CONTRÔLE D'UN GÉNÉRATEUR À INDUCTION À DOUBLE ALIMENTATION À L'AIDE D'UN CONTRÔLEUR DE RÉSEAU DE NEURONES ARTIFICIEL
DOI :
https://doi.org/10.59277/RRST-EE.2023.68.1.8Mots-clés :
Eolienne à vitesse variable, Générateur à induction à double alimentation, Contrôle orienté terrain, Contrôle de puissance direct conventionnel, Distorsion harmonique totale, Suivi du point de puissance maximum, Réseau neuronal artificielRésumé
Dans cet article, nous proposons un contrôle direct de la puissance (DPC) basé sur un réseau de neurones artificiels (ANN-DPC) pour le générateur à induction à double alimentation (DFIG), qui est appliqué au système éolien. L'objectif principal de cette technique intelligente est de remplacer la table de commutation et les comparateurs à hystérésis par une commande neuronale pour réduire l'ondulation au niveau du courant et de la puissance. Le contrôle orienté champ (FOC) est traditionnellement réalisé à l'aide d'un contrôleur proportionnel-intégral (PI) conventionnel. Les ondulations de puissance sont réduites et un taux de distorsion harmonique total raisonnable est assuré en utilisant un ANN-DPC
Références
(1) ***Rapport, Le baromètre éolien ; systèmes solaires, journal des énergies renouvelables”, 183, Union Européen (Février 2008).
(2) L. Tuan, M.Q. Duong, G.N. Sava, V. Tanasiev, Optimization of renewable energy sources operation in Vietnam’s electricity market, Rev. Roum. Sci. Techn. – Électrotechn. Et Énerg., 65, 4, pp. 221–217, 2020.
(3) S. Abdelmalek, L. Barazane, A. Larabi, Fault diagnosis for a doubly fed induction generator, Revue Roumaine des Sciences Techniques–Électrotechnique et Énergetique, 61, 2, pp. 159–163 (2016).
(4) H. Nian, Y. Song, P. Zhou, Y. He, Improved direct power control of a wind turbine driven doubly fed induction generator during transient grid voltage unbalance, IEEE Trans. Energy Convers., 26, 3, pp. 976–986, Sep. 2011.
(5) S. Arezki, M. Boudour, Study and regulation of dc bus voltages of wind-photovoltaic system, Rev. Roum. Sci. Techn. – Électrotechn. et Énerg., 59, 1, pp. 35–46 (2014).
(6) O. Bachir, A.F. Zoubir, Comparative analysis of robust controller based on classical proportional-integral controller approach for power control of wind energy system, Revue Roumaine des Sciences Techniques–Électrotechnique et Énergetique, 63, 2, pp. 210–216 (2018).
(7) A.M. Eltamaly, M.S. Al-Saud, A.G. Abo-Khalil, Dynamic control of a DFIG wind power generation system to mitigate unbalanced grid voltage, IEEE Xplore, IEEE, pp. 39091–39103
(8) S. Siniscalchi-Minna, M. De-Prada-Gil, F.D. Bianchi, C. Ocampo-Martinez, B. De Schutter, A multi-objective predictive control strategy for enhancing primary frequency support with wind farms, IOP Conf. Series: Journal of Physics: Conf. Series 1037 (2018).
(9) I. Yaichi, A. Semmah, M. Djlaila, A. Harrouz, S. Mansouri, Y. Bakou, 'Modelling and control of doubly fed induction machine, application for a wind turbine System, IEEE Xplore Digital Library, International Renewable and Sustainable Energy Conference (IRSEC), Marrakech, Morocco (2016).
(10) O. Aouchenni, R. Babouri, K. Ghedamsi, D. Aouzellag, Wind farm based on doubly fed induction generator entirely interfaced with power grid through multilevel inverter, Rev. Roum. Sci. Techn.– Electrotechn. et Energ., 62, 2, pp. 170–174 (2017).
(11) A. Beddar, H. Bouzekri, B. Babes, H. Afghoul, Real time implementation of improved fractional-order proportional-integral controller for grid connected wind energy conversion system, Rev. Roum. Sci. Techn.– Electrotechn. et Energ., 61, 4, pp. 402–407 (2016).
(12) O. Bachir, A-F, Zoubir, Comparative analysis of robust controller based on classical proportional-integral controller approach for power control of wind energy system, Rev. Roum. Sci. Techn.– Electrotechn. et Energ., 63, 2, pp. 210–216 (2018).
(13) A. Tapia, G. Tapia, Modeling and control of a wind turbine driven doubly fed induction generator, IEEE Tran. on Energy Conversion, 18, 2, pp. 194–204 (2003).
(14) I. Yaichi, A. Semmah, P. Wira, Y. Djeriri, 'Super-twisting sliding mode control of a doubly-fed induction generator based on the SVM strategy'', Periodica Polytechnica Electrical Engineering and Computer Science, 63, 3, pp. 178–190 (2019).
(15) I. Izanlo, M.V. Kazemi, A. Gholamian, A new method of predictive direct torque control for doubly fed induction generator under unbalanced grid voltage, Rev. Roum. Sci. Techn.– Électrotechn. et Énerg., 63, 3, pp. 332–337, (2018).
(16) Y.K. Wu, W.H. Shu, H.Y. Cheng, G.T. Ye, D.C. Jiang, Mathematical modelling and simulation of the DFIG-based wind turbine, CACS International Automatic Control Conference, 2nd ed., 3, pp. 15–64 (2014).
(17) Hu, Y. Huang, D. Wang, H.Y X. Yuan, Modeling of grid-connected DFIG-based wind turbines for dc-link voltage stability analysis, IEEE Transactions on Sustainable Energy, 6, 4, pp. 1325–1336 (2015).
(18) S. Bellarbi, D.S. Koussa, Fuzzy robust control of double fed induction generator with parameter uncertainties, Rev. Roum. Sci. Techn.– Électrotechn. et Énerg., 61, 4, pp. 367–371 (2016).
(19) I. Ngom, A.B. Mbou, L. Thiaw, Active and reactive power control of doubly fed-induction generator based on variable speed wind power generation, International Conference on Electrical, Control, Automation and Robotics ECAR 2018.
(20) A. Khajeha, Z. Shabani, Adaptive gain scheduling control of doubly fed induction generator based wind turbines to improve fault ride through performance, Int. J. of Industrial Electronics, Control and Optimization, 1, 1, pp. 61-70 (June 2018).
(21) Azib, D. Ziane, T. Rekioua, A. Tounzi, Robustness of the direct torque control of double star induction motor in fault condition, Rev. Roum. Sci. Techn.– Électrotechn. et Énerg., 61, 1, pp. 147– 152 (2016).
(22) A. Aberbour, K. Idjdarene, Z. Boudries, Adaptable sliding mode control for wind energy application, Rev. Roum. Sci. Techn. – Électrotechn. et Énerg., 61, 3, pp. 258–262 (2016).
(23) M.J. Zandzadeh, A. Vahedi, Modelling and improvement of direct power control of DFIG under unbalanced grid voltage condition, Int. J. of Electrical Power and Energy Systems, 59, pp. 58-68 (Jan 2014).
(24) P.T. Sharadbhai, S. Gupta, Artificial neural network based control of doubly fed induction generator for active filtering capabilities, IEEE Xplore, 2021 4th International Conference on Recent Developments in Control, Automation & Power Engineering (RDCAPE), Noida, India.
(25) A. Asri, Y. Mihoub, S. Hassaine, P.O. Logerais, A. Amiar, T. Allaoui, An adaptive fuzzy proportional integral method for maximum power point tracking control of permanent magnet synchronous generator wind energy conversion system, Rev. Roum. Sci. Techn. – Électrotechn. Et Énerg., 63, 3, pp. 320-325 (2018).
(26) Y.A. Ali, M. Ouassaid, Advanced control strategy of DFIG based wind turbine using combined artificial neural network and PSO algorithm”, IEEE Xplore, 2020 International Conference on Electrical and Information Technologies (ICEIT), Rabat, Morocco.
(27) B.F. Florea, O. Grigore, M. Datcu, Learning online spatial exploration by optimizing artificial neural networks assisted by a pheromone map, Rev. Roum. Sci. Techn.– Electrotechn. et Energ., 62, 2, pp. 209–214 (2017).
(28) N.G. Lantewa, N. Magaji, Control of doubly fed induction generator of variable speed wind turbine system using neural network, IEEE Xplore, 2018 International Conference and Utility Exhibition on Green Energy for Sustainable Development (ICUE), Phuket, Thailand.
(29) ***IEEE Standards Association IEEE Std 519™-2014 IEEE Recommended Practice and Requirements for Harmonic Control in Electric Power Systems, The Institute of Electrical and Electronics Engineers, New York, USA, 2014. https://edisciplinas.usp.br/pluginfile.php/1589263/mod_resource/content/1/IEE%20Std%20519-2014.pdf [Accessed: 11 March 2018].
