Abstract
In this paper, an enhanced control scheme for doubly fed induction generators (DFIGs) operating in the standalone mode is proposed and experimentally validated. The intended aim is to regulate the amplitude and frequency of DFIG stator voltage profile, subjected to variations in the electrical load and rotor speed. For that, the excitation of DFIG is manipulated via rotor side converter (RSC), typically known as field-oriented control strategy fed by a hysteresis current controller. To reach the projected aim, a fuzzy logic controller (FLC) is implemented and systematically investigated. Compared to its counterpart Mamdani type, the fuzzy inference system (FIS) is based on Takagi–Sugeno (T–S) method, which is justified by comparatively lower computational burden and enhanced practical implementation. The advantages of the proposed T–S based FLC are highlighted by conducting a comprehensive comparison with the conventional PI controller and Mamdani type FLC. Both the simulations study and experimental verification are conducted under diverse scenarios: (i) a wide range of rotor speed, (ii) sudden variations in the electrical load, and (iii) step changes in the desired stator voltage profile. The simulation study and experimental analysis are, respectively, conducted via MATLAB/Simulink platform and dSPACE DS1104 platform. The results depict superior performance of proposed T–S type FLC scheme, when compared with the conventional PI controller. Moreover, based on experimental findings, the proposed scheme leverages 0.26 ms reduction in the computational time (vs Mamdani FLC), facilitating a superior practical implementation.
Similar content being viewed by others
References
Akin, E.; Aya, M.; Karakose, M.: A robust integrator algorithm with genetic based fuzzy controller feedback for direct vector control. Comput. Electr. Eng. 29, 379–94 (2003). https://doi.org/10.1016/S0045-7906(01)00
Elbouchikhi, E.; Feld, G.; Amirat, Y.; Benbouzid, M.; Le, Gall F.: Design and experimental implementation of wind energy a conversion platform with education and research capabilities. Comput. Electr. Eng. (2020). https://doi.org/10.1016/j.compeleceng;85.2020.106661
Tamaarat, A.; Benakcha, A.: Performance of PI controller for control of active and reactive power in DFIG operating in a grid-connected variable speed wind energy conversion system. Front Energy 8, 371–378 (2014). https://doi.org/10.1007/s11708-014-0318-6
Abdeddaim, S.; Betka, A.; Drid, S.; Becherif, M.: Implementation of MRAC controller of a DFIG based variable speed grid connected wind turbine. Energy Convers. Manag. 79, 281–288 (2014). https://doi.org/10.1016/j.enconman.2013.12.003
Mahvash, H.; Taher, S.A.; Rahimi, M.; Shahidehpour, M.: DFIG performance improvement in grid connected mode by using fractional order [PI] controller. Int. J. Electr. Power Energy Syst. 96, 398–411 (2018). https://doi.org/10.1016/j.ijepes.2017.10.008
El Azri, H.; Essadki, A.; Nasser, T.; LQR controller design for a nonlinear, doubly fed induction generator model. Proc 2018 6th Int Renew Sustain Energy Conf IRSEC 2018;5:1–6. (2018) https://doi.org/10.1109/IRSEC.2018.8702830.
Alrifai, M.; Zribi, M.; Rayan, M.: Feedback linearization controller for a wind energy power system. Energies (2016). https://doi.org/10.3390/en9100771
Mensou, S.; Essadki, A.; Nasser, T.; Idrissi, B.B.; Ben, T.L.: Dspace DS1104 implementation of a robust nonlinear controller applied for DFIG driven by wind turbine. Renew. Energy 147, 1759–1771 (2020). https://doi.org/10.1016/j.renene.2019.09.042
Merabet, A.; Eshaft, H.; Tanvir, A.A.: Power-current controller based sliding mode control for DFIG-wind energy conversion system. IET Renew. Power Generat. (2018). https://doi.org/10.1049/iet-rpg.2017.0313
Djilali, L.; Sanchez, E.N.; Belkheiri, M.: Real-time implementation of sliding-mode field-oriented control for a DFIG-based wind turbine. Int Trans. Electr. Energy Syst. (2018). https://doi.org/10.1002/etep.2539
Kelkoul, B.; Boumediene, A.: Stability analysis and study between classical sliding mode control (SMC) and super twisting algorithm (STA) for doubly fed induction generator (DFIG) under wind turbine. Energy 214, 118871 (2021). https://doi.org/10.1016/j.energy.2020.118871
Abdeddaim, S.; Betka, A.; Charrouf, O.: Optimal tracking and second order sliding power control of the DFIG wind turbine. AIP Conf Proc;1814. (2017). https://doi.org/10.1063/1.4976267.
Dieu Nguimfack-Ndongmo, J.; Kenné, G.; Nfah, E.M.: Design of nonlinear synergetic controller for transient stabilization enhancement of DFIG in multimachine wind power systems. Energy Procedia 93, 125–132 (2016). https://doi.org/10.1016/j.egypro.2016.07.160
Khoete, S.; Manabe, Y.; Kurimoto, M.; Funabashi, T.; Kato, T.: Robust h-infmity control for DFIG to enhance transient stability during grid faults. Lect Notes Eng Comput Sci 2226, 725–730 (2016)
Li, P.; Wang, J.; Xiong, L.; Huang, S.; Ma, M.; Wang, Z.: Energy-shaping controller for DFIG-based wind farm to mitigate subsynchronous control interaction. IEEE Trans Power Syst 36, 2975–2991 (2021). https://doi.org/10.1109/TPWRS.2020.3048141
Wang, J.; Peng, P.; Passivity-based control for doubly-fed induction generator with variable speed and constant frequency in wind power system. Electric, electronic and control engineering, ICEECE 2015. Phuket Island (Thailand): CRC Press; 2015. 5–6 March 2015.
Taveiros, F.E.V.; Barros, L.S.; Costa, F.B.: Back-to-back converter state-feedback control of DFIG (doubly-fed induction generator)-based wind turbines. Energy 89, 896–906 (2015). https://doi.org/10.1016/j.energy.2015.06.027
Abo-Khalil, A.G.; Alghamdi, A.; Tlili, I.; Eltamaly, A.M.: Current controller design for DFIG-based wind turbines using state feedback control. IET Renew. Power Gener. 13, 1938–1949 (2019). https://doi.org/10.1049/iet-rpg.2018.6030
Li, P.; Xiong, L.; Wu, F.; Ma, M.; Wang, J.: Sliding mode controller based on feedback linearization for damping of sub-synchronous control interaction in DFIG-based wind power plants. Int. J. Electr. Power Energy Syst. 107, 239–250 (2019). https://doi.org/10.1016/j.ijepes.2018.11.020
Roy TK, Mahmud MA. Hybrid Robust Adaptive Backstepping Sliding Mode Controller Design for Mitigating SSR in Series-Compensated DFIG-Based Wind Generation Systems. Conf Rec - IAS Annu Meet (IEEE Ind Appl Soc 2021;2021-October:2–7. doi:https://doi.org/10.1109/IAS48185.2021.9677445.
Ebrahimkhani, S.: Robust fractional order sliding mode control of doubly fed induction generator (DFIG)-based wind turbines. ISA Trans. 63, 343–354 (2016). https://doi.org/10.1016/j.isatra.2016.03.003
Saihi, L.; Berbaoui, B.; Glaoui, H.; Djilali, L.; Abdeldjalil, S.: Robust sliding mode H∞ controller of DFIG based on variable speed wind energy conversion system. Period Polytech. Electr. Eng. Comput. Sci. 64, 53–63 (2020). https://doi.org/10.3311/PPee.14490
Patel, R.; Hafiz, F.; Swain, A.; Ukil, A.: Nonlinear rotor side converter control of DFIG based wind energy system. Electr. Power Syst. Res. 198, 107358 (2021). https://doi.org/10.1016/j.epsr.2021.107358
Moreno-Valenzuela, J.; Quevedo-Pillado, Y.; Pérez-Aboytes, R.; González-Hernández, L.: Lyapunov-based adaptive control for the permanent magnet synchronous motor driving a robotic load. J. Circuits, Syst. Comput. (2017). https://doi.org/10.1142/S0218126617501687
Liu, X.D.; Li, K.; Zhang, C.H.: Improved Backstepping Control with Nonlinear Disturbance Observer for the Speed Control of Permanent Magnet Synchronous Motor. J. Electr. Eng. Technol. 14(1), 275–285 (2019). https://doi.org/10.1007/s42835-018-00021-9
Barambones, O.; Alkorta, P.; & De La Sen, M:. An adaptive sliding mode position control for induction motor drives. EUROCON 2011 - International Conference on Computer as a Tool - Joint with Conftele (2011). https://doi.org/10.1109/EUROCON.2011.5929166
Mossa, A.; Echeikh, H.; El Ouanjli, N.; Alhelou, H.H.: Enhanced Second-Order Sliding Mode Control Technique for a Five-Phase Induction Motor. Int. Transact. on Electr. Energy Syst. (2022). https://doi.org/10.1155/2022/8215525
Duarte-Mermoud, M.A.; Travieso-Torres, J.C.; Pelissier, I.S.; González, H.A.: Induction motor control based on adaptive passivity. Asian J. Cont. 14(1), 67–84 (2012). https://doi.org/10.1002/asjc.260
Bentaallah, A.; Massoum, A.; Benhamida, F.; Meroufel, A.: Adaptive feedback linearization control for asynchronous machine with nonlinear for natural dynamic complete observer. J. Electr. Eng. 63(2), 88–94 (2012). https://doi.org/10.2478/v10187-012-0013-y
Eminoǧlu, I.; Altaş, I.H.: The effects of the number of rules on the output of a fuzzy logic controller employed to a PM dc motor. Comput. Electr. Eng. 24, 245–61 (1998). https://doi.org/10.1016/S0045-7906(97)00021-9
Roose, A.I.; Yahya, S.; Al-Rizzo, H.: Fuzzy-logic control of an inverted pendulum on a cart. Comput. Electr. Eng. 61, 31–47 (2017). https://doi.org/10.1016/j.compeleceng.2017.05.016
Bounar, N.; Labdai, S.; Boulkroune, A.: PSO–GSA based fuzzy sliding mode controller for DFIG-based wind turbine. ISA Trans. 85, 177–188 (2019). https://doi.org/10.1016/j.isatra.2018.10.020
Dida, A.; Merahi, F.; Mekhilef, S.: New grid synchronization and power control scheme of doubly fed induction generator-based wind turbine system using fuzzy logic control. Comput. Electr. Eng. (2020). https://doi.org/10.1016/j.compeleceng.2020.106647
Ali, M.; Talha, A.; Berkouk, E.: New M5P model tree-based control for doubly fed induction generator in wind energy conversion system. Wind Energy 23, 1831–45 (2020). https://doi.org/10.1002/we.2519
Giannakis, A.; Karlis, A.; Karnavas, Y.L.: A combined control strategy of a DFIG based on a sensorless power control through modified phase-locked loop and fuzzy logic controllers. Renew. Energy 121, 489–501 (2018). https://doi.org/10.1016/j.renene.2018.01.052
Kasbi, A.; Rahali, A.: Performance optimization of doubly fed induction generator (DFIG) equipped variable-speed wind energy turbines by using three-level converter with adaptive fuzzy PI control system. Mater. Today Proc. 47, 2648–2656 (2021). https://doi.org/10.1016/j.matpr.2021.05.406
Ganthia, B.P.; Barik, S.K.; Nayak, B.: Genetic Algorithm Optimized and Type-I fuzzy logic-controlled power smoothing of mathematical modeled Type-III DFIG based wind turbine system. Mater. Today Proc. 56, 3355–3365 (2022). https://doi.org/10.1016/j.matpr.2021.10.193
Belmokhtar, K.; Doumbia, M.L.; Agbossou, K.: Novel fuzzy logic based sensorless maximum power point tracking strategy for wind turbine systems driven DFIG (doubly fed induction generator). Energy 76, 679–693 (2014). https://doi.org/10.1016/j.energy.2014.08.066
Elyazid, A.; Yanis, H.; Koussaila, I.; Kaci, G.; Djamal, A.; Azeddine, H: New Fuzzy Speed Controller for Dual Star Permanent Magnet Synchronous Motor. 2021 IEEE 1st Int Maghreb Meet Conf Sci Tech Autom Control Comput Eng MI-STA 2021 - Proc:69–73.https://doi.org/10.1109/MI-STA52233.2021.9464422.
Mesloub, H.; Benchouia, M.T.; Boumaaraf, R.; Goléa, A.; Goléa, N.; Becherif, M.: Design and implementation of DTC based on AFLC and PSO of a PMSM. Math Comput Simul 167, 340–355 (2020). https://doi.org/10.1016/j.matcom.2018.04.010
Farah, N.; Talib, M.H.N.; Mohd Shah, N.S.; Abdullah, Q.; Ibrahim, Z.; Lazi, J.B.M., et al.: A novel self-tuning fuzzy logic controller based induction motor drive system: an experimental approach. IEEE Access 7, 68172–68184 (2019). https://doi.org/10.1109/ACCESS.2019.2916087
Benchouia, M.T.; Ghadbane, I.; Golea, A.; Srairi, K.; Benbouzid, M.E.H.: Implementation of adaptive fuzzy logic and PI controllers to regulate the DC bus voltage of shunt active power filter. Appl. Soft Comput. J 28, 125–131 (2015). https://doi.org/10.1016/j.asoc.2014.10.043
Roselyn, J.P.; Chandran, C.P.; Nithya, C.; Devaraj, D.; Venkatesan, R.; Gopal, V., et al.: Design and implementation of fuzzy logic based modified real-reactive power control of inverter for low voltage ride through enhancement in grid connected solar PV system. Cont. Eng. Pract. 101, 104494 (2020). https://doi.org/10.1016/j.conengprac.2020.104494
Suganthi, L.; Iniyan, S.; Samuel, A.A.: Applications of fuzzy logic in renewable energy systems - a review. Renew. Sustain. Energy Rev. 48, 585–607 (2015). https://doi.org/10.1016/j.rser.2015.04.037
Farah, N.; Nizam Talib, M.H.; Ibrahim, Z.; Abdullah, Q.; Aydogdu, O.; Azri, M., et al.: Investigation of the Computational Burden Effects of Self-Tuning Fuzzy Logic Speed Controller of Induction Motor Drives with Different Rules Sizes. IEEE Access 9, 155443–155456 (2021). https://doi.org/10.1109/ACCESS.2021.3128351
Talib, M.H.N.; Ibrahim, Z., et al.: An improved simplified rules fuzzy logic speed controller method applied for induction motor drive. ISA Trans. 105, 230–9 (2020). https://doi.org/10.1016/j.isatra.2020.05.040
Tarbosh, Q.A.; Aydogdu, O.; Farah, N.; Talib, M.H.N.; Salh, A.; Cankaya, N., et al.: Review and investigation of simplified rules fuzzy logic speed controller of high-performance induction motor drives. IEEE Access 8, 49377–49394 (2020). https://doi.org/10.1109/ACCESS.2020.2977115
Ali, O.A.; Ali, A.Y.; Sumait, B.S.: ‘Comparison between the Effects of Different Types of Membership Functions on Fuzzy Logic Controller Performance. Int. J. Emerg. Eng. Res. Technol. 3(3), 76–83 (2015)
Kosko, B.; Mitaim, S What is the best shape for a fuzzy set-in function approximation? Proceedings of the 5th IEEE International Conference on Fuzzy Systems (FUZZ-96); September 1996. pp. 1237–1243
Introductory Chapter: Which Membership Function is Appropriate in Fuzzy System? BYAli Sadollah , Published: October 31st, 2018, DOI: https://doi.org/10.5772/intechopen.79552
Zhao, J.; and Bose, B. K. "Evaluation of membership functions for fuzzy logic-controlled induction motor drive,": In IEEE 2002 28th Annual Conference of the Industrial Electronics Society. IECON 02, , 1: IEEE, pp. 229–234, (2002) https://doi.org/10.1109/IECON.2002.1187512.
Naik, K.A.; Gupta, C.P.; Fernandez, E.: Design and implementation of interval type-2 fuzzy logic-PI based adaptive controller for DFIG based wind energy system. Int J Electr Power Energy Syst 115, 105468 (2020). https://doi.org/10.1016/j.ijepes.2019.105468
Raju, S.K.; Pillai, G.N.: Design and real time implementation of type-2 fuzzy vector control for DFIG based wind generators. Renew. Energy 88, 40–50 (2016). https://doi.org/10.1016/j.renene.2015.11.006
Demirbas, S.: Self-tuning fuzzy-PI-based current control algorithm for doubly fed induction generator. IET Renew. Power Gener. 11, 1714–1722 (2017). https://doi.org/10.1049/iet-rpg.2016.0700
Chabani, M.S.; Benchouia, M.T.; Golea, A.; Becherif, M.: Finite-state predictive current control of a standalone DFIG-based wind power generation systems: simulation and experimental analysis. J Control Autom. Electr. Syst. 32, 1332–1343 (2021). https://doi.org/10.1007/s40313-021-00750-9
Soued, S.; Chabani, M.S.; Becherif, M.; Benchouia, M.T.; Ramadan, H.S.; Betka, A., et al.: Experimental behaviour analysis for optimally controlled standalone DFIG system. IET Electr Power Appl 13, 1462–1473 (2019). https://doi.org/10.1049/iet-epa.2018.5648
Chabani, M. S.; Benchouia, M. T.; Golea, A.; & Boumaaraf, R. (2017). Implementation of direct stator voltage control of stand- alone DFIG based wind energy conversion system. In 2017 5th International Conference on Electrical Engineering - Boumerdes (ICEE-B).
Lin, F.J.; Huang, Y.S.; Tan, K.H.; Lu, Z.H.; Chang, Y.R.: Intelligent controlled doubly fed induction generator system using PFNN. Neural Comput. Appl. 22, 1695–1712 (2013). https://doi.org/10.1007/s00521-012-0965-7
Puchalapalli, S.; Singh, B.: A single input variable FLC for DFIG-Based WPGS in standalone mode. IEEE Trans. Sustain. Energy 11, 595–607 (2020). https://doi.org/10.1109/TSTE.2019.2898115
Ammar, A.; Talbi, B.; Ameid, T.; Azzoug, Y.; Kerrache, A.: Predictive direct torque control with reduced ripples for induction motor drive based on T-S fuzzy speed controller. Asian J Control 21, 2155–2166 (2019). https://doi.org/10.1002/asjc.2148
Funding
No funding was received for conducting this study.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors have no competing interests to declare that are relevant to the content of this article.
Appendix A: Major parameters of experimental bench
Appendix A: Major parameters of experimental bench
See Table 3
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Chabani, M.S., Benchouia, M.T., Golea, A. et al. Takagi–Sugeno Fuzzy Logic Controller for DFIG Operating in the Stand-Alone Mode: Simulations and Experimental Investigation. Arab J Sci Eng 48, 14605–14620 (2023). https://doi.org/10.1007/s13369-023-07704-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13369-023-07704-0