltaic arrays: a PSO approach. IEEE Trans Aerosp Electron Syst 2011;47(1):367–80.
[21] Koutroulis E, Blaabjerg F. A new technique for tracking the global maximum power point of PV arrays operating under partial-shading conditions. IEEE J Photovoltaics 2012;2(2):184–90.
[22] Nguyen TL, Low K-S. A global maximum power point tracking scheme employing DIRECT search algorithm for photovoltaic systems. IEEE Trans Industr Electron October 2010;57(10):3456–67.
[23] Ji YH, Jun DY, Kim JG, Kim JH, Lee TW, Won CY. A real maximum power point tracking method for mismatching compensation in PV array under partially shaded conditions. IEEE Trans Power Electron 2011;26(4):1001–9.
[24] Patel H, Agarwal V. Maximum power point tracking scheme for PV systems operating under partially shaded conditions. IEEE Trans Ind Electron 2008;55(4):1689–98.
[25] Boztepe M, Guinjoan F, Velasco-Quesada G, Silvestre S, Chouder A, Karatepe E. Global MPPT scheme for photovoltaic string inverters based on restricted voltage window search algorithm. IEEE Trans Industr Electron 2014;61(7):3302–12.
[26] Syafaruddin, Karatepe E, Hiyama T. Artificial neural network-polar coordinated fuzzy controller based maximum power point tracking control under partially shaded conditions. IET Renew Power Gener 2009;3(2):239–53.
[27] Alajmi BN, Ahmed KH, Finney SJ, Williams BW. A maximum power point tracking technique for partially shaded photovoltaic systems in microgrids. IEEE Trans Industr Electron 2013;60(4):1596–606.
[28] Peng L, Yaoyu L, Seem JE. Sequential ESC-based global MPPT control for photovoltaic array with variable shading. IEEE Trans Sustain Energy 2011;2(3):348–58.
[29] Kazmi S, Goto H, Ichinokura O, Guo Hai-Jiao. An improved and very efficient MPPT controller for PV systems subjected to rapidly varying atmospheric conditions and partial shading. In: Proc of the Australasian power engineering conference; 2009. p. 1–6.
[30] Ahmed J, Salam Z. A maximum power point tracking (MPPT) for PV system using Cuckoo Search with partial shading capability. Appl Energy 2014;119(15):118–30.
[31] Mamarelis E, Petrone G, Spagnuolo G. A two-steps algorithm improving the P&O steady state MPPT efficiency. Appl Energy 2014;113(January):414–21.
[32] Punitha K, Devaraj D, Sakthivel S. Artificial neural network based modified incremental conductance algorithm for maximum power point tracking in photovoltaic system under partial shading conditions. Energy 2013;62(1):330–40.
[33] Ishaque K, Salam Z, Shamsudin A, Amjad M. A direct control based maximum power point tracking method for photovoltaic system under partial shading conditions using particle swarm optimization algorithm. Appl Energy 2012;99(April):414–22.
[34] Ghosh A, Dehuri S. Evolutionary algorithms for multi-criterion optimization: a survey. Int J Comput Inform Sci 2004;2(1):38–57.
[35] Daraban S, Petreus D, Morel C. A novel global MPPT based on genetic algorithms for photovoltaic systems under the influence of partial shading. In:39th annual conference of the IEEE industrial electronics society, IECON; 2013.
[36] Sareni B, Krahenbuhl L. Fitness sharing and niching method revisited. IEEE Trans Evol Comput 1998;2(3):97–106. 4235.735432.
[37] Hooke R, Jeeves TA. Direct search solution of numerical and statistical problems. J Assoc Comput Mach 1961;8(2):212–29.
[38] Dilettoso E, Rizzo SA, Salerno N. A parallel version of the self-adaptive low high evaluation evolutionary-algorithm for electromagnetic device optimization. IEEE Trans Magn 2014;50(2):633–6. TMAG.2013.2284928.
[39] Liu YH, Chen JH, Huang JW. Global maximum power point tracking algorithmfor PV systems operating under partially shaded conditions using the segmentation search method. Sol Energy 2014;103(May):350–63.
[40] N. Femia, G. Petrone, G. Spagnuolo, and M. Vitelli, Power Electronics and Control Techniques for Maximum Energy Harvesting in Photovoltaic Systems (CRC Press, 2012).
[41] C. S. Solanki, Solar Photovoltaics: Fundamentals, Technologies and Applications (PHI Learning Pvt. Ltd., 2015).
[42] Chatrenour, N., Razmi, H., & Doagou-Mojarrad, H. Improved double integral sliding mode MPPT controller based parameter estimation for a stand-alone photovoltaic system. Energy Conversion and Management 2017, 139, 97-109.‏
[43] Habibi, Mehran, and Alireza Yazdizadeh. "New MPPT controller design for PV arrays using neural networks (Zanjan City Case Study)." International Symposium on Neural Networks. Springer, Berlin, Heidelberg, 2009.‏
[44] F. Blaabjerg, Z. Chen, and S. B. Kjaer, “Power electronics as efficient interface in dispersed power generation systems,” IEEE Trans. Power Electron. 19(5), 1184–1194 (2004).