Recurrent functional-link-based fuzzy-neural-network-controlled induction-generator system using improved particle swarm optimization

Faa Jeng Lin; Li Tao Teng; Jeng Wen Lin; Syuan Yi Chen

doi:10.1109/TIE.2008.2010105

Recurrent functional-link-based fuzzy-neural-network-controlled induction-generator system using improved particle swarm optimization

Faa Jeng Lin^*, Li Tao Teng, Jeng Wen Lin, Syuan Yi Chen

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

90 Citations (Scopus)

Abstract

A recurrent functional-link (FL)-based fuzzy-neural-network (FNN) controller with improved particle swarm optimization (IPSO) is proposed in this paper to control a three-phase induction-generator (IG) system for stand-alone power application. First, an indirect field-oriented mechanism is implemented for the control of the IG. Then, an ac/dc power converter and a dc/ac power inverter are developed to convert the electric power generated by a three-phase IG from variable frequency and variable voltage to constant frequency and constant voltage, respectively. Moreover, two online-trained recurrent FL-based FNNs are introduced as the regulating controllers for both the dc-link voltage of the ac/dc power converter and the ac line voltage of the dc/ac power inverter. Furthermore, IPSO is adopted to adjust the learning rates to improve the online learning capability of the recurrent FL-based FNNs. Finally, some experimental results are provided to demonstrate the effectiveness of the proposed recurrent FL-based FNN-controlled IG system.

Original language	English
Pages (from-to)	1557-1577
Number of pages	21
Journal	IEEE Transactions on Industrial Electronics
Volume	56
Issue number	5
DOIs	https://doi.org/10.1109/TIE.2008.2010105
Publication status	Published - 2009
Externally published	Yes

Keywords

Functional link neural network (FLNN)
Induction generator
Induction generator (IG)
Particle swarm optimization (PSO)
Power converter

ASJC Scopus subject areas

Control and Systems Engineering
Electrical and Electronic Engineering

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10.1109/TIE.2008.2010105

Cite this

@article{59b769b6a9ab49a8912e1c2022fa27c7,

title = "Recurrent functional-link-based fuzzy-neural-network-controlled induction-generator system using improved particle swarm optimization",

abstract = "A recurrent functional-link (FL)-based fuzzy-neural-network (FNN) controller with improved particle swarm optimization (IPSO) is proposed in this paper to control a three-phase induction-generator (IG) system for stand-alone power application. First, an indirect field-oriented mechanism is implemented for the control of the IG. Then, an ac/dc power converter and a dc/ac power inverter are developed to convert the electric power generated by a three-phase IG from variable frequency and variable voltage to constant frequency and constant voltage, respectively. Moreover, two online-trained recurrent FL-based FNNs are introduced as the regulating controllers for both the dc-link voltage of the ac/dc power converter and the ac line voltage of the dc/ac power inverter. Furthermore, IPSO is adopted to adjust the learning rates to improve the online learning capability of the recurrent FL-based FNNs. Finally, some experimental results are provided to demonstrate the effectiveness of the proposed recurrent FL-based FNN-controlled IG system.",

keywords = "Functional link neural network (FLNN), Induction generator, Induction generator (IG), Particle swarm optimization (PSO), Power converter",

author = "Lin, {Faa Jeng} and Teng, {Li Tao} and Lin, {Jeng Wen} and Chen, {Syuan Yi}",

note = "Funding Information: Manuscript received June 9, 2008; revised October 27, 2008. First published December 2, 2008; current version published April 29, 2009. This work was supported by the National Science Council, Taiwan, under Grant NSC 96-2221-E-008-125.",

year = "2009",

doi = "10.1109/TIE.2008.2010105",

language = "English",

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AU - Lin, Faa Jeng

AU - Teng, Li Tao

AU - Lin, Jeng Wen

AU - Chen, Syuan Yi

N1 - Funding Information: Manuscript received June 9, 2008; revised October 27, 2008. First published December 2, 2008; current version published April 29, 2009. This work was supported by the National Science Council, Taiwan, under Grant NSC 96-2221-E-008-125.

PY - 2009

Y1 - 2009

N2 - A recurrent functional-link (FL)-based fuzzy-neural-network (FNN) controller with improved particle swarm optimization (IPSO) is proposed in this paper to control a three-phase induction-generator (IG) system for stand-alone power application. First, an indirect field-oriented mechanism is implemented for the control of the IG. Then, an ac/dc power converter and a dc/ac power inverter are developed to convert the electric power generated by a three-phase IG from variable frequency and variable voltage to constant frequency and constant voltage, respectively. Moreover, two online-trained recurrent FL-based FNNs are introduced as the regulating controllers for both the dc-link voltage of the ac/dc power converter and the ac line voltage of the dc/ac power inverter. Furthermore, IPSO is adopted to adjust the learning rates to improve the online learning capability of the recurrent FL-based FNNs. Finally, some experimental results are provided to demonstrate the effectiveness of the proposed recurrent FL-based FNN-controlled IG system.

AB - A recurrent functional-link (FL)-based fuzzy-neural-network (FNN) controller with improved particle swarm optimization (IPSO) is proposed in this paper to control a three-phase induction-generator (IG) system for stand-alone power application. First, an indirect field-oriented mechanism is implemented for the control of the IG. Then, an ac/dc power converter and a dc/ac power inverter are developed to convert the electric power generated by a three-phase IG from variable frequency and variable voltage to constant frequency and constant voltage, respectively. Moreover, two online-trained recurrent FL-based FNNs are introduced as the regulating controllers for both the dc-link voltage of the ac/dc power converter and the ac line voltage of the dc/ac power inverter. Furthermore, IPSO is adopted to adjust the learning rates to improve the online learning capability of the recurrent FL-based FNNs. Finally, some experimental results are provided to demonstrate the effectiveness of the proposed recurrent FL-based FNN-controlled IG system.

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KW - Particle swarm optimization (PSO)

KW - Power converter

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Recurrent functional-link-based fuzzy-neural-network-controlled induction-generator system using improved particle swarm optimization

Abstract

Keywords

ASJC Scopus subject areas

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