Nature Inspired Optimization for Electrical Power System

Language: English

Published by Springer, 2021

9811540063 / 9789811540066

Series: Book 17 of 95 - Algorithms for Intelligent Systems

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Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH

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Druck auf Anfrage Neuware - Printed after ordering - This book presents a wide range of optimization methods and their applications to various electrical power system problems such as economical load dispatch, demand supply management in microgrids, levelized energy pricing, load frequency control and congestion management, and reactive power management in radial distribution systems. Problems related to electrical power systems are often highly complex due to the massive dimensions, nonlinearity, non-convexity and discontinuity associated with objective functions. These systems also have a large number of equality and inequality constraints, which give rise to optimization problems that are difficult to solve using classical numerical methods. In this regard, nature inspired optimization algorithms offer an effective alternative, due to their ease of use, population-based parallel search mechanism, non-dependence on the nature of the problem, and ability to accommodate non-differentiable, non-convex problems. The analytical model ofnature inspired techniques mimics the natural behaviors and intelligence of life forms. These techniques are mainly based on evolution, swarm intelligence, ecology, human intelligence and physical science.

Seller Inventory # 9789811540066

Title
Nature Inspired Optimization for Electrical Power System
Author
Jagdish Chand Bansal
Publisher
Springer
Publication year
2021
Condition
Neu
Binding
Taschenbuch
Language
English
ISBN 10
9811540063
ISBN 13
9789811540066
Item weight
230 grams
Dimensions
235x155x9 mm
Series
Book 17 of 95: Algorithms for Intelligent Systems

AHA-BUCH GmbH

Einbeck, Germany

5-star seller

AbeBooks seller since August 14, 2006

Shipping rates from Germany to U.S.A.

Item5 to 7 business days7 to 10 business days
First itemUS$ 35.03US$ 35.03
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