Efficient and Fast Wind Turbine MPPT Algorithm Using TS Fuzzy Logic and Optimal Relation Methods

Authors

  • David R. López-Flores Tecnológico Nacional de México / IT de Chihuahua https://orcid.org/0000-0003-4016-0845
  • Pedro R. Acosta-Cano-de-los-Rios Tecnológico Nacional de México / IT de Chihuahua
  • Pedro R. Márquez-Gutierrez Tecnológico Nacional de México / IT de Chihuahua
  • José E. Acosta-Cano-de-los-Rios Tecnológico Nacional de México / IT de Chihuahua https://orcid.org/0009-0003-0711-6313
  • Rogelio E. Baray-Arana Tecnológico Nacional de México / IT de Chihuahua
  • Graciela Ramirez-Alonso Universidad Autonoma de Chihuahua https://orcid.org/0000-0002-9781-3010

Keywords:

Wind turbine system, MPPT algorithm, fuzzy logic, Takagi–Sugeno, optimal relation, F28069M board, PIL simulation

Abstract

This paper proposes an efficient and fast maximum power point tracking (MPPT) algorithm for a wind turbine (WT) connected to a battery bank via a permanent magnet synchronous generator, a three-phase diode rectifier, and a dc-dc boost converter. The algorithm is based on the Takagi-Sugeno (TS) fuzzy system and optimal relation methods and is called TS-MPPT. The fuzzy system computes the converter duty cycle using an input that combines the error and its rate of change. The error is the difference between the reference current computed from the optimal relation and the rectifier current. The methods used in the algorithm resulted in a five-rule TS fuzzy system, which contributed to a fast algorithm in terms of its total execution time (TET): 89.12 µs on the F28069M board. The TET attained enabled a synchronized operation of the algorithm with the converter switching frequency. Additionally, the results based on the processor-in-the-loop simulation approach show that the TS-MPPT algorithm achieves an effective MPP tracking process with an energy conversion efficiency of 99.43% and behaves properly when evaluated over the typical WT power curve. Furthermore, the effectiveness and performance of the proposed algorithm are demonstrated against others using the proportional-integral controller, the Mamdani fuzzy method, and a TS fuzzy model from the literature.

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Author Biographies

David R. López-Flores, Tecnológico Nacional de México / IT de Chihuahua

David R. Lopez-Flores obtained a Master degree of Science in Electronic Engineering in 2005 and a Doctor of Science degree in Electronic Engineering in 2022 from the Tecnológico Nacional de México / IT de Chihuahua Chih., México, where he is currently working as a full-time professor. He is interested in power electronic and mechatronic systems for conditioning renewable energies. Currently, he is a member of the National System of Researchers of the National Council of Humanities, Sciences, and Technologies in Mexico.

Pedro R. Acosta-Cano-de-los-Rios, Tecnológico Nacional de México / IT de Chihuahua

Pedro R. Acosta-Cano-de-los-Rios professor and researcher in the Automation and Industrial Informatics Group of the Graduate and Research Division of the Instituto Tecnológico de Chihuahua, Mexico. He received his Ph.D. degree from the Polytechnic University of Valencia, Spain in 2005. He is an Industrial Engineer in Electronics and a Master of Science in Electrical Engineering from the Instituto Tecnológico de Chihuahua. His current research interests are in the area of automatic control within the theory and application of control of nonlinear systems and, in particular, sliding mode control.

Pedro R. Márquez-Gutierrez, Tecnológico Nacional de México / IT de Chihuahua

Pedro R. Márquez-Gutierrez received a degree in Industrial Engineering in Electronics from the Instituto Tecnológico de Chihuahua (TecNM/ITCH) México, a Master's degree in Computer Science from the Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas (IIMAS), Universidad Autónoma de México (UNAM), and a Ph.D. in Computer Science from New Mexico State University (NMSU), New Mexico, USA. He received the Chihuahua Prize in Science and Technology from the Government of the State of Chihuahua. He is a Research Professor at TecNM/ITCH. He has taught at NMSU, IIMAS-UNAM, UACJ, ITESM Campus Chihuahua, FEFyCD and FCA UACH, in different periods.

José E. Acosta-Cano-de-los-Rios, Tecnológico Nacional de México / IT de Chihuahua

José E. Acosta-Cano-de-los-Rios was born in Chihuahua, México. He received his B.S. from Instituto Tecnológico de Chihuahua, a M.Sc degree from Case Institute of Technology and a Ph.D. from Universidad Politécnica de Madrid. His research interests concern supervisory control, flexible automation systems, and theorical aspects of automation. He has been as a titular professor at the Insituto Tecnológico de Chihuahua and Universidad Autónoma de Chihuahua.

Rogelio E. Baray-Arana, Tecnológico Nacional de México / IT de Chihuahua

Rogelio E. Baray-Arana received the BSc.(1987) and the MSc.(1990) degrees in electrical engineering from the Chihuahua Institute of Technology, Chihuahua, Chih., Mexico. Has been an research-professor with the Electrical-Electronics Engineering Department at Chihuahua Institute of Technology. His professional experience includes development of projects and consultancy for several companies. He currently works as a Research Professor at the Chihuahua Institute of Technology since 1991, in Chihuahua, Chih., Mexico where he is the director of the electromechanics control Laboratory and director of the Graduate Studies and Research Division. His research interests include mechatronic design, electronic power converter, control of electromechanical systems and robotics.

Graciela Ramirez-Alonso, Universidad Autonoma de Chihuahua

Graciela Ramírez-Alonso received the M.Sc. (2204) and Ph.D. (2015) degrees in EE from the Chihuahua Institute of Technology. She currently teaches in the undergraduate and graduate programs at the Universidad Autónoma de Chihuahua, Facultad de Ingeniería. She is currently a member of the National System of Researchers of the National Council of Humanities, Sciences and Technologies. Her research interests include computer vision, signal processing, fuzzy logic, and machine learning algorithms.

References

M. J. Khan, L. Mathew, M. A. Alotaibi, H. Malik, and M. E. Nassar, “Fuzzy-logic-based comparative analysis of different maximum power point tracking controllers for hybrid renewal energy systems,” Mathematics (Basel), vol. 10, no. 3, pp. 529–, 2022. https://doi.org/10.3390/math10030529.

M. A. Morales Caporal, J. d. J. Rangel Magdaleno, I. Cruz Vega, and R. Morales Caporal, “Improved grid-photovoltaic system based on variable-step MPPT, predictive control, and active/reactive control,” IEEE Latin America Transactions, vol. 15, no. 11, pp. 2064–2070, 2017. DOI:10.1109/TLA.2017.8070409.

A. A. Salem, N. A. N. Aldin, A. M. Azmy, and W. S. E. Abdellatif, “Implementation and validation of an adaptive fuzzy logic controller for MPPT of PMSG-based wind turbines,” IEEE Access, vol. 9, pp. 165690– 165707, 2021. DOI:10.1109/ACCESS.2021.3134947.

R. B. Bollipo, S. Mikkili, and P. K. Bonthagorla, “Hybrid, optimal, intelligent and classical PV MPPT techniques: A review,” CSEE Journal of Power and Energy Systems, vol. 7, no. 1, pp. 9–33, 2021. DOI:10.17775/CSEEJPES.2019.02720.

C. V. Govinda, S. V. Udhay, C. Rani, Y. Wang, and K. Busawon, “A review on various MPPT techniques for wind energy conversion system,” in 2018 International Conference on Computation of Power, Energy, Information and Communication (ICCPEIC), pp. 310–326, 2018. DOI:10.1109/ICCPEIC.2018.8525219.

M. Mao, L. Cui, Q. Zhang, K. Guo, L. Zhou, and H. Huang, “Classification and summarization of solar photovoltaic MPPT techniques: A review based on traditional and intelligent control strategies,” Energy reports, vol. 6, pp. 1312–1327, 2020. https://doi.org/10.1016/j.egyr.2020.05.013.

X. C. Le, M. Q. Duong, and K. H. Le, “Review of the modern maximum power tracking algorithms for permanent magnet synchronous generator of wind power conversion systems,” Energies (Basel), vol. 16, no. 1, pp. 402–, 2023. https://doi.org/10.3390/en16010402.

M. Oubella, M. Ajaampun, and B. Bouachrine, “Low cost microcontroller implementation of Takagi–Sugeno fuzzy MPPT controller for photovoltaic systems,” International Journal of Electrical and Computer Engineering Systems, vol. 13, no. 10, pp. 971–981, 2022. https: //doi.org/10.32985/ijeces.13.10.12.

M. H. Qais, H. M. Hasanien, and S. Alghuwainem, “Enhanced whale optimization algorithm for maximum power point tracking of variables peed wind generators,” Applied Soft Computing, vol. 86, p. 105937, 2020. https://doi.org/10.1016/j.asoc.2019.105937.

Z. Civelek, “Optimization of fuzzy logic (Takagi-Sugeno) blade pitch angle controller in wind turbines by genetic algorithm,” Engineering Science and Technology, an International Journal, vol. 23, no. 1, pp. 1– 9, 2020. https://doi.org/10.1016/j.jestch.2019.04.010.

L. L. Wang, “MPPT control for photovoltaic system based on T-S fuzzy reasoning,” in 2015 5th International Conference on Electric Utility Deregulation and Restructuring and Power Technologies (DRPT), pp. 1976–1980, 2015. DOI: 10.1109/DRPT.2015.7432562.

H. Abbes, H. Abid, and K. Loukil, “An improved MPPT incremental conductance algorithm using TS fuzzy system for photovoltaic panel,” International journal of renewable energy research, vol. 5, no. 1, pp. 160–167, 2015. https://doi.org/10.20508/ijrer.v5i1.1868.g6481.

A. Jouda, F. Elyes, R. Abdelhamid, and M. Abdelkader, “Simulation and real implementation of the fuzzy MPPT algorithm for photovoltaic panel,” Indian Journal of Science and Technology, vol. 10, pp. 1–11, 2017. DOI:10.17485/ijst/2017/v10i17/90437.

H. Abid, A. Toumi, and M. Chaabane, “MPPT algorithm for photovoltaic panel based on augmented Takagi-Sugeno fuzzy model,” International Scholarly Research Notices, vol. 2014, pp. 1–10, 2014. https://doi.org/ 10.1155/2014/253146.

S. Abderrahim, M. Allouche, and M. Chaabane, “Intelligent power control of wind conversion system based on Takagi–Sugeno fuzzy model,” International Journal of Circuit Theory and Applications, vol. 51, no. 5, pp. 2247–2265, 2023. https://doi.org/10.1002/cta.3517.

M. Allouche, K. Dahech, M. Chaabane, and D. Mehdi, “T-S fuzzy control for MPPT of photovoltaic pumping system,” Journal of intelligent & fuzzy systems, vol. 34, no. 4, pp. 2521–2533, 2018.

M. Allouche, K. Dahech, and M. Chaabane, “Multiobjective maximum power tracking control of photovoltaic systems: T-S fuzzy model-based approach,” Soft computing (Berlin, Germany), vol. 22, no. 7, pp. 2121– 2132, 2018. https://doi.org/10.1007/s00500-017-2691-7.

D. Ounnas, M. Ramdani, S. Chenikher, and T. Bouktir, “An efficient maximum power point tracking controller for photovoltaic systems using Takagi–Sugeno fuzzy models,” Arabian journal for science and engineering (2011), vol. 42, no. 12, pp. 4971–4982, 2017. https: //doi.org/10.1007/s13369-017-2532-0.

D. R. Lopez-Flores, J. L. Duran-Gomez, and J. Vega-Pineda, “Discretetime adaptive PID current controller for wind boost converter,” IEEE Latin America Transactions, vol. 21, no. 1, pp. 98–107, 2023. DOI:10.1109/TLA.2023.10015131.

K. Ogata, Sistemas de control en tiempo discreto. Pearson Educación, 1996.

S. Elert, “Programming possibilities using Matlab Simulink embedded coder on the example of data analysis from ahrs module,” Journal of Physics: Conference Series, vol. 1507, p. 082042, mar 2020. DOI:10.1088/1742-6596/1507/8/082042.

S. Stipa, A. Ajay, D. Allaerts, and J. Brinkerhoff, “Tosca – an opensource, finite-volume, large-eddy simulation (les) environment for wind farm flows,” Wind Energy Science, vol. 9, no. 2, pp. 297–320, 2024. https://doi.org/10.5194/wes-9-297-2024.

L. Legris, M. L. Pahus, T. Nishino, and E. Perez-Campos, “Prediction and mitigation of wind farm blockage losses considering mesoscale atmospheric response,” Energies, vol. 16, no. 1, 2023. https://doi.org/10.3390/en16010386.

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Published

2024-06-16

How to Cite

López-Flores, D. R. ., Acosta-Cano-de-los-Rios, P. R. ., Márquez-Gutierrez, P. R. ., Acosta-Cano-de-los-Rios, J. E. ., Baray-Arana, R. E., & Ramirez-Alonso, G. (2024). Efficient and Fast Wind Turbine MPPT Algorithm Using TS Fuzzy Logic and Optimal Relation Methods. IEEE Latin America Transactions, 22(7), 612–619. Retrieved from https://latamt.ieeer9.org/index.php/transactions/article/view/8727

Issue

Vol. 22 No. 7 (2024): Ordinary Issue

Section

Electric Energy

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