Hybrid control of a wind induction generator based on grey-elman neural network

Whei Min Lin; Chih Ming Hong; Cong Hui Huang; Ting Chia Ou

doi:10.1109/TCST.2012.2231865

Hybrid control of a wind induction generator based on grey-elman neural network

Whei Min Lin, Chih Ming Hong, Cong Hui Huang, Ting Chia Ou

Program on Smart and Sustainable Manufacturing

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

38 Citations (Scopus)

Abstract

This brief presents the design of an optimal wind energy control system for maximum power point tracking. With the help of a grey predictor for the preprocessor, a high-performance online training Elman neural network (ENN) is designed to derive the turbine speed needed to extract maximum power from wind. Moreover, the connective weights of the improved ENN are trained online by the backpropagation learning algorithm. Compared to earlier methods, better results are obtained when the ENN controller is used together with the grey system modeling approach. Performance of the proposed approach is verified by the experimental results.

Original language	English
Article number	6407971
Pages (from-to)	2367-2373
Number of pages	7
Journal	IEEE Transactions on Control Systems Technology
Volume	21
Issue number	6
DOIs	https://doi.org/10.1109/TCST.2012.2231865
Publication status	Published - 2013

All Science Journal Classification (ASJC) codes

Control and Systems Engineering
Electrical and Electronic Engineering

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10.1109/TCST.2012.2231865

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abstract = "This brief presents the design of an optimal wind energy control system for maximum power point tracking. With the help of a grey predictor for the preprocessor, a high-performance online training Elman neural network (ENN) is designed to derive the turbine speed needed to extract maximum power from wind. Moreover, the connective weights of the improved ENN are trained online by the backpropagation learning algorithm. Compared to earlier methods, better results are obtained when the ENN controller is used together with the grey system modeling approach. Performance of the proposed approach is verified by the experimental results.",

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AB - This brief presents the design of an optimal wind energy control system for maximum power point tracking. With the help of a grey predictor for the preprocessor, a high-performance online training Elman neural network (ENN) is designed to derive the turbine speed needed to extract maximum power from wind. Moreover, the connective weights of the improved ENN are trained online by the backpropagation learning algorithm. Compared to earlier methods, better results are obtained when the ENN controller is used together with the grey system modeling approach. Performance of the proposed approach is verified by the experimental results.

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Hybrid control of a wind induction generator based on grey-elman neural network

Abstract

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