Development of New Hybrid Artificial Neural Network Based Control of Doubly Fed Induction Generator

2021 ◽  
pp. 64-77
Author(s):  
G. Venu Madhav ◽  
Y. P. Obulesu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Induction Generator ◽  
Doubly Fed Induction Generator ◽  
Artificial Neural ◽  
Doubly Fed ◽  
Hybrid Artificial Neural Network

scholarly journals A New Hybrid Artificial Neural Network Based Control of Doubly Fed Induction Generator

2015 ◽  
Vol 5 (3) ◽  
pp. 379 ◽  
Author(s):  
Venu Madhav Gopala ◽  
Obulesu Y.P.
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Dynamic Performance ◽  
Control Technique ◽  
Induction Generator ◽  
Pi Control ◽  
Doubly Fed Induction Generator ◽  
Control Techniques ◽  
Doubly Fed ◽  
Hybrid Artificial Neural Network

In this paper, Hybrid Artificial Neural Network (ANN) with Proportional Integral (PI) control technique has been developed for Doubly Fed Induction Generator (DFIG) based wind energy generation system and the performance of the system is compared with NN and PI control techniques. With the increasing use of wind power generation, it is required to instigate the dynamic performance analysis of Doubly Fed Induction Generator under various operating conditions. In this paper, three control techniques have been proposed, the first one is using PI controller, the second one is ANN control, and the third one is based on combination of ANN and PI. The performance of the proposed control techniques is demonstrated through the results, determined by using MATLab/Simulink. From the results it is observed that the dynamic performance of the DFIG is improved with the Hybrid control technique.

Development of a Hybrid Artificial Neural Network and Genetic Algorithm Model for Regime Identification of Slurry Transport in Pipelines

2009 ◽  
Vol 4 (1) ◽  
Author(s):  
Sandip K Lahiri ◽  
Kartik Chandra Ghanta
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Operating Conditions ◽  
Misclassification Error ◽  
Wide Range ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Pipeline Design ◽  
Hybrid Artificial Neural Network

Four distinct regimes were found existent (namely sliding bed, saltation, heterogeneous suspension and homogeneous suspension) in slurry flow in pipeline depending upon the average velocity of flow. In the literature, few numbers of correlations has been proposed for identification of these regimes in slurry pipelines. Regime identification is important for slurry pipeline design as they are the prerequisite to apply different pressure drop correlation in different regime. However, available correlations fail to predict the regime over a wide range of conditions. Based on a databank of around 800 measurements collected from the open literature, a method has been proposed to identify the regime using artificial neural network (ANN) modeling. The method incorporates hybrid artificial neural network and genetic algorithm technique (ANN-GA) for efficient tuning of ANN meta parameters. Statistical analysis showed that the proposed method has an average misclassification error of 0.03%. A comparison with selected correlations in the literature showed that the developed ANN-GA method noticeably improved prediction of regime over a wide range of operating conditions, physical properties, and pipe diameters.

scholarly journals Development of option c measurement and verification model using hybrid artificial neural network-cross validation technique to quantify saving

2020 ◽  
Vol 9 (1) ◽  
pp. 25
Author(s):  
Wan Nazirah Wan Md Adnan ◽  
Nofri Yenita Dahlan ◽  
Ismail Musirin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Artificial Bee Colony Optimization ◽  
Bee Colony ◽  
Measurement And Verification ◽  
Bee Colony Optimization ◽  
Validation Technique ◽  
Artificial Neural ◽  
Verification Model ◽  
Hybrid Artificial Neural Network

<span lang="EN-US">This paper aims to develop a hybrid artificial neural network for Option C Measurement and Verification model to predict monthly building energy consumption. In this work, baseline energy model development using artificial neural networks embedded with artificial bee colony optimization and cross validation technique for a small dataset were considered. Artificial bee colony optimization with coefficient of correlation fitness function was used in optimizing the neural network training process and selecting the optimal values of initial weights and biases. Working days, class days and cooling degree days were used as input meanwhile monthly electricity consumption as an output of artificial neural network. The results indicated that this hybrid artificial neural network model provided better prediction results compared to the other model. The best model with the highest value of coefficient of correlation was selected as the baseline model hence is used to determine the saving. </span>

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