RETRACTED ARTICLE: Optimized deep learning neural network model for doubly fed induction generator in wind energy conversion systems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 |
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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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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. 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N.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>RETRACTED ARTICLE: Optimized deep learning neural network model for doubly fed induction generator in wind energy conversion systems</atitle><jtitle>Soft computing (Berlin, Germany)</jtitle><stitle>Soft Comput</stitle><date>2019-09-01</date><risdate>2019</risdate><volume>23</volume><issue>18</issue><spage>8453</spage><epage>8470</epage><pages>8453-8470</pages><issn>1432-7643</issn><eissn>1433-7479</eissn><abstract>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.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s00500-019-03947-y</doi><tpages>18</tpages></addata></record> |
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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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