Técnicas de control inteligente para el seguimiento del punto de máxima potencia en turbinas eólicas

  1. Muñoz-Palomeque, Eduardo 1
  2. Sierra-García, Jesús Enrique 1
  3. Santos, Matilde 2
  1. 1 Universidad de Burgos
    info

    Universidad de Burgos

    Burgos, España

    ROR https://ror.org/049da5t36

  2. 2 Universidad Complutense de Madrid
    info

    Universidad Complutense de Madrid

    Madrid, España

    ROR 02p0gd045

Journal:
Revista iberoamericana de automática e informática industrial ( RIAI )

ISSN: 1697-7920

Year of publication: 2024

Volume: 21

Issue: 3

Pages: 193-204

Type: Article

DOI: 10.4995/RIAI.2024.21097 DIALNET GOOGLE SCHOLAR lock_openOpen access editor

More publications in: Revista iberoamericana de automática e informática industrial ( RIAI )

Abstract

Maximum power point tracking (MPPT) is an essential stage in the operation of wind turbines to ensure efficient power generation. In recent years, advanced control techniques have been designed and applied to achieve this objective, solving some of the limitations of classical methods. This article provides an overview of existing strategies and describes some specific control configurations in more detail, explaining their usefulness and providing a basis for future developments. Specifically, it includes control techniques based on artificial intelligence for the study of MPPT control in wind turbines. Two intelligent control strategies are exemplified: a neural network and a fuzzy logic controller. These approaches are framed in the regulation of the electromagnetic torque of the generator and, consequently, the angular velocity of the system, improving power generation. The results show the benefits of these intelligent controllers to maximize power and improve the energy conversion process.

€ View funding

Funding information

Funders

Bibliographic References

  • Aissaoui, H.E., Ougli, A.E., Tidhaf, B., 2021. Neural Networks and Fuzzy Logic Based Maximum Power Point Tracking Control for Wind Energy Conversion System. Advances in Science, Technology and Engineering Systems Journal. 6(2), 586-592. https://doi.org/10.25046/aj060267
  • Azzouz, S., 2019. Innovative PID-GA MPPT Controller for Extraction of Maximum Power from Variable Wind Turbine. Electrotechnical Review 1(8), 117-122. https://doi.org/10.15199/48.2019.08.26
  • Chandrasekaran, K., Mohanty, M., Golla, M., Venkadesan, A., Simon, S.P., 2022. Dynamic MPPT Controller Using Cascade Neural Network for a Wind Power Conversion System with Energy Management. IETE Journal of Research 68(5), 3316-3330. https://doi.org/10.1080/03772063.2020.1756934
  • Cheng, M., and Zhu, Y., 2014. The State of the Art of Wind Energy Conversion Systems and Technologies: A Review. Energy Conversion and Management, 88, 332-47. https://doi.org/10.1016/j.enconman.2014.08.037
  • Chhipa, A.A., Kumar, V., Joshi, R.R., Chakrabarti, P., Jasinski, M., Burgio, A., Leonowicz, Z., Jasinska, E., Soni, R., Chakrabarti, T., 2021. Adaptive Neuro-Fuzzy Inference System-Based Maximum Power Tracking Controller for Variable Speed WECS. Energies 14(19), 6275. https://doi.org/10.3390/en14196275
  • Chojaa, H., Derouich, A., Chehaidia, S.E., Zamzoum, O., Taoussi, M., Elouatouat, H., 2021. Integral sliding mode control for DFIG based WECS with MPPT based on artificial neural network under a real wind profile. Energy Reports 7, 4809-4824. https://doi.org/10.1016/j.egyr.2021.07.066
  • Dida, A., Benattous, D., 2015. Fuzzy logic based sensorless MPPT algorithm for wind turbine system driven DFIG. In: 2015 3rd International Conference on Control, Engineering & Information Technology (CEIT), IEEE, Tlemcen, Algeria, pp. 1-6. https://doi.org/10.1109/CEIT.2015.7233139
  • Elaissaoui, H., Zerouali, M., Ougli, A.E., Tidhaf, B., 2020. MPPT Algorithm Based on Fuzzy Logic and Artificial Neural Network (ANN) for a Hybrid Solar/Wind Power Generation System. In: 2020 Fourth International Conference On Intelligent Computing in Data Sciences (ICDS), IEEE, Fez, Morocco, pp. 1-6. https://doi.org/10.1109/ICDS50568.2020.9268747
  • George, T., P, J., Francis, T., Sreedharan, C.E.S., 2022. Wind Energy Conversion System Based PMSG for Maximum Power Tracking and Grid Synchronization Using Adaptive Fuzzy Logic Control. Journal of Applied Research and Technology 20(6), 703-717. https://doi.org/10.22201/icat.24486736e.2022.20.6.1256
  • Kermany, S.D., Joorabian, M., Deilami, S., Masoum, M.A.S., 2017. Hybrid Islanding Detection in Microgrid With Multiple Connection Points to Smart Grids Using Fuzzy-Neural Network. IEEE Transactions on Power Systems 32(4), 2640-2651. https://doi.org/10.1109/TPWRS.2016.2617344
  • Korlepara, N.S.D., Subramani, Dr.P., 2022. Analysis of Dual Stator Winding Induction Generator-Based Wind Energy Conversion System Using Artificial Neural Network Maximum Power Point Tracking. International Journal of Renewable Energy Research 12, 372-382. https://doi.org/10.20508/ijrer.v12i1.12759.g8411
  • Kumar, K., Ramesh Babu, N., Prabhu, K.R., 2017. Design and Analysis of RBFN-Based Single MPPT Controller for Hybrid Solar and Wind Energy System. IEEE Access 5, 15308-15317. https://doi.org/10.1109/ACCESS.2017.2733555
  • Kumar, R., Agrawal, H.P., Shah, A., Bansal, H.O., 2019. Maximum power point tracking in wind energy conversion system using radial basis function based neural network control strategy. Sustainable Energy Technologies and Assessments 36, 100533. https://doi.org/10.1016/j.seta.2019.100533
  • Lee, C.-Y., Chen, P.-H., Shen, Y.-X., 2011. Maximum power point tracking (MPPT) system of small wind power generator using RBFNN approach. Expert Systems with Applications 38(10), 12058-12065. https://doi.org/10.1016/j.eswa.2011.02.054
  • Manasa, M., Jayaprakash, P., & Kumar, N. B. (2022, July). A comparative study of maximum power tracking of turbines of wind energy conversion systems. In 2022 International Conference on Futuristic Technologies in Control Systems & Renewable Energy (ICFCR) (pp. 1-6). IEEE. https://doi.org/10.1109/ICFCR54831.2022.9893580
  • Mahmoud, H.Y., Hasanien, H.M., Besheer, A.H., Abdelaziz, A.Y., 2020. Hybrid cuckoo search algorithm and grey wolf optimiser-based optimal control strategy for performance enhancement of HVDC-based offshore wind farms. IET Generation, Transmission & Distribution 14(10), 1902-1911. https://doi.org/10.1049/iet-gtd.2019.0801
  • Mansouri, Adil, Abdelmounime El Magri, Rachid Lajouad, Ilyass El Myasse, El Khlifi Younes, and Fouad Giri. 2023. "Wind Energy Based Conversion Topologies and Maximum Power Point Tracking: A Comprehensive Review and Analysis." E-Prime - Advances in Electrical Engineering, Electronics and Energy 6 (December): 100351. https://doi.org/10.1016/j.prime.2023.100351
  • Mariprasath, T., Shilaja, C., Hussaian Basha, CH., Murali, M., Fathima, F., Aisha, S., 2023. Design and Analysis of an Improved Artificial Neural Network Controller for the Energy Efficiency Enhancement of Wind Power Plant. In: Asari, V.K., Singh, V., Rajasekaran, R., Patel, R.B. (Eds.), Computational Methods and Data Engineering, Lecture Notes on Data Engineering and Communications Technologies. Springer Nature, Singapore, pp. 67-77. https://doi.org/10.1007/978-981-19-3015-7_6
  • Mousa, H.H.H., Youssef, A.-R., Mohamed, E.E.M., 2021. State of the art perturb and observe MPPT algorithms based wind energy conversion systems: A technology review. International Journal of Electrical Power & Energy Systems 126, 106598. https://doi.org/10.1016/j.ijepes.2020.106598
  • Muñoz, E., Ayala, E., Pozo, N., Simani, S., 2021. Fuzzy PID Control System Analysis for a Wind Turbine Maximum Power Point Tracking Using FAST and Matlab Simulink. In: Iano, Y., Saotome, O., Kemper, G., Mendes de Seixas, A.C., Gomes de Oliveira, G. (Eds.), Proceedings of the 6th Brazilian Technology Symposium (BTSym'20), Smart Innovation, Systems and Technologies. Springer International Publishing, Cham, pp. 905-917. https://doi.org/10.1007/978-3-030-75680-2_100
  • Muñoz-Palomeque, E., Sierra-García, J.E., Santos, M., 2023a. Hybrid Intelligent Control for Maximum Power Point Tracking of a Floating Wind Turbine. In: García Bringas, P., Pérez García, H., Martínez de Pisón, F.J., Martínez Álvarez, F., Troncoso Lora, A., Herrero, Á., Calvo Rolle, J.L., Quintián, H., Corchado, E. (Eds.), Hybrid Artificial Intelligent Systems, Lecture Notes in Computer Science. Springer Nature Switzerland, Cham, pp. 495-506. https://doi.org/10.1007/978-3-031-40725-3_42
  • Muñoz-Palomeque, E., Sierra-García, J.E., Santos, M., 2023b. MPPT Control in an Offshore Wind Turbine Optimized with Genetic Algorithms and Unsupervised Neural Networks. In: Maglogiannis, I., Iliadis, L., MacIntyre, J., Dominguez, M. (Eds.), Artificial Intelligence Applications and Innovations, IFIP Advances in Information and Communication Technology. Springer Nature Switzerland, Cham, pp. 465-477. https://doi.org/10.1007/978-3-031-34107-6_37
  • Ngo, Q.-V., Yi, C., Nguyen, T.-T., 2020. The maximum power point tracking based-control system for small-scale wind turbine using fuzzy logic. International Journal of Electrical and Computer Engineering (IJECE) 10(4), 3927. https://doi.org/10.11591/ijece.v10i4.pp3927-3935
  • NREL 2023. OpenFAST. https://openfast.readthedocs.io/en/main/
  • Pande, J., Nasikkar, P., Kotecha, K., Varadarajan, V., 2021. A Review of Maximum Power Point Tracking Algorithms for Wind Energy Conversion Systems. Journal of Marine Science and Engineering 9(11), 1187. https://doi.org/10.3390/jmse9111187
  • Phan, N.M.-L., Tung, D.-N., Thanh, T.-N., Vu, N.T.-T., 2023. ANFIS Wind Speed Estimator-Based Output Feedback Near-Optimal MPPT Control for PMSG Wind Turbine. Journal of Control, Automation, and Electrical Systems 34(3), 588-598. https://doi.org/10.1007/s40313-022-00980-5
  • Qais, M.H., Hasanien, H.M., Alghuwainem, S., 2019. Enhanced salp swarm algorithm: Application to variable speed wind generators. Engineering Applications of Artificial Intelligence 80, 82-96. https://doi.org/10.1016/j.engappai.2019.01.011
  • Ramadan, H., Youssef, A.-R., Mousa, H.H.H., Mohamed, E.E.M., 2019. An efficient variable-step P&O maximum power point tracking technique for grid-connected wind energy conversion system. SN Applied Sciences 1(12), 1658. https://doi.org/10.1007/s42452-019-1716-5
  • Ramos-Teodoro, J. and Rodríguez, F., 2022. Distributed energy production, control and management: a review of terminology and common approaches. Revista Iberoamericana de Automática e Informática industrial 19(3), pp. 233-253. https://doi.org/10.4995/riai.2022.16497
  • Rhaili, S.E., Abbou, A., Hichami, N.E., Marhraoui, S., 2018. A New Strategy Based Neural Networks MPPT Controller for Five-phase PMSG Based Variable-Speed Wind Turbine. In: 2018 7th International Conference on Renewable Energy Research and Applications (ICRERA). IEEE, Paris, pp. 1038-1043. https://doi.org/10.1109/ICRERA.2018.8566822
  • Sachan, Ayushi, Motilal Nehru National Institute of Technology Allahabad, India, Akhilesh Kumar Gupta, Motilal Nehru National Institute of Technology Allahabad, India, Paulson Samuel, and Motilal Nehru National Institute of Technology Allahabad, India. 2017. "A Review of MPPT Algorithms Employedin Wind Energy Conversion Systems." Journal of Green Engineering 6 (4):385-402. https://doi.org/10.13052/jge1904-4720.643
  • Salem, A.A., 2019. A Fuzzy Logic-based MPPT Technique for PMSG Wind Generation System. International Journal of Renewable Energy Research (IJRER) 9(4), pp. 1751-1760. https://doi.org/10.20508/ijrer.v9i4.10138.g7778
  • Santos, M., López, R., De la Cruz, J. M., 2005. Fuzzy control of the vertical acceleration of fast ferries. Control Engineering Practice, 13(3), 305-313. https://doi.org/10.1016/j.conengprac.2004.03.012
  • Sierra-García, J.E., Santos, M., 2021. Redes neuronales y aprendizaje por refuerzo en el control de turbinas eólicas. Revista Iberoamericana de Automática e Informática industrial 18(4), pp. 327-335. https://doi.org/10.4995/riai.2021.16111
  • Sitharthan, R., Karthikeyan, M., Sundar, D.S., Rajasekaran, S., 2020. Adaptive hybrid intelligent MPPT controller to approximate effectual wind speed and optimal rotor speed of variable speed wind turbine. ISA Transactions 96, 479-489. https://doi.org/10.1016/j.isatra.2019.05.029
  • Sitharthan, R., Parthasarathy, T., Sheeba Rani, S., Ramya, K., 2019. An improved radial basis function neural network control strategy-based maximum power point tracking controller for wind power generation system. Transactions of the Institute of Measurement and Control 41(11), 3158-3170. https://doi.org/10.1177/0142331218823858
  • Soliman, M.A., Hasanien, H.M., Azazi, H.Z., El-kholy, E.E., Mahmoud, S.A., 2018. Hybrid ANFIS-GA-based control scheme for performance enhancement of a grid-connected wind generator. IET Renewable Power Generation 12(7), 832-843. https://doi.org/10.1049/iet-rpg.2017.0576
  • Thanh, S.N., Xuan, H.H., The, C.N., Hung, P.P., Van, T.P., Kennel, R., 2016. Fuzzy logic based maximum power point tracking technique for a stand-alone wind energy system. In: 2016 IEEE International Conference on Sustainable Energy Technologies (ICSET), IEEE, Hanoi, Vietnam, pp. 320-325. https://doi.org/10.1109/ICSET.2016.7811803
  • Tiwari, R., Krishnamurthy, K., Neelakandan, R.B., Padmanaban, S., Wheeler, P.W., 2018. Neural Network Based Maximum Power Point Tracking Control with Quadratic Boost Converter for PMSG-Wind Energy Conversion System. Electronics 7(2), 20. https://doi.org/10.3390/electronics7020020
  • Tiwari, R., Kumar, K., Devi, V.L., V, S.S., Atyam, N.R., 2022. Evaluation of the MPPT for the Wind Energy Conversion System's Performance using ANN and ANFIS. In: 2022 3rd International Conference on Communication, Computing and Industry 4.0 (C2I4), pp. 1-6. https://doi.org/10.1109/C2I456876.2022.10051429
  • Vamvakas, D., Michailidis, P., Korkas, C., Kosmatopoulos, E., 2023. Review and Evaluation of Reinforcement Learning Frameworks on Smart Grid Applications. Energies 16(14), 5326. https://doi.org/10.3390/en16145326
  • Vu, N.T.-T., Nguyen, H.D., Nguyen, A.T., 2022. Reinforcement Learning-Based Adaptive Optimal Fuzzy MPPT Control for Variable Speed Wind Turbine. IEEE Access 10, 95771-95780. https://doi.org/10.1109/ACCESS.2022.3205124
  • Wei, C., Zhang, Z., Qiao, W., Qu, L., 2015. Reinforcement-Learning-Based Intelligent Maximum Power Point Tracking Control for Wind Energy Conversion Systems. IEEE Transactions on Industrial Electronics 62(10), 6360-6370. https://doi.org/10.1109/TIE.2015.2420792
  • Wei, C., Zhang, Z., Qiao, W., Qu, L., 2016. An Adaptive Network-Based Reinforcement Learning Method for MPPT Control of PMSG Wind Energy Conversion Systems. IEEE Transactions on Power Electronics 31(11), 7837-7848. https://doi.org/10.1109/TPEL.2016.2514370
  • Zerouali, M., Boutouba, M., Ougli, A.E., Tidhaf, B., 2019. Control of variable speed wind energy conversion systems by fuzzy logic and conventional P&O. In: 2019 International Conference on Intelligent Systems and Advanced Computing Sciences (ISACS), IEEE, Taza, Morocco, pp. 1-5. https://doi.org/10.1109/ISACS48493.2019.9068866
  • Zhou, B., Zhang, Z., Li, G., Yang, D. and Santos, M., 2023. Review of Key Technologies for Offshore Floating Wind Power Generation. Energies, 16(2), p.710. https://doi.org/10.3390/en16020710
  • Zouheyr, D., Lotfi, B., Abdelmadjid, B., 2021. Improved hardware implementation of a TSR based MPPT algorithm for a low cost connected wind turbine emulator under unbalanced wind speeds. Energy 232, 121039. https://doi.org/10.1016/j.energy.2021.121039
Data source: Dialnet