RETRACTED ARTICLE: Optimized deep learning neural network model for doubly fed induction generator in wind energy conversion systems

Design of controller for a doubly fed induction generator driven by a variable speed wind turbine employing deep learning neural networks whose weights are tuned by grey artificial bee colony algorithm is developed and simulated in this work. This paper presents the mathematical modelling of the dou...

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Veröffentlicht in:Soft computing (Berlin, Germany) Germany), 2019-09, Vol.23 (18), p.8453-8470
Hauptverfasser: Rajasingam, N., Rasi, D., Deepa, S. N.
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Rasi, D.
Deepa, S. N.
description Design of controller for a doubly fed induction generator driven by a variable speed wind turbine employing deep learning neural networks whose weights are tuned by grey artificial bee colony algorithm is developed and simulated in this work. This paper presents the mathematical modelling of the doubly fed induction generator (DFIG) and the controller design is implemented using the third generation deep learning neural network (DLNN). In the proposed work, the variable speed wind turbine generator torque is regulated employing a proportional–integral–derivative (PID) controller. The gains of the PID controller are tuned using DLNN model. The proposed density-based grey artificial bee colony (D-GABC) algorithm provides the optimal dataset required for training DLNN model. As well, the weights of the developed neural network controller are also optimized by D-GABC algorithm to avoid premature convergence and to reduce the incurred computational time of the network model. The effectiveness of the proposed DLNN-based controller for DFIG in wind energy conversion is proved and observed to be better than that of the other methods proposed in the previous literature works in respect of the simulated results obtained.
doi_str_mv 10.1007/s00500-019-03947-y
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subjects Alternative energy sources
Artificial Intelligence
Bees
Computational Intelligence
Computer simulation
Computing time
Control
Control systems design
Controllers
Deep learning
Design
Energy conversion
Energy resources
Engineering
Focus
Induction generators
Machine learning
Mathematical Logic and Foundations
Mathematical models
Mechatronics
Neural networks
Optimization algorithms
Optimization techniques
Proportional integral derivative
Renewable resources
Robotics
Search algorithms
Swarm intelligence
Turbines
Turbogenerators
Wind power
Wind turbines
title RETRACTED ARTICLE: Optimized deep learning neural network model for doubly fed induction generator in wind energy conversion systems
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