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,&nbsp;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.

Neural Control of Renewable Electrical Power Systems (Studies in Systems, Decision and Control, 278)

Computers & Technology

Sánchez, Edgar N.

Djilali, Larbi

Computer Science

Springer

Neural Control of Renewable Electrical Power Systems (Studies in Systems, Decision and Control)

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Books by Djilali, Larbi

See the best price to sell, buy, or rent books by Djilali, Larbi. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Neural Control of Renewable Electrical Power Systems (Studies in Systems, Decision and Control, 278)

Neural Control of Renewable Electrical Power Systems (Studies in Systems, Decision and Control)