Abstract
The performance of a wind energy conversion system (WECS) under employing a permanent magnet synchronous generator (PMSG) is investigated in this article under MATLAB/Simulink software environment. An intelligent approach for performance investigation of direct-drive generator-based system for conversion of wind energy under variable speed operation is presented here. A peak (max.) power point (location) tracking (MPPT) that is based on traditional tip speed control (TSC) technique and artificial intelligence relied MPPT estimation procedure is used to mine the maximum energy obtainable from the wind energy conversion system. The used MPPTs control strategies regulate the optimal value of active reference current which is maintained by the grid side converter’s active current. Control strategy applied on converter (at the grid end) is used to regulate the overall power added to the grid in conjunction with converter on generator end so as to augment the per unit overall power from the generator.
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
References
R. Sitharthan, T. Parthasarathy, S. Sheeba Rani, K.C. Ramya, An improved radial basis function neural network control strategy-based maximum power point tracking controller for wind power generation system. Trans. Inst. Meas. Control 41(11), 3158–3170 (2019)
U. Yasin, Tracking of Maximum Power from Variable Speed Wind Using Fuzzy Controller Based on Permanent Magnet Synchronous Generator (Doctoral dissertation, ASTU) (2020)
M.A. Husain, A. Tariq, S. Hameed, M.S.B. Arif, A. Jain, Comparative assessment of maximum power point tracking procedures for photovoltaic systems. Green Energy & Environment 2(1), 5–17 (2017)
A. Nouriani, H. Moradi, Variable speed wind turbine power control: a comparison between multiple MPPT based methods. Int. J. Dyn. Control 1–14 (2021)
M. Naseem, M.A. Husain, A.F. Minai, A.N. Khan, M. Amir, J. Dinesh Kumar, A. Iqbal, Assessment of meta-heuristic and classical methods for GMPPT of PV system. Trans. Electr. Electron. Mater. 1–18 (2021)
M. Hannachi, O. Elbeji, M. Benhamed, L. Sbita, Comparative study of four MPPT for a wind power system. Wind Eng. (2021). 0309524X21995946
R. MelÃcio, V.M.F. Mendes, J.P.D.S. Catalão, Power converter topologies for wind energy conversion systems: integrated modeling, control strategy and performance simulation. Renew. Energy 35(10), 2165–2174 (2010)
A. Hebala, O. Hebala, W.A. Ghoneim, H.A. Ashour, Multi-objective particle swarm optimization of wind turbine directly connected PMSG, in 2017 Nineteenth International Middle East Power Systems Conference (MEPCON). IEEE, Dec 2017, pp. 1075–1080
A. Jain, S. Shankar, V. Vanitha, Power generation using permanent magnet synchronous generator (PMSG) based variable speed wind energy conversion system (WECS): an overview. J. Green Eng. 7(4), 477–504 (2017)
M.M. Amin, O.A. Mohammed, Development of a grid-connected wind generation system utilizing high frequency-based three-phase semicontrolled rectifier-current source inverter, in 2011 Twenty-Sixth Annual IEEE Applied Power Electronics Conference and Exposition (APEC). IEEE, Mar 2011, pp. 645–652
Y. Errami, M. Ouassaid, M. Maaroufi, Modeling and variable structure power control of PMSG based variable speed wind energy conversion system. J. Optoelectron. Adv. Mater. 15(November–December 2013) (2013), 1248–1255
M.A. Husain, A. Tariq, Modeling of a standalone Wind-PV Hybrid generation system using MATLAB/SIMULINK and its performance analysis. Int. J. Sci. Eng. Res 4(11), 1805–1811 (2013)
M. Tabrez, et al., A comparative simulation study of different sensorless permanent magnet synchronous motor drives using neural network and fuzzy logic. J. Intell. Fuzzy Syst. 35(5), 5177–5184 (2018)
S. Muyeen, R. Takahashi, T. Murata, J. Tamura, A variable speed wind turbine control strategy to meet wind farm grid code requirements. IEEE Trans. Power Syst. 25(1), 331–340 (2010)
M.A. Husain, A. Tariq, Modeling and study of a standalone PMSG wind generation system using MATLAB/SIMULINK. Univ. J. Electr. Electron. Eng. 2(7), 270–277 (2014)
E. Spooner, A.C. Williamson, Direct coupled, permanent magnet generators for wind turbine applications. IEE Proc.-Electr. Power Appl. 143(1), 1–8 (1996)
W.M. Lin, C.M. Hong, Intelligent approach to maximum power point tracking control strategy for variable-speed wind turbine generation system. Energy 35(6), 2440–2447 (2010)
G. Yang, Y. Zhu, Application of a matrix converter for PMSG wind turbine generation system, in The 2nd International Symposium on Power Electronics for Distributed Generation Systems. IEEE, June 2010, pp. 185–189
R. Chedid, F. Mard, M. Basma, Intelligent control of a class of wind energy conversion system. IEEE Trans. Energy Conv. EC 14, 1597–1604 (1999)
R. Tiwari, N. Ramesh Babu, Recent developments of control strategies for wind energy conversion system. Renew. Sustain. Energy Rev. 66, 268–285 (2016)
A.K. Yadav, et al., Soft computing in condition monitoring and diagnostics of electrical and mechanical systems, in Part of the Advances in Intelligent Systems and Computing, vol. 1096. Springer Nature, 2020, pp. 496. ISBN 978-981-15-1532-3. https://doi.org/10.1007/978-981-15-1532-3
A. Iqbal, et al., Meta heuristic and evolutionary computation: algorithms and applications, in Part of the Studies in Computational Intelligence, vol. 916. Springer Nature, 2020, p. 849. ISBN 978-981-15-7571-6. https://doi.org/10.1007/978-981-15-7571-6
J.A. Alzubi, AI and machine learning paradigms for health monitoring system: intelligent data analytics, in Part of the Studies in Big Data, vol. 86. Springer Nature, 2020, p. 513. ISBN: 978-981-33-4412-9. https://doi.org/10.1007/978-981-33-4412-9
A. Iqbal et al., Renewable power for sustainable growth, in Part of the Lecture Notes in Electrical Engineering, vol. 723. Springer Nature, 2021, 805 p) ISBN: 978-981-33-4080-0. https://doi.org/10.1007/978-981-33-4080-0
N. Fatema, et al., Intelligent Data-Analytics for Condition Monitoring: Smart Grid Applications (Academic Press, 2021). ISBN: 978-0-323-85510-5. https://doi.org/10.1016/C2020-0-02173-0
S. Srivastava, et al., Applications of Artificial Intelligence Techniques in Engineering, vol. 1, Part of the Advances in Intelligent Systems and Computing, vol. 698, 643 p (Springer Nature, 2018). ISBN 978-981-13-1819-1. https://doi.org/10.1007/978-981-13-1819-1
S. Srivastava et al., Applications of Artificial Intelligence Techniques in Engineering, vol. 2, Part of the Advances in Intelligent Systems and Computing, vol. 697, 647 p (Springer Nature, 2018). ISBN 978-981-13-1822-1. https://doi.org/10.1007/978-981-13-1822-1
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 Singapore Pte Ltd.
About this chapter
Cite this chapter
Husain, M.A., Singh, S.P., Tabrez, M. (2022). Intelligent Approach for Performance Investigation of Direct-Drive Generator-Based Wind Energy Conversion System Under Variable Speed Operation. In: Malik, H., Ahmad, M.W., Kothari, D. (eds) Intelligent Data Analytics for Power and Energy Systems. Lecture Notes in Electrical Engineering, vol 802. Springer, Singapore. https://doi.org/10.1007/978-981-16-6081-8_23
Download citation
DOI: https://doi.org/10.1007/978-981-16-6081-8_23
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-6080-1
Online ISBN: 978-981-16-6081-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)