@inproceedings{8a54d931401343bd821603f16ff6813c,
title = "Neuroadaptive speed assistance control of wind turbine with Variable Ratio Gearbox (VRG)",
abstract = "Wind power as a renewable energy source is irregular in occurrence. It is interesting yet challenging to maximize the energy capture from wind. Most existing control methods for wind power generation are traditionally based on wind turbine with fixed ratio gear box. In this work we investigate the control problem of wind power conversion by wind turbine with Variable-Ratio-Gearbox (VRG). In this setting, a permanent magnet synchronous motor (PMSM) unit is embedded into the system to enhance the generated power quality. This is achieved by regulating the PMSM speed properly to maintain constant (synchronous) speed of the generator over wide range of wind speed. Model-independent control algorithms are developed based on neuroadaptive backstepping approach. Both theoretical analysis and numerical simulation confirm that the proposed control scheme is able to ensure high precision motor speed tracking in the presence of parameter uncertainties and external load disturbances. {\textcopyright} 2012 Springer-Verlag.",
keywords = "Neuroadaptive Control, PMSM, Speed Regulation, Wind Turbine",
author = "WANG, \{Xue Fei\} and SONG, \{Yong Duan\} and LI, \{Dan Yong\} and Kai ZHANG and Shan XUE and Ming QIN",
year = "2012",
doi = "10.1007/978-3-642-31362-2\_60",
language = "English",
isbn = "9783642313615",
series = "Lecture Notes in Computer Science",
publisher = "Springer Berlin Heidelberg",
pages = "544--554",
editor = "Jun WANG and YEN, \{Gary G.\} and POLYCARPOU, \{Marios M.\}",
booktitle = "Advances in Neural Networks, ISNN 2012: 9th International Symposium on Neural Networks, Proceedings",
}