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Neural control of renewable electric...

Djilali, Larbi.

Neural control of renewable electrical power systems

Record Type:	Electronic resources : Monograph/item
Title/Author:	Neural control of renewable electrical power systemsby Edgar N. Sanchez, Larbi Djilali.
Author:	Sanchez, Edgar N.
other author:	Djilali, Larbi.
Published:	Cham :Springer International Publishing :2020.
Description:	xxv, 206 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Electric power systemsControl.
Online resource:	https://doi.org/10.1007/978-3-030-47443-0
ISBN:	9783030474430$q(electronic bk.)

Neural control of renewable electrical power systems
Sanchez, Edgar N.

Neural control of renewable electrical power systems[electronic resource] /by Edgar N. Sanchez, Larbi Djilali. - Cham :Springer International Publishing :2020. - xxv, 206 p. :ill., digital ;24 cm. - Studies in systems, decision and control,v.2782198-4182 ;. - Studies in systems, decision and control ;v.3..

Introduction -- Mathematical Preliminaries -- Wind System Modeling -- Neural Control Synthesis -- Experimental Results -- Microgrid Control -- Conclusions and Future Work.

This book presents advanced control techniques that use neural networks to deal with grid disturbances in the context renewable energy sources, and to enhance low-voltage ride-through capacity, which is a vital in terms of ensuring that the integration of distributed energy resources into the electrical power network. It presents modern control algorithms based on neural identification for different renewable energy sources, such as wind power, which uses doubly-fed induction generators, solar power, and battery banks for storage. It then discusses the use of the proposed controllers to track doubly-fed induction generator dynamics references: DC voltage, grid power factor, and stator active and reactive power, and the use of simulations to validate their performance. Further, it addresses methods of testing low-voltage ride-through capacity enhancement in the presence of grid disturbances, as well as the experimental validation of the controllers under both normal and abnormal grid conditions. The book then describes how the proposed control schemes are extended to control a grid-connected microgrid, and the use of an IEEE 9-bus system to evaluate their performance and response in the presence of grid disturbances. Lastly, it examines the real-time simulation of the entire system under normal and abnormal conditions using an Opal-RT simulator.

ISBN: 9783030474430$q(electronic bk.)

Standard No.: 10.1007/978-3-030-47443-0doiSubjects--Topical Terms:

224373
Electric power systems
--Control.

LC Class. No.: TK1007 / .S263 2020

Dewey Class. No.: 621.31

Neural control of renewable electrical power systems
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245 1 0 $a Neural control of renewable electrical power systems $h [electronic resource] / $c by Edgar N. Sanchez, Larbi Djilali.
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490 1 $a Studies in systems, decision and control, $x 2198-4182 ; $v v.278
505 0 $a Introduction -- Mathematical Preliminaries -- Wind System Modeling -- Neural Control Synthesis -- Experimental Results -- Microgrid Control -- Conclusions and Future Work.
520 $a This book presents advanced control techniques that use neural networks to deal with grid disturbances in the context renewable energy sources, and to enhance low-voltage ride-through capacity, which is a vital in terms of ensuring that the integration of distributed energy resources into the electrical power network. It presents modern control algorithms based on neural identification for different renewable energy sources, such as wind power, which uses doubly-fed induction generators, solar power, and battery banks for storage. It then discusses the use of the proposed controllers to track doubly-fed induction generator dynamics references: DC voltage, grid power factor, and stator active and reactive power, and the use of simulations to validate their performance. Further, it addresses methods of testing low-voltage ride-through capacity enhancement in the presence of grid disturbances, as well as the experimental validation of the controllers under both normal and abnormal grid conditions. The book then describes how the proposed control schemes are extended to control a grid-connected microgrid, and the use of an IEEE 9-bus system to evaluate their performance and response in the presence of grid disturbances. Lastly, it examines the real-time simulation of the entire system under normal and abnormal conditions using an Opal-RT simulator.
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650 2 4 $a Computational Intelligence. $3 338479
700 1 $a Djilali, Larbi. $3 869212
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