Design and Comparison of Performance of DFIG Based Wind Turbine with PID Controller, Fuzzy Controller, Artificial Neural Network and Model Predictive Controller

The everyday benefits of environmentally friendly power sources urges to build their use to the bigger degree of whichwind energy is the most accessible asset. This paper presents the plan of multimode hang control methodology based variable speed wind power a...

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Autores principales:	K. Naresh, P. Reddy, P. Sujatha
Formato:	article
Lenguaje:	EN
Publicado:	European Alliance for Innovation (EAI) 2022
Materias:	multimode control strategy pi controller fuzzy controller artificial neural network model predictive controller Science Q Mathematics QA1-939 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/d49ffca7ee75444aac19a4bb2cafdd2c
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id	oai:doaj.org-article:d49ffca7ee75444aac19a4bb2cafdd2c
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spelling	oai:doaj.org-article:d49ffca7ee75444aac19a4bb2cafdd2c2021-11-30T11:07:32ZDesign and Comparison of Performance of DFIG Based Wind Turbine with PID Controller, Fuzzy Controller, Artificial Neural Network and Model Predictive Controller2032-944X10.4108/eai.29-6-2021.170251https://doaj.org/article/d49ffca7ee75444aac19a4bb2cafdd2c2022-01-01T00:00:00Zhttps://eudl.eu/pdf/10.4108/eai.29-6-2021.170251https://doaj.org/toc/2032-944XThe everyday benefits of environmentally friendly power sources urges to build their use to the bigger degree of whichwind energy is the most accessible asset. This paper presents the plan of multimode hang control methodology based variable speed wind power age framework. The multimode hang control procedure improves the framework to work regarding the network framework and furthermore in the independent method of activity. The multimode control methodology utilizes the DC connect voltage regulator to control the DC interface capacitor voltage for working the framework side converter and current regulator to control current and force of the rotor side converter. The control methodology is investigated with the customary regulator like PI regulator, astute regulators like Fuzzy regulator, fake neural organization (ANN) and model prescient regulator (MPC) which predicts the future factors. A correlation has been performed with the previously mentioned various sorts of regulators based breeze power age framework regarding various boundaries. This paper likewise includes examination of various experiments with the previously mentioned regulators. The examination of various experiments with various regulators has been performed utilizing MATLAB 2013a and every one of the outcomes are checked.K. NareshP. ReddyP. SujathaEuropean Alliance for Innovation (EAI)articlemultimode control strategypi controllerfuzzy controller artificial neural networkmodel predictive controllerScienceQMathematicsQA1-939Electronic computers. Computer scienceQA75.5-76.95ENEAI Endorsed Transactions on Energy Web, Vol 9, Iss 37 (2022)
institution	DOAJ
collection	DOAJ
language	EN
topic	multimode control strategy pi controller fuzzy controller artificial neural network model predictive controller Science Q Mathematics QA1-939 Electronic computers. Computer science QA75.5-76.95
spellingShingle	multimode control strategy pi controller fuzzy controller artificial neural network model predictive controller Science Q Mathematics QA1-939 Electronic computers. Computer science QA75.5-76.95 K. Naresh P. Reddy P. Sujatha Design and Comparison of Performance of DFIG Based Wind Turbine with PID Controller, Fuzzy Controller, Artificial Neural Network and Model Predictive Controller
description	The everyday benefits of environmentally friendly power sources urges to build their use to the bigger degree of whichwind energy is the most accessible asset. This paper presents the plan of multimode hang control methodology based variable speed wind power age framework. The multimode hang control procedure improves the framework to work regarding the network framework and furthermore in the independent method of activity. The multimode control methodology utilizes the DC connect voltage regulator to control the DC interface capacitor voltage for working the framework side converter and current regulator to control current and force of the rotor side converter. The control methodology is investigated with the customary regulator like PI regulator, astute regulators like Fuzzy regulator, fake neural organization (ANN) and model prescient regulator (MPC) which predicts the future factors. A correlation has been performed with the previously mentioned various sorts of regulators based breeze power age framework regarding various boundaries. This paper likewise includes examination of various experiments with the previously mentioned regulators. The examination of various experiments with various regulators has been performed utilizing MATLAB 2013a and every one of the outcomes are checked.
format	article
author	K. Naresh P. Reddy P. Sujatha
author_facet	K. Naresh P. Reddy P. Sujatha
author_sort	K. Naresh
title	Design and Comparison of Performance of DFIG Based Wind Turbine with PID Controller, Fuzzy Controller, Artificial Neural Network and Model Predictive Controller
title_short	Design and Comparison of Performance of DFIG Based Wind Turbine with PID Controller, Fuzzy Controller, Artificial Neural Network and Model Predictive Controller
title_full	Design and Comparison of Performance of DFIG Based Wind Turbine with PID Controller, Fuzzy Controller, Artificial Neural Network and Model Predictive Controller
title_fullStr	Design and Comparison of Performance of DFIG Based Wind Turbine with PID Controller, Fuzzy Controller, Artificial Neural Network and Model Predictive Controller
title_full_unstemmed	Design and Comparison of Performance of DFIG Based Wind Turbine with PID Controller, Fuzzy Controller, Artificial Neural Network and Model Predictive Controller
title_sort	design and comparison of performance of dfig based wind turbine with pid controller, fuzzy controller, artificial neural network and model predictive controller
publisher	European Alliance for Innovation (EAI)
publishDate	2022
url	https://doaj.org/article/d49ffca7ee75444aac19a4bb2cafdd2c
work_keys_str_mv	AT knaresh designandcomparisonofperformanceofdfigbasedwindturbinewithpidcontrollerfuzzycontrollerartificialneuralnetworkandmodelpredictivecontroller AT preddy designandcomparisonofperformanceofdfigbasedwindturbinewithpidcontrollerfuzzycontrollerartificialneuralnetworkandmodelpredictivecontroller AT psujatha designandcomparisonofperformanceofdfigbasedwindturbinewithpidcontrollerfuzzycontrollerartificialneuralnetworkandmodelpredictivecontroller
_version_	1718406712792186880

Design and Comparison of Performance of DFIG Based Wind Turbine with PID Controller, Fuzzy Controller, Artificial Neural Network and Model Predictive Controller

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