Passivity Based Control of Doubly Fed Induction Generator Using an Interval Type-2 Fuzzy Logic Controller

Izzeddine Allali; Boubekeur Dehiba

doi:10.18540/jcecvl10iss9pp21012

Autores/as

Izzeddine Allali IRECOM-laboratory, University DjillaliLiabes of SidiBel-Abbes, BP 89 SidiBel Abbes 22000-Algeria https://orcid.org/0009-0006-3668-7336
Boubekeur Dehiba IRECOM-laboratory, University DjillaliLiabes of SidiBel-Abbes, BP 89 SidiBel Abbes 22000-Algeria

DOI:

https://doi.org/10.18540/jcecvl10iss9pp21012

Palabras clave:

Doubly fed induction generator, Wind power, Passivity based control, Interval type-2 fuzzy logic control.

Resumen

El artículo se interesa por el control de un generador de inducción doblemente alimentado (DFIG) para la conversión de energía eólica. La estructura propuesta se basa en la asociación de pasividad y control por lógica difusa de tipo intervalo 2. El objetivo principal de esta tarea es regular y optimizar eficazmente el flujo de potencia activa y reactiva del generador a la red interconectada para garantizar un funcionamiento eficiente y la estabilidad, con las señales del rotor operadas a través de un convertidor bidireccional. La técnica de control propuesta se somete a diversas condiciones para evaluar su rendimiento, incluyendo velocidades de viento variables y ajustes de parámetros. Los resultados de la simulación muestran la robustez del control propuesto, donde la integración del controlador lógico difuso de intervalo tipo 2 (IT2-FLC) mejora el comportamiento dinámico, la sensibilidad a perturbaciones y la robustez frente a cambios de parámetros.

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Acikgoz, H., Kececioglu, O., Gani, A., Tekin, M., & Sekkeli, M. (2017). Robust control of shunt active power filter using interval type-2 fuzzy logic controller for power quality improvement. Tehnicki Vjesnik-Technical Gazette, 24. https://doi.org/10.17559/TV-20161213004749

Adeyanju, A. A. (2023). The Influence Of Rotor Separation On The Performance Of A Dual-Rotor Wind Turbine. Journal of Namibian Studies: History Politics Culture, 35, 4684-4702. https://doi.org/10.59670/jns.v35i.4573

Amira, L., Tahar, B., & Abdelkrim, M. (2020, June). Sliding mode control of doubly-fed induction generator in wind energy conversion system. In 2020 8th International Conference on Smart Grid (icSmartGrid) (pp. 96-100). IEEE. https://doi.org/ 10.1109/icSmartGrid49881.2020.9144778

Belkhier, Y., Achour, A., Bures, M., Ullah, N., Bajaj, M., Zawbaa, H. M., & Kamel, S. (2022). Interconnection and damping assignment passivity-based non-linear observer control for efficiency maximization of permanent magnet synchronous motor. Energy Reports, 8, 1350-1361. https://doi.org/10.1016/j.egyr.2021.12.057

Chen, C., Wu, D., Garibaldi, J. M., John, R. I., Twycross, J., & Mendel, J. M. (2020). A comprehensive study of the efficiency of type-reduction algorithms. IEEE Transactions on Fuzzy Systems, 29(6), 1556-1566. https://doi.org/ 10.1109/TFUZZ.2020.2981002

Doumi, M. H., Aissaoui, A., Tahour, A., Abid, M., & Tahir, K. (2016). Nonlinear integral backstepping control of wind energy conversion system based on a double-fed induction generator. Przegl?d Elektrotechniczny, 92(3), 130-135. https://doi.org /10.15199/48.2016.03.32

Heier, S. (2014). Grid integration of wind energy: onshore and offshore conversion systems. John Wiley & Sons.

Hemeyine, A. V., Abbou, A., Tidjani, N., Mokhlis, M., & Bakouri, A. (2020). Robust takagi sugeno fuzzy models control for a variable speed wind turbine based a DFI-generator. International Journal of Intelligent Engineering and Systems, 13(3), 90-100. https://doi.org/10.22266/ijies2020.0630.09

Hichem, H., Abderrazak, T. A., Iliace, A., Bendelhoum, M. S., & Abdelkrim, B. (2023). A new robust SIDA-PBC approach to control a DFIG. Bulletin of Electrical Engineering and Informatics, 12(3), 1310-1317. https://doi.org/10.11591/eei.v12i3.2155

Kadri, A., Marzougui, H., Aouiti, A., & Bacha, F. (2020). Energy management and control strategy for a DFIG wind turbine/fuel cell hybrid system with supercapacitor storage system. Energy, 192, 116518. https://doi.org/10.1016/j.energy.2019.116518

Kaloi, G. S., Baloch, M. H., Kumar, M., Soomro, D. M., Chauhdary, S. T., Memon, A. A., & Ishak, D. (2019). An LVRT scheme for grid connected DFIG based WECS using state feedback linearization control technique. Electronics, 8(7), 777. https://doi.org/10.3390/electronics8070777

Khan, D., Ahmed Ansari, J., Aziz Khan, S., & Abrar, U. (2020). Power optimization control scheme for doubly fed induction generator used in wind turbine generators. Inventions, 5(3), 40. https://doi.org/10.3390/inventions5030040

Kheir Saadaoui, B. B., Assas, O., & Khodja, M. A. (2019). Type-1 and type-2 fuzzy sets to control a nonlinear dynamic system. Revue d'Intelligence Artificielle, 33(1), 1-7. https://doi.org/10.18280/ria.330101

Li, K., Zhang, X., & Han, Y. (2023). Robot path planning based on interval type-2 fuzzy controller optimized by an improved Aquila optimization algorithm. IEEE Access. https://doi.org/10.1109/ACCESS.2023.3323437

Liang, Q., & Mendel, J. M. (2000). Interval type-2 fuzzy logic systems: theory and design. IEEE Transactions on Fuzzy Systems, 8(5), 535-550. https://doi.org/10.1109/91.873577

Magaji, N., Mustafa, M. W. B., Lawan, A. U., Tukur, A., Abdullahi, I., & Marwan, M. (2022). Application of Type 2 Fuzzy for Maximum Power Point Tracker for Photovoltaic System. Processes, 10(8), 1530. https://doi.org/10.3390/pr10081530

Milles, A., Merabet, E., Benbouhenni, H., Debdouche, N., & Colak, I. (2024). Robust control technique for wind turbine system with interval type-2 fuzzy strategy on a dual star induction generator. Energy Reports, 11, 2715-2736. https://doi.org/10.1016/j.egyr.2024.01.060

Minka, I., Essadki, A., Mensou, S., & Nasser, T. (2019). Primary frequency control applied to the wind turbine based on the DFIG controlled by the ADRC. International Journal of Power Electronics and Drive System, 10(2), 1049-1058. https://doi.org/ 10.11591/ijpeds

Mousa, H. H., Youssef, A. R., & Mohamed, E. E. (2020). Hybrid and adaptive sectors P&O MPPT algorithm based wind generation system. Renewable Energy, 145, 1412-1429. https://doi.org/10.1016/j.renene.2019.06.078

Okedu, K. E., Al Tobi, M., & Al Araimi, S. (2021). Comparative study of the effects of machine parameters on DFIG and PMSG variable speed wind turbines during grid fault. Frontiers in Energy Research, 9, 681443. https://doi.org/10.3389/fenrg.2021.681443

Sahri, Y., Tamalouzt, S., Lalouni Belaid, S., Bacha, S., Ullah, N., Ahamdi, A. A. A., & Alzaed, A. N. (2021). Advanced fuzzy 12 dtc control of doubly fed induction generator for optimal power extraction in wind turbine system under random wind conditions. Sustainability, 13(21), 11593. https://doi.org/10.3390/su132111593

Saihi, L., Berbaoui, B., Djilali, L., & Boura, M. (2023). Sensorless passivity based control of doubly-fed induction generators in variable-speed wind turbine systems based on high gain observer. Wind Engineering, 47(1), 86-103. https://doi.org/10.1177/0309524X221122531

Sanjuan, J. J. V., Flores, J. L., Mendoza, E. Y., & Tlaxcaltecatl, M. E. (2018, February). A sensorless passivity-based control for PMSM. In 2018 International Conference on Electronics, Communications and Computers (CONIELECOMP) (pp. 86-91). IEEE. https://doi.org/10.1109/CONIELECOMP.2018.8327180

Scarabaggio, P., Grammatico, S., Carli, R., & Dotoli, M. (2021). Distributed demand side management with stochastic wind power forecasting. IEEE Transactions on Control Systems Technology, 30(1), 97-112. https://doi.org/ 10.1109/TCST.2021.3056751

Shuaibu, M., Abubakar, A. S., & Shehu, A. F. (2021). Techniques for ensuring fault ride-through capability of grid connected dfig-based wind turbine systems: a review. Nigerian Journal of Technological Development, 18(1), 39-46. https://doi.org/ 10.4314/njtd.v18i1.6

Wu, D., & Mendel, J. M. (2019). Recommendations on designing practical interval type-2 fuzzy systems. Engineering Applications of Artificial Intelligence, 85, 182-193. https://doi.org/10.1016/j.engappai.2019.06.012

Xiong, L., Li, J., Li, P., Huang, S., Wang, Z., & Wang, J. (2021). Event triggered prescribed time convergence sliding mode control of DFIG with disturbance rejection capability. International Journal of Electrical Power & Energy Systems, 131, 106970. https://doi.org/10.1016/j.ijepes.2021.106970

Yan, S. R., Dai, Y., Shakibjoo, A. D., Zhu, L., Taghizadeh, S., Ghaderpour, E., & Mohammadzadeh, A. (2024). A fractional-order multiple-model type-2 fuzzy control for interconnected power systems incorporating renewable energies and demand response. Energy Reports, 12, 187-196. https://doi.org/10.1016/j.egyr.2024.06.018

Yang, B., Yu, T., Shu, H., Zhang, Y., Chen, J., Sang, Y., & Jiang, L. (2018). Passivity-based sliding-mode control design for optimal power extraction of a PMSG based variable speed wind turbine. Renewable energy, 119, 577-589. https://doi.org/10.1016/j.renene.2017.12.047

Yin, M., Xu, Y., Shen, C., Liu, J., Dong, Z. Y., & Zou, Y. (2016). Turbine stability-constrained available wind power of variable speed wind turbines for active power control. IEEE Transactions on Power Systems, 32(3), 2487-2488. https://doi.org/10.1109/TPWRS.2016.2605012