Neural Control of Renewable Electrical Power Systems (Paperback, 1st ed. 2020)

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.

General

Imprint:	Springer Nature Switzerland AG
Country of origin:	Switzerland
Series:	Studies in Systems, Decision and Control, 278
Release date:	May 2021
First published:	2020
Authors:	Edgar N. Sanchez • Larbi Djilali
Dimensions:	235 x 155mm (L x W)
Format:	Paperback
Pages:	206
Edition:	1st ed. 2020
ISBN-13:	978-3-03-047445-4
Categories:	Books > Professional & Technical > Energy technology & engineering > Alternative & renewable energy sources & technology Books > Computing & IT > Applications of computing > Artificial intelligence > General Books > Professional & Technical > Electronics & communications engineering > Electronics engineering > Automatic control engineering > General
LSN:	3-03-047445-3
Barcode:	9783030474454

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