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Studies in Systems, Decision and Control

Neural Control of Renewable Electrical Power Systems

Authors: Sánchez, Edgar N., Djilali, Larbi

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  • Presents recent research on neural control of renewable electrical power systems  
  • Describes robust control schemes based on neural network identification 
  • Intended for researchers and students with a control background wishing to expand their knowledge of wind power generation and distributed energy resources installed into a grid-connected microgrid
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eBook $89.00
price for USA in USD
  • ISBN 978-3-030-47443-0
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $159.99
price for USA in USD
  • ISBN 978-3-030-47442-3
  • Free shipping for individuals worldwide
  • Institutional customers should get in touch with their account manager
  • Covid-19 shipping restrictions
  • Usually ready to be dispatched within 3 to 5 business days, if in stock
Softcover $119.99
price for USA in USD
  • ISBN 978-3-030-47445-4
  • Free shipping for individuals worldwide
  • Institutional customers should get in touch with their account manager
  • Covid-19 shipping restrictions
  • Usually ready to be dispatched within 3 to 5 business days, if in stock
About this book

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.

Table of contents (7 chapters)

Table of contents (7 chapters)

Buy this book

eBook $89.00
price for USA in USD
  • ISBN 978-3-030-47443-0
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $159.99
price for USA in USD
  • ISBN 978-3-030-47442-3
  • Free shipping for individuals worldwide
  • Institutional customers should get in touch with their account manager
  • Covid-19 shipping restrictions
  • Usually ready to be dispatched within 3 to 5 business days, if in stock
Softcover $119.99
price for USA in USD
  • ISBN 978-3-030-47445-4
  • Free shipping for individuals worldwide
  • Institutional customers should get in touch with their account manager
  • Covid-19 shipping restrictions
  • Usually ready to be dispatched within 3 to 5 business days, if in stock
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Bibliographic Information

Bibliographic Information
Book Title
Neural Control of Renewable Electrical Power Systems
Authors
Series Title
Studies in Systems, Decision and Control
Series Volume
278
Copyright
2020
Publisher
Springer International Publishing
Copyright Holder
The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
eBook ISBN
978-3-030-47443-0
DOI
10.1007/978-3-030-47443-0
Hardcover ISBN
978-3-030-47442-3
Softcover ISBN
978-3-030-47445-4
Series ISSN
2198-4182
Edition Number
1
Number of Pages
XXV, 206
Number of Illustrations
10 b/w illustrations, 208 illustrations in colour
Topics