The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
An Intelligent Maximum Power Extraction Algorithm for Inverter-Based Variable Speed Wind Turbine Systems
Abstract
This paper focuses on the development of maximum wind power extraction algorithms for inverter-based variable speed wind power generation systems. A review of existing maximum wind power extraction algorithms is presented in this paper, based on which an intelligent maximum power extraction algorithm is developed by the authors to improve the system performance and to facilitate the control implementation. As an integral part of the max-power extraction algorithm, advanced hill-climb searching method has been developed to take into account the wind turbine inertia. The intelligent memory method with an on-line training process is described in this paper. The developed maximum wind power extraction algorithm has the capability of providing initial power demand based on error driven control, searching for the maximum wind turbine power at variable wind speeds, constructing an intelligent memory, and applying the intelligent memory data to control the inverter for maximum wind power extraction, without the need for either knowledge of wind turbine characteristics or the measurements of mechanical quantities such as wind speed and turbine rotor speed. System simulation results and test results have confirmed the functionality and performance of this method.
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Johnson, G. L., & Moretti, P. M. (1985) · Journal of Solar Energy Engineering · doi:10.1115/1.3267708 · Cited by 585
Design and performance evaluation of a fuzzy-logic-based variable-speed wind generation system
Simoes, M. G., Bose, B. K., & Spiegel, R. J. (1997) · IEEE Transactions on Industry Applications · doi:10.1109/28.605737 · Cited by 301
Hilloowala, R. M., & Sharaf, A. M. (1996) · IEEE Transactions on Industry Applications · doi:10.1109/28.485813 · Cited by 247
Cited by
High-power wind energy conversion systems: State-of-the-art and emerging technologies
Yaramasu, V., Wu, B., Sen, P. C., Kouro, S., & Narimani, M. (2015) · Proceedings of the IEEE · doi:10.1109/jproc.2014.2378692 · Cited by 991
A review of maximum power point tracking algorithms for wind energy systems
Abdullah, M. A., Yatim, A. H. M., Tan, C. W., & Saidur, R. (2012) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2012.02.016 · Cited by 770
The state of the art of wind energy conversion systems and technologies: A review
Cheng, M., & Zhu, Y. (2014) · Energy Conversion and Management · doi:10.1016/j.enconman.2014.08.037 · Cited by 600
Neural-Network-Based MPPT Control of a Stand-Alone Hybrid Power Generation System
Lin, W.-M., Hong, C.-M., & Chen, C.-H. (2011) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2011.2161775 · Cited by 347
Zhao, Y., Wei, C., Zhang, Z., & Qiao, W. (2013) · IEEE Journal of Emerging and Selected Topics in Power Electronics · doi:10.1109/jestpe.2013.2280572 · Cited by 274
Metadata
- Authors
- Wang, Q., & Chang, L.
- Venue
- IEEE Transactions on Power Electronics
- Published
- 2004-09
- Volume
- 19
- Issue
- 5
- Pages
- 1242-1249
- ISSN
- 0885-8993 (ISSN-L)
- OpenAlex
- W2158937797
- Cited by
- 555
- References
- 3
- FWCI
- 9.48
- Type
- journal article
- Language
- en
- Open access
- closed
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- Wang, Q., & Chang, L. (2004). An Intelligent Maximum Power Extraction Algorithm for Inverter-Based Variable Speed Wind Turbine Systems. IEEE Transactions on Power Electronics. https://doi.org/10.1109/tpel.2004.833459
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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