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paperback. Condizione: Gut. 232 Seiten; 9783030474454.3 Gewicht in Gramm: 500.

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Condizione: New. pp. XXV, 206 218 illus., 208 illus. in color. 1st ed. 2020 edition NO-PA16APR2015-KAP.

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Condizione: gut. 2021. Neural Control of Renewable Electrical Power Systems (Studies in Systems, Decision and Control, Band 278) In deutscher Sprache. pages.

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Buch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - At present, the growing use of electric machines and drives in more critical applications has driven research on condition monitoring and fault tolerance. The condition monitoring of electrical machines has a very important impact in the field of electrical systems maintenance, mainly for its potential functions of failure prediction, fault identification, and dynamic reliability estimation. The fault diagnosis of electrical machines and drives has received a great deal of attention due to its benefits in maintenance cost reduction, unscheduled downtime prevention, and, in many cases, harm prevention and failure disruption. Fault-tolerant design provides a solution combining fault occurrence conditions, failure detection and location tools, and the reconfiguration of control features. On the other hand, recent advancements in smart technology using artificial intelligence and advanced machine learning capabilities provide new perspectives for meaningful fault diagnostics and fault-tolerant control. These outstanding advancements enhance the performance of condition monitoring and have significant potential for the fault detection of electrical machines and devices. This reprint collected research and technological achievements related to the following topics: robust control strategies; failure detection and diagnosis; fault-tolerant control; and artificial intelligence (AI) and machine learning techniques for control, fault diagnosis, and tolerant control of electrical machines and devices.…

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 232 pp. Englisch.…