A New Multivariate Linear Regression MPPT Algorithm for Solar PV System with Boost Converter

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P. Venkata Mahesh
S. Meyyappan
Rama Koteswara Rao Alla


Operating solar photovoltaic (PV) panels at the maximum power point (MPP) is considered to enrich energy conversion efficiency. Each MPP tracking technique (MPPT) has its conversion efficiency and methodology for tracking the MPP. This paper introduces a new method for operating the PV panel at MPP by implementing the multivariate linear regression (MLR) machine learning algorithm. The MLR machine learning model in this study is trained and tested using the data collected from the PV panel specifications. This MLR algorithm can predict the maximum power available at the panel, and the voltage corresponds to this maximum power for specific values of irradiance and temperature. These predicted values help in the calculation of the duty ratio for the boost converter. The MATLAB/SIMULINK results illustrate that, as time progresses, the PV panel is forced to operate at the MPP predicted by the MLR algorithm, yielding a mean efficiency of more than 96% in the steady-state operation of the PV system, even under variable irradiances and temperatures.


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Mahesh, P. V., Meyyappan, S., & Alla, R. K. R. (2022). A New Multivariate Linear Regression MPPT Algorithm for Solar PV System with Boost Converter. ECTI Transactions on Electrical Engineering, Electronics, and Communications, 20(2), 269–281. https://doi.org/10.37936/ecti-eec.2022202.246909
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M. L. Azad, S. Das, P. K. Sadhu, B. Satpati, A. Gupta, and P. Arvind, “P&O algorithm based MPPT technique for solar PV system under different weather conditions,” in 2017 International Conference on Circuit, Power and Computing Technologies (ICCPCT), 2017.

A. K. Gupta, R. K. Pachauri, T. Maity, Y. K. Chauhan, O. P. Mahela, B. Khan, and P. K. Gupta, “Effect of various incremental conductance MPPT methods on the charging of battery load feed by solar panel,” IEEE Access, vol. 9, pp. 90 977–90 988, 2021.

A. M. Farayola, A. N. Hasan, and A. Ali, “Curve fitting polynomial technique compared to ANFIS technique for maximum power point tracking,” in 2017 8th International Renewable Energy Congress (IREC), 2017.

K. R. Bharath and E. Suresh, “Design and implementation of improved fractional open circuit voltage based maximum power point tracking algorithm for photovoltaic applications,” International Journal of Renewable Energy Research, vol. 7, no. 3, pp. 1108–1113, 2017.

H. A. Sher, A. F. Murtaza, A. Noman, K. E. Addoweesh, and M. Chiaberge, “An intelligent control strategy of fractional short circuit current maximum power point tracking technique for photovoltaic applications,” Journal of Renewable and Sustainable Energy, vol. 7, no. 1, Jan. 2015, Art. no. 013114.

J. Zhang, T. Wang, and H. Ran, “A maximum power point tracking algorithm based on gradient descent method,” in 2009 IEEE Power & Energy Society General Meeting, 2009.

S. Narendiran, S. K. Sahoo, R. Das, and A. K. Sahoo, “Fuzzy logic controller based maximum power point tracking for PV system,” in 2016 3rd International Conference on Electrical Energy Systems (ICEES), 2016, pp. 29–34.

S. A. Rizzo and G. Scelba, “ANN based MPPT method for rapidly variable shading conditions,” Applied Energy, vol. 145, pp. 124–132, May 2015.

E. H. M. Ndiaye, A. Ndiaye, M. A. Tankari, and G. Lefebvre, “Adaptive neuro-fuzzy inference system application for the identification of a photovoltaic system and the forecasting of its maximum power point,” in 2018 7th International Conference on Renewable Energy Research and Applications (ICRERA), 2018, pp. 1061–1067.

L. Mohammad, E. Prasetyono, and F. D. Murdianto, “Performance evaluation of ACO-MPPT and constant voltage method for street lighting charging system,” in 2019 International Seminar on Application for Technology of Information and Communication (iSemantic), 2019, pp. 411–416.

P. Dhivya and K. R. Kumar, “MPPT based control of sepic converter using firefly algorithm for solar PV system under partial shaded conditions,” in 2017 International Conference on Innovations in Green Energy and Healthcare Technologies (IGEHT), 2017.

A. Borni, T. Abdelkrim, N. Bouarroudj, A. Bouchakour, L. Zaghba, A. Lakhdari, and L. Zarour, “Optimized MPPT controllers using GA for grid connected photovoltaic systems, comparative study,” Energy Procedia, vol. 119, pp. 278–296, 2017.

R. A. Abdul-Nabe, R. A. Abdul-Nabi, and S. Alwaisawy, “A grey wolf optimization based MPPT algorithm for energy harvesting PV system,” Journal of Green Engineering, vol. 10, no. 2, pp. 299–326, Feb. 2020.

J. A. Carballo, J. Bonilla, M. Berenguel, J. Fernández-Reche, and G. García, “Machine learning for solar trackers,” AIP Conference Proceedings, vol. 2126, 2019, Art. no. 030012.

P. Kofinas, S. Doltsinis, A. Dounis, and G. Vouros, “A reinforcement learning approach for MPPT control method of photovoltaic sources,” Renewable Energy, vol. 108, pp. 461–473, Aug. 2017.

L. Avila, M. D. Paula, I. Carlucho, and C. S. Reinoso, “MPPT for PV systems using deep reinforcement learning algorithms,” IEEE Latin America Transactions, vol. 17, no. 12, pp. 2020–2027, Dec. 2019.

R. Ayop and C. W. Tan, “Design of boost converter based on maximum power point resistance for photovoltaic applications,” Solar Energy, vol. 160, pp. 322–335, Jan. 2018.

O. Nabil, B. Bachir, and A. ALLAG, “Implementation of a new MPPT technique for PV systems using a boost converter driven by arduino MEGA,” in 2018 International Conference on Communications and Electrical Engineering (ICCEE), 2018.

D. P. Winston, B. P. Kumar, S. C. Christabel, A. J. Chamkha, and R. Sathyamurthy, “Maximum power extraction in solar renewable power system - a bypass diode scanning approach,” Computers & Electrical Engineering, vol. 70, pp. 122–136, Aug. 2018.

B. M. Sundaram, B. V. Manikandan, B. P. Kumar, and D. P. Winston, “Combination of novel converter topology and improved MPPT algorithm for harnessing maximum power from grid connected solar PV systems,” Journal of Electrical Engineering & Technology, vol. 14, no. 2, pp. 733–746, 2019.

B. P. Kumar, D. P. Winston, S. C. Christabel, and S. Venkatanarayanan, “Implementation of a switched PV technique for rooftop 2 kW solar PV to enhance power during unavoidable partial shading conditions,” Journal of Power Electronics, vol. 17, no. 6, pp. 1600–1610, Nov. 2017.

J. M. Kumbhare and M. M. Renge, “Line commutated converter for grid interfacing of solar photovoltaic array,” in 2014 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES), 2014.

V. Tamrakar, S. C. Gupta, and Y. Sawle, “Singlediode PV cell modeling and study of characteristics of single and two-diode equivalent circuit,” Electrical and Electronics Engineering: An International Journal, vol. 4, no. 3, pp. 13–24, Aug. 2015.

K. Kim and N. Timm, Univariate and Multivariate General Linear Models: Theory and Applications with SAS, 2nd ed. New York, USA: Chapman and Hall/CRC, 2007.

M. H. Rashid, Power Electronics: Circuits, Devices & Applications, 4th ed. London, UK: Pearson, 2004.

K. Bingi, B. R. Prusty, A. Kumra, and A. Chawla, “Torque and temperature prediction for permanent magnet synchronous motor using neural networks,” in 2020 3rd International Conference on Energy, Power and Environment: Towards Clean Energy Technologies, 2021.

A. Choudhary, D. Pandey, and S. Bhardwaj, “Artificial neural networks based solar radiation estimation using backpropagation algorithm,” International Journal of Renewable Energy Research, vol. 10, no. 4, pp. 1566–1575, Dec. 2020.

F. K. Abo-Elyousr, A. M. Abdelshafy, and A. Y. Abdelaziz, “MPPT-based particle swarm and cuckoo search algorithms for PV systems,” in Modern Maximum Power Point Tracking Techniques for Photovoltaic Energy Systems, A. Eltamaly and A. Y. Abdelaziz, Eds. Cham, Switzerland: Springer, 2020, pp. 379–400.

E. M. Ali, A. K. Abdelsalam, K. H. Youssef, and A. A. Hossam-Eldin, “An enhanced cuckoo search algorithm fitting for photovoltaic systems’ global maximum power point tracking under partial shading conditions,” Energies, vol. 14, no. 21, 2021, Art. no. 7210.