Neural control of renewable electrical power systems / Edgar N. Sánchez, Larbi Djilali.
2020
TK1007
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Details
Title
Neural control of renewable electrical power systems / Edgar N. Sánchez, Larbi Djilali.
Author
Sanchez, Edgar N.
ISBN
9783030474430 (electronic book)
3030474437 (electronic book)
9783030474423
3030474429
3030474437 (electronic book)
9783030474423
3030474429
Publication Details
Cham : Springer, 2020.
Language
English
Description
1 online resource (221 pages).
Item Number
10.1007/978-3-030-47
Call Number
TK1007
Dewey Decimal Classification
621.31
Summary
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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Source of Description
Description based on print version record.
Added Author
Djilali, Larbi.
Series
Studies in systems, decision and control ; v. 278.
Available in Other Form
Neural Control of Renewable Electrical Power Systems
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