Neural Control of Renewable Electrical Power Systems

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 abnormalgrid 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.

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

Zusammenfassung
"The book addresses graduate students and researchers in advanced control engineering, applied mathematics, mathematical systems theory and wind power technologies." (Vladimir Sobolev, zbMATH 1482.93003, 2022)

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