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ISSN (Print): 2352-0965
ISSN (Online): 2352-0973

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Research Article

Crowbar Protection and Blades Pitch Angle Control of a Wind Turbine at Severe Faulty Conditions using Adaptive Neuro-Fuzzy Inference System

Author(s): Ayman Safwat*, Mohiy El-Sayed. Bahgat, Abdel-Ghany Muhammad Abdel-Ghany and Helmy Mohammad El Zoghby

Volume 16, Issue 4, 2023

Published on: 21 November, 2022

Page: [362 - 371] Pages: 10

DOI: 10.2174/2352096516666221103102058

Price: $65

Abstract

Background: Due to their superior efficiency, stability, and ability to produce maximum power under various typical operating situations, wind turbines driving doubly fed induction generator systems are frequently utilized in wind power extraction. These systems face stability problems especially at severe faulty conditions.

Objective: To protect the rotating parts of the system from over speeding when the fault occurs and to ensure that the generator does not deviate from stability by adjusting the aerodynamic torque of the wind turbine. In addition, to protect electrical parts of the system, especially DC bus voltage and power electronics converters.

Methods: Using Adaptive Neuro-Fuzzy Inference System (ANFIS). The proposed ANFIS technique detects the faulty conditions from the measured voltages and currents at the terminals of the generator. In case of faulty cases, an ANFIS technology activates the wind turbine's pitch angle controller and the crowbar resistance.

Results: A comparison between the behavior of DFIG at faulty conditions without any fault controller and with the proposed ANFIS technique is applied. When the ANFIS technique is used, the wind system's performance and response are improved.

Conclusion: The proposed ANFIS control system has proven its effectiveness in protecting the DFIG in the event of a grid fault.

Keywords: Wind energy, DFIG, fault, ANFIS, crowbar protection, pitch angle control.

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[1]
R.M. Elavarasan, G.M. Shafiullah, S. Padmanaban, N.M. Kumar, A. Annam, A.M. Vetrichelvan, L. Mihet-Popa, and J.B. Holm-Nielsen, "A comprehensive review on renewable energy development, challenges, and policies of leading Indian states with an international perspective", IEEE Access, vol. 8, pp. 74432-74457, 2020.
[http://dx.doi.org/10.1109/ACCESS.2020.2988011]
[2]
T. Lehtola, and A. Zahedi, "Solar energy and wind power supply supported by storage technology: A review", Sustain. Energy Technol. Assess., vol. 35, pp. 25-31, 2019.
[http://dx.doi.org/10.1016/j.seta.2019.05.013]
[3]
Y. Yu, H. Cao, X. Yan, T. Wang, and S.S. Ge, "Defect identification of wind turbine blades based on defect semantic features with transfer feature extractor", Neurocomputing, vol. 376, pp. 1-9, 2020.
[http://dx.doi.org/10.1016/j.neucom.2019.09.071]
[4]
K.B. Tawfiq, A.S. Mansour, H.S. Ramadan, M. Becherif, and E.E. El-kholy, "Wind energy conversion system topologies and converters: Comparative review", Energy Procedia, vol. 162, pp. 38-47, 2019.
[http://dx.doi.org/10.1016/j.egypro.2019.04.005]
[5]
B.P. Ganthia, S.K. Barik, and B. Nayak, Wind Turbines in Energy Conversion System: Types & Techniques.In: Renewable Energy and Future Power Systems., Springer: Singapore, 2021, pp. 199-217.
[http://dx.doi.org/10.1007/978-981-33-6753-1_9]
[6]
R. Sitharthan, M. Karthikeyan, D.S. Sundar, and S. Rajasekaran, "Adaptive hybrid intelligent MPPT controller to approximate effectual wind speed and optimal rotor speed of variable speed wind turbine", ISA Trans., vol. 96, pp. 479-489, 2020.
[http://dx.doi.org/10.1016/j.isatra.2019.05.029] [PMID: 31202532]
[7]
W. Cao, Y. Xie, and T. Zheng, "Wind turbine generator technologies", Advances in Wind Power, vol. 1, no. 1, pp. 177-204, 2012.
[8]
D.K. Bhutto, J. Ahmed Ansari, S.S. Hussain Bukhari, and F. Akhtar Chachar,
Wind energy conversion systems (WECS) Generators: A review 2019 2nd International Conference on Computing, Mathematics and Engineering Technologies, Jan 30-31, 2019, Sukkur, Pakistan, pp. 1-6. [http://dx.doi.org/10.1109/ICOMET.2019.8673429]
[9]
O. Apata, and D.T.O. Oyedokun,
Wind turbine generators: Conventional and emerging technologies Proceedings - 2017 IEEE PES-IAS PowerAfrica Conference: Harnessing Energy, Information and Communications Technology (ICT) for Affordable Electrification of Africa, Jun 27-30, 2017, Accra, Ghana, pp. 606- 611, 2017. [http://dx.doi.org/10.1109/PowerAfrica.2017.7991295]
[10]
R. Hiremath, and T. Moger, "Modified Super Twisting algorithm based sliding mode control for LVRT enhancement of DFIG driven wind system", Energy Rep., vol. 8, pp. 3600-3613, 2022.
[http://dx.doi.org/10.1016/j.egyr.2022.02.235]
[11]
S. Soued, M.S. Chabani, M. Becherif, M.T. Benchouia, H.S. Ramadan, A. Betka, A. Golea, and S.E. Zouzou, "Experimental behaviour analysis for optimally controlled standalone DFIG system", IET Electr. Power Appl., vol. 13, no. 10, pp. 1462-1473, 2019.
[http://dx.doi.org/10.1049/iet-epa.2018.5648]
[12]
S. Ghosh, Y.J. Isbeih, R. Bhattarai, M.S.E. Moursi, E.F. El-Saadany, and S. Kamalasadan, "A dynamic coordination control architecture for reactive power capability enhancement of the DFIG-based wind power generation", IEEE Trans. Power Syst., vol. 35, no. 4, pp. 3051-3064, 2020.
[http://dx.doi.org/10.1109/TPWRS.2020.2968483]
[13]
A. Dahbi, A. Reama, M. Hamouda, and N.N. Said, "Control and study of a real wind turbine Comput", Electr. Eng., vol. 80. 2019
[14]
H. Jenkal, B. Bossoufi, A. Boulezhar, A. Lilane, and S. Hariss, "Vector control of a doubly fed induction generator wind turbine", Mater. Today Proc., vol. 30, pp. 976-980, 2020.
[http://dx.doi.org/10.1016/j.matpr.2020.04.360]
[15]
S. Gupta, and A. Shukla, "Improved dynamic modelling of DFIG driven wind turbine with algorithm for optimal sharing of reactive power between converters Sustain", Energy Technol. Assess..
vol. 51, p. 101961, 2022. [http://dx.doi.org/10.1016/j.seta.2022.101961]
[16]
M.M. Ahmed, W.S. Hassanein, N.A. Elsonbaty, and M.A. Enany, "Proposing and evaluation of MPPT algorithms for high-performance stabilized WIND turbine driven DFIG", Alex. Eng. J., vol. 59, no. 6, pp. 5135-5146, 2020.
[http://dx.doi.org/10.1016/j.aej.2020.09.043]
[17]
Z. Rafiee, R. Heydari, M. Rafiee, M.R. Aghamohammadi, and F. Blaabjerg, "Enhancement of the LVRT capability for DFIG-based wind farms based on short-circuit capacity", IEEE Syst. J., vol. 16, no. 2, pp. 3237-3248, 2022.
[http://dx.doi.org/10.1109/JSYST.2022.3153887]
[18]
S. Li, X. Yao, and J. Liu, "Fuzzy-ADRC strategy to DFIG low-voltage ride through", Smart Innov. Sys. Technol., vol. 258, pp. 75-85, 2022.
[http://dx.doi.org/10.1007/978-981-16-5168-7_10]
[19]
M. Shuaibu, A.S. Abubakar, and A.F. Shehu, "Techniques for ensuring fault ride-through capability of grid connected DFIG-based wind turbine systems: A review", Niger. J. Technol. Dev., vol. 18, no. 1, pp. 39-46, 2021.
[http://dx.doi.org/10.4314/njtd.v18i1.6]
[20]
I. Hamdan, and O. Noureldeen, "An Overview of control method with various crowbar techniques of wind turbines during power system faults", Int. J. Eng. Res. Appl., vol. 2, no. 1, pp. 35-45, 2021.
[http://dx.doi.org/10.21608/svusrc.2021.80571.1010]
[21]
N.Z. Ibrahim, A. Al-Quteimat, M. Mozumdar, and S. al Jufout, "A novel approach for crowbar resistance determination for doubly fed induction generators in wind energy conversion systems", Int. J. Ambient Energy, vol. 43, pp. 1-10, 2021.
[22]
M.M. Ali, A.R. Youssef, A.S. Ali, and G.T. Abdel-Jaber, "Comparative study of different pitch angle control strategies for DFIG based on wind energy conversion system", Int. J. Renew. Energy Res., vol. 9, no. 1, pp. 157-163, 2019.
[23]
A.G. Abo-Khalil, and S. Alyami, "Dynamic modeling of wind turbines based on estimated wind speed under turbulent conditions", J. Energy, vol. 12, no. 10, 2017.
[24]
B. Benyachou, B. Bahrar, A. Moufakkir, K. Gueraoui, and M. Saidi Hassani Alaoui, "Optimization & control strategy for offshore wind turbine based on a dual fed induction generator", Mater. Today Proc., vol. 58, pp. 1470-1475, 2022.
[http://dx.doi.org/10.1016/j.matpr.2022.02.546]
[25]
A. Onan, "“Bidirectional convolutional recurrent neural network architecture with group-wise enhancement mechanism for text sentiment classification”, J. King Saud Univ. -", Comput. Inf. Sci., vol. 34, no. 5, pp. 2098-2117, 2022.
[http://dx.doi.org/10.1016/j.jksuci.2022.02.025]
[26]
A. Onan, and S. Korukoğlu, "A feature selection model based on genetic rank aggregation for text sentiment classification", J. Inf. Sci., vol. 43, no. 1, pp. 25-38, 2017.
[http://dx.doi.org/10.1177/0165551515613226]
[27]
A. Onan, and M.A. Toçoğlu, "A term weighted neural language model and stacked bidirectional LSTM based framework for sarcasm identification IEEE Access, vol", Vol. 9, pp. 7701-7722, 2021.
[28]
D.J. Armaghani, and P.G. Asteris, "A comparative study of ANN and ANFIS models for the prediction of cement-based mortar materials compressive strength", Neural Comput. Appl., vol. 33, no. 9, pp. 4501-4532, 2021.
[http://dx.doi.org/10.1007/s00521-020-05244-4]
[29]
M. Bahiraei, S. Nazari, and H. Safarzadeh, "Modeling of energy efficiency for a solar still fitted with thermoelectric modules by ANFIS and PSO-enhanced neural network: A nanofluid application", Powder Technol., vol. 385, pp. 185-198, 2021.
[http://dx.doi.org/10.1016/j.powtec.2021.03.001]
[30]
M.A.A. Al-qaness, A.A. Ewees, H. Fan, L. Abualigah, and M.A. Elaziz, "Boosted ANFIS model using augmented marine predator algorithm with mutation operators for wind power forecasting", Appl. Energy.
vol. 314, pp. 118851, 2022. [http://dx.doi.org/10.1016/j.apenergy.2022.118851]
[31]
A. Onan, S. Korukoğlu, and H. Bulut, "A hybrid ensemble pruning approach based on consensus clustering and multi-objective evolutionary algorithm for sentiment classification", Inf. Process. Manage., vol. 53, no. 4, pp. 814-833, 2017.
[http://dx.doi.org/10.1016/j.ipm.2017.02.008]
[32]
A. Onan, "An ensemble scheme based on language function analysis and feature engineering for text genre classification", J. Inf. Sci., vol. 44, no. 1, pp. 28-47, 2018.
[http://dx.doi.org/10.1177/0165551516677911]
[33]
A. Onan, "Consensus clustering-based undersampling approach to imbalanced learning", Sci. Program., vol. 2019, pp. 1-14, 2019.
[http://dx.doi.org/10.1155/2019/5901087]
[34]
A. Onan, "Mining opinions from instructor evaluation reviews: A deep learning approach", Comput. Appl. Eng. Educ., vol. 28, no. 1, pp. 117-138, 2020.
[http://dx.doi.org/10.1002/cae.22179]
[35]
A. Onan, "Deep Learning Based Sentiment Analysis on Product Reviews on Twitter In: Younas, M., Awan, I., Benbernou, S. Eds. Big Data Innovations and Applications. Innovate-Data 2019. Communications in Computer and Information Science, Istanbul, Turkey, August",
26-28, 2019, vol 1054. Springer, Cham, 2019. [http://dx.doi.org/10.1007/978-3-030-27355-2_6]
[36]
A. Onan, "Sentiment analysis on product reviews based on weighted word embeddings and deep neural networks", Concurr. Comput..
vol. 33, no. 23, p. e5909, 2021. [http://dx.doi.org/10.1002/cpe.5909]
[37]
A. Onan, "Two-stage topic extraction model for bibliometric data analysis based on word embeddings and clustering", IEEE Access, vol. 7, pp. 145614-145633, 2019.
[http://dx.doi.org/10.1109/ACCESS.2019.2945911]
[38]
N. Priyadarshi, S. Padmanaban, J.B. Holm-Nielsen, F. Blaabjerg, and M.S. Bhaskar, "An experimental estimation of hybrid ANFIS–PSO-Based MPPT for PV grid integration under fluctuating sun irradiance", IEEE Syst. J., vol. 14, no. 1, pp. 1218-1229, 2020.
[http://dx.doi.org/10.1109/JSYST.2019.2949083]
[39]
B. Kelkoul, and A. Boumediene, "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.
vol. 214, pp. 118871, 2021. [http://dx.doi.org/10.1016/j.energy.2020.118871]
[40]
M.R. Islam, J. Hasan, M.R.R. Shipon, M.A.H. Sadi, A. Abuhussein, and T.K. Roy, "Neuro fuzzy logic controlled parallel resonance type fault current limiter to improve the fault ride through capability of DFIG based wind farm", IEEE Access, vol. 8, pp. 115314-115334, 2020.
[http://dx.doi.org/10.1109/ACCESS.2020.3000462]
[41]
A. Sajadi, Ł. Rosłaniec, M. Kłos, P. Biczel, and K.A. Loparo, "An emulator for fixed pitch wind turbine studies", Renew. Energy, vol. 87, pp. 391-402, 2016.
[http://dx.doi.org/10.1016/j.renene.2015.10.033]
[42]
H. Chojaa, A. Derouich, S.E. Chehaidia, O. Zamzoum, M. Taoussi, and H. Elouatouat, "Integral sliding mode control for DFIG based WECS with MPPT based on artificial neural network under a real wind profile", Energy Rep., vol. 7, pp. 4809-4824, 2021.
[http://dx.doi.org/10.1016/j.egyr.2021.07.066]

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