Maximum Power Point Tracking with Regression Machine Learning Algorithms for Solar PV systems

P Venkata Mahesh, S Meyyappan, Alla RamakoteswaraRao

Abstract


Solar panels generate energy by utilizing the sun rays on their surface, which depends on the amount of surface temperature and the strength of solar radiation. To maximize the efficiency of the energy conversion, the solar PV panel should be operated at maximum power point (MPP). Each maximum power point tracking (MPPT) method has its unique conversion efficacy and tracking strategy of MPP. This paper describes a novel approach to operating the PV system at MPP by implementing linear and nonlinear regression type machine learning algorithms. The data acquired from the PV panel specifications were used to train and test the machine learning model. For specific quantities of irradiation and temperature, these algorithms predict the available maximum power at the PV panel and its corresponding voltage. These predicted values help to determine the duty cycle for the boost converter to work the system at MPP. The simulation results show that the PV panel was forced to work at the predicted MPP by regression algorithms even in the presence of changes in solar radiation and temperature.


Keywords


Boost converter; Linear and nonlinear regression; Maximum power point tracking; Photovoltaic system; Regression machine learning.

Full Text:

PDF

References


Mahdi, A. S., et al., "Maximum power point tracking using perturb and observe, fuzzy logic and ANFIS", SN Applied Sciences 2, no.1 (2020), pp 1-9. https://doi.org/10.1007/s42452-019-1886-1.

Shang, Liqun, Hangchen Guo, and Weiwei Zhu, "An improved MPPT control strategy based on incremental conductance algorithm", Protection and Control of Modern Power Systems 5, no. 1 (2020): 1-8.https://doi.org/10.1186/s41601-020-00161-z.

González-Castaño, et al., "An MPPT strategy based on a surface-based polynomial fitting for solar photovoltaic systems using real-time hardware", Electronics 10, no. 2 (2021): 206. https://doi.org/10.3390/electronics10020206.

Li, Xingshuo, Huiqing Wen, and Chenhao Zhao, "Improved beta parameter based MPPT method in photovoltaic system", In 2015 9th International Conference on Power Electronics and ECCE Asia (ICPE-ECCE Asia), pp. 1405-1412. IEEE, 2015. doi: 10.1109/ICPE.2015.7167963.

Kota, Venkata Reddy, and Muralidhar Nayak Bhukya, "A simple and efficient MPPT scheme for PV module using 2-dimensional lookup table." In 2016 IEEE Power and Energy Conference at Illinois (PECI), pp. 1-7. IEEE, 2016. doi: 10.1109/PECI.2016.7459226.

Kaffash, Mahtab, Mohammad Hossein Javidi, and Ali Darudi, "A combinational maximum power point tracking algorithm in photovoltaic systems under partial shading conditions", In 2016 Iranian Conference on Renewable Energy & Distributed Generation (ICREDG), pp. 103-107. IEEE, 2016. doi: 10.1109/ICREDG.2016.7875903.

Baimel, Dmitry, et al., "Improved fractional open circuit voltage MPPT methods for PV systems", Electronics 8, no. 3 , 2019, p. 321. https://doi.org/10.3390/electronics8030321.

Sher, Hadeed Ahmed, et al., "A new sensorless hybrid MPPT algorithm based on fractional short-circuit current measurement and P&O MPPT", IEEE Transactions on sustainable energy 6, no. 4 , 2015, pp.1426-1434. doi: 10.1109/TSTE.2015.2438781

Amara, Karima,et al., "An Optimized Steepest Gradient Based Maximum Power Point Tracking for PV Control Systems", International Journal on Electrical Engineering & Informatics 11, no. 4, 2019. doi: 10.15676/ijeei.2019.11.4.3.

Zhang, Longlong, William Gerard Hurley, and Werner Hugo Wölfle, "A new approach to achieve maximum power point tracking for PV system with a variable inductor", IEEE Transactions on Power Electronics 26, no. 4, 2010, pp. 1031-1037. doi: 10.1109/PEDG.2010.5545758.

Hadji, Slimane, Jean-Paul Gaubert, and Fateh Krim, "Real-time genetic algorithms-based MPPT: study and comparison (theoretical an experimental) with conventional methods," Energies 11, no. 2 , 2018,p. 459. https://doi.org/10.3390/en11020459.

Alshareef, Muhannad, Zhengyu Lin, Mingyao Ma, and Wenping Cao, "Accelerated particle swarm optimization for photovoltaic maximum power point tracking under partial shading conditions", Energies 12, no. 4, 2019, p. 623. https://doi.org/10.3390/en12040623.

Krishnan G, Satheesh, et al., "MPPT in PV systems using ant colony optimisation with dwindling population", IET Renewable Power Generation 14, no. 7, 2020, pp. 1105-1112. https://doi.org/10.1049/iet-rpg.2019.0875.

Atici, Koray, Ibrahim Sefa, and Necmi Altin, "Grey wolf optimization based MPPT algorithm for solar PV system with sepic converter", In 2019 4th International Conference on Power Electronics and their Applications (ICPEA), pp. 1-6. IEEE, 2019. doi: 10.1109/ICPEA1.2019.8911159.

Mosaad, Mohamed I., et al., "Maximum power point tracking of PV system based cuckoo search algorithm; review and comparison", Energy Procedia 162, 2019, pp. 117-126. https://doi.org/10.1016/j.egypro.2019.04.013.

Jyothy, Lakshmi PN, and M. R. Sindhu, "An artificial neural network based MPPT algorithm for solar PV system", In 2018 4th International Conference on Electrical Energy Systems (ICEES), pp. 375-380. IEEE, 2018. doi: 10.1109/ICEES.2018.8443277.

Jose A. Carballo, Javier Bonilla, Manuel Berenguel, Jesús Fernández-Reche, and Ginés García , "Machine learning for solar trackers", AIP Conference Proceedings 2126, 030012 , 2019, https://doi.org/10.1063/1.5117524.

Hussain Shareef, Ammar Hussein Mutlag, Azah Mohamed, “Random Forest-Based Approach for Maximum Power Point Tracking of Photovoltaic Systems Operating under Actual Environmental Conditions”, Computational Intelligence and Neuroscience, vol. 2017, 17 pages. https://doi.org/10.1155/2017/1673864.

Chou, Kuan-Yu, Shu-Ting Yang, and Yon-Ping Chen, "Maximum power point tracking of photovoltaic system based on reinforcement learning", Sensors 19, no. 22, 2019, p. 5054. https://doi.org/10.3390/s19225054.

Tamrakar Vivek et al., “Single-Diode 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, 2015, pp.13-24. doi:10.14810/elelij.2015.4302.

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

Li, M., Liu, X., "Maximum Likelihood Least Squares Based Iterative Estimation for a Class of Bilinear Systems Using the Data Filtering Technique", Int. J. Control Autom. Syst. 18, 2020, pp. 1581–1592. https://doi.org/10.1007/s12555-019-0191-5.

Ayop, Razman, and Chee Wei Tan, "Design of boost converter based on maximum power point resistance for photovoltaic applications", Solar Energy 160, 2018, pp. 322-335. https://doi.org/10.1016/j.solener.2017.12.016.

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




DOI (PDF): https://doi.org/10.20508/ijrer.v12i3.13249.g8517

Refbacks

  • There are currently no refbacks.


Online ISSN: 1309-0127

Publisher: Gazi University

IJRER is cited in SCOPUS, EBSCO, WEB of SCIENCE (Clarivate Analytics);

IJRER has been cited in Emerging Sources Citation Index from 2016 in web of science.

WEB of SCIENCE in 2025; 

h=35,

Average citation per item=6.59

Last three Years Impact Factor=(1947+1753+1586)/(146+201+78)=5286/425=12.43

Category Quartile:Q4