Intelligent controlled three-phase squirrel-cage induction generator system using wavelet fuzzy neural network for wind power

An intelligent controlled three-phase squirrel-cage induction generator (SCIG) system for grid-connected wind power application using wavelet fuzzy neural network (WFNN) is proposed in this study. First, the indirect field-oriented mechanism is implemented for the control of the SCIG system. Then, a...

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Bibliographic Details
Published in:IET renewable power generation Vol. 7; no. 5; pp. 552 - 564
Main Authors: Lin, Faa-Jeng, Tan, Kuang-Hsiung, Fang, Dun-Yi, Lee, Yih-Der
Format: Journal Article
Language:English
Published: Stevenage The Institution of Engineering and Technology 01.09.2013
The Institution of Engineering & Technology
Subjects:
active power output
AC‐DC power converter
AC‐DC power convertors
Algorithms
asynchronous generators
backpropagation
backpropagation learning algorithm
constant‐frequency
constant‐voltage
DC‐AC power convertors
DC‐AC power inverter
DC‐link voltage controller
electric current control
Electric power
electric power generation
fuzzy neural nets
grid‐connected wind power application
indirect field‐oriented mechanism
Intelligent controlled three‐phase squirrel‐cage induction generator system
intelligent WFNN controller
machine vector control
neurocontrollers
phase control
power generation control
power grids
reactive power control
reactive power output
SCIG
tracking controller
variable‐frequency
variable‐voltage
wavelet fuzzy neural network
wavelet transforms
WFNN training
Wind power
wind power plants
indirect field-oriented mechanism
wavelet fuzzy neural network
wavelet transforms
AC-DC power converter
power generation control
constant-voltage
SCIG
Intelligent controlled three-phase squirrel-cage induction generator system
WFNN training
electric current control
grid-connected wind power application
backpropagation learning algorithm
constant-frequency
neurocontrollers
machine vector control
power grids
tracking controller
wind power plants
variable-frequency
intelligent WFNN controller
fuzzy neural nets
reactive power output
electric power generation
asynchronous generators
DC-AC power inverter
DC-link voltage controller
DC-AC power convertors
variable-voltage
backpropagation
phase control
active power output
reactive power control
AC-DC power convertors
ISSN:1752-1416, 1752-1424, 1752-1424
Online Access:Get full text
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Description
Summary:An intelligent controlled three-phase squirrel-cage induction generator (SCIG) system for grid-connected wind power application using wavelet fuzzy neural network (WFNN) is proposed in this study. First, the indirect field-oriented mechanism is implemented for the control of the SCIG system. Then, an AC/DC power converter and a DC/AC power inverter are developed to convert the electric power generated by a three-phase SCIG from variable-voltage and variable-frequency to constant-voltage and constant-frequency. Moreover, the intelligent WFNN controller is proposed for both the AC/DC power converter and DC/AC power inverter to improve the transient and steady-state responses of the SCIG system at different operating conditions. Three online trained WFNNs using backpropagation learning algorithm are implemented as the tracking controllers for the DC-link voltage of the AC/DC power converter and the active power and reactive power outputs of the DC/AC power inverter. Furthermore, the network structure and the online learning algorithm of the WFNN are introduced in detail. Finally, some experimental results are provided to demonstrate the effectiveness of the proposed SCIG system for wind power.
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ISSN:1752-1416
1752-1424
1752-1424
DOI:10.1049/iet-rpg.2012.0201