Abstract
In this research paper, a maximum power point tracking (MPPT) has been achieved using controllers based on artificial intelligence techniques, such as fuzzy logic (FLC), and artificial neural networks (ANN) controllers, since PI and PID classical controllers cannot give good performances in many applications that include strong nonlinearity caused by wind turbines aerodynamics, power converters of the conversion system, and the nature of wind flow. For this reason, we have proposed to use three MPPT control strategies; classical PI controller, fuzzy logic controller (FLC), and artificial neural network (ANN) controller. To avoid wind turbine catastrophes in high winds, the technique of pitch control has been investigated in parallel. Using MATLAB/Simulink, the proposed technique has been validated on a variable speed wind turbine with five-phase permanents magnets synchronous generator (PMSG) connected to a grid. The simulation results show the effectiveness of the proposed FLC and ANN controllers to achieve high tracking performance in the variable speed wind energy conversion systems (WECS).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Abdullah, M.A., Yatim, A.H.M., Tan, C.W., Saidur, R.: A review of maximum power point tracking algorithms for wind energy systems. Renew. Sustain. Energy Rev. 16(5), 3220–3227 (2012)
El Yaakoubi, A., Attari, K., Asselman, A., Djebli, A.: Novel power capture optimization based sensorless maximum power point tracking strategy and internal model controller for wind turbines systems driven SCIG. Front. Energy 1–15 (2017)
Ram, J.P., Rajasekar, N., Miyatake, M.: Design and overview of maximum power point tracking techniques in wind and solar photovoltaic systems: a review. Renew. Sustain. Energy Rev. 73, 1138–1159 (2017)
Sheikhan, N., Shahnazi, R., Yousefi, A.N.: An optimal fuzzy PI controller to capture the maximum power for variable speed wind turbines. J. Neural Comput. Appl. 23(5), 1359–1368 (2012)
Mousa, H.H.H., Youssef, A.-R., Mohamed, E.E.M.: Optimal power extraction control schemes for five-phase PMSG based wind generation systems. Eng. Sci. Technol. Int. J. (2019)
Rhaili, S., Abbou, A., Marhraoui, S., Moutchou, R., Hichami, N.: Robust sliding mode control with five sliding surfaces of five-phase PMSG based variable speed wind energy conversion system. Int. J. Intell. Eng. Syst. 13(4), 346–357 (2020)
Novaes-Menezes, E.J., Araújo, A.M., da Silva, N.S.B.: A review on wind turbine control and its associated methods. J. Clean. Prod. 174, 945–953 (2018)
Soued, S., Ebrahim, M.A., Ramadan, H.S., Becherif, M.: Optimal blade pitch control for enhancing the dynamic performance of wind power plants via metaheuristic optimizers. IET Electr. Power Appl. 11, 1432–1440 (2017)
Ren, Y., Li, L., Brindley, J., et al.: Nonlinear PI control for variable pitch wind turbine. J. Control Eng. Practice 50, 84–94 (2016)
Civelek, Z.: Optimization of fuzzy logic (Takagi-Sugeno) blade pitch angle controller in wind turbines by genetic algorithm. Eng. Sci. Technol. Int. J. 23, 1–9 (2020)
Thanh, S.N., Xuan, H.H., The, C.N., Hung, P.P., Van, T.P., Kennel, R.: Fuzzy logic based maximum power point tracking technique for a stand-alone wind energy system. In: Proceedings of the IEEE International Conference on Sustainable Energy Technologies (ICSET), Hanoi, Vietnam, 14–16 November 2016
Tiwari, R., Krishnamurthy, K., Neelakandan, R., Padmanaban, S., Wheeler, P.: Neural network based maximum power point tracking control with quadratic boost converter for PMSG—wind energy conversion system. Electronics 7, 20 (2018)
Rahman, M.M.A.; Rahim, A.H.M.A.: Performance evaluation of ANN and ANFIS based wind speed sensor-less MPPT controller. In: Proceedings of the 5th International Conference on Informatics, Electronics and Vision (ICIEV), Dhaka, Bangladesh, 13–14 May 2016
Chojaa, H., Derouich, A., Chehaidia, S.E., Zamzoum, O., Taoussi, M., Elouatouat, H.: Integral sliding mode control for DFIG based WECS with MPPT based on artificial neural network under a real wind profile. Energy Rep. 7, 4809–4824 (2021)
Nadour, M., Essadki, A., Nasser, T.: Comparative analysis between PI & backstepping control strategies of DFIG driven by wind turbine. Int. J. Renew. Energy Res. 7(3), 1307–1316 (2017)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Boulkhrachef, O., Hadef, M., Djerdir, A. (2022). Maximum Power Point Tracking of a Wind Turbine Based on Artificial Neural Networks and Fuzzy Logic Controllers. In: Lejdel, B., Clementini, E., Alarabi, L. (eds) Artificial Intelligence and Its Applications. AIAP 2021. Lecture Notes in Networks and Systems, vol 413. Springer, Cham. https://doi.org/10.1007/978-3-030-96311-8_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-96311-8_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-96310-1
Online ISBN: 978-3-030-96311-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)