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Nature inspired optimization for ele...

Bansal, Jagdish Chand.

Nature inspired optimization for electrical power system

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Nature inspired optimization for electrical power systemedited by Manjaree Pandit, Hari Mohan Dubey, Jagdish Chand Bansal.
其他作者:	Pandit, Manjaree.
出版者:	Singapore :Springer Singapore :2020.
面頁冊數:	xiv, 129 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Nature-inspired algorithms.
電子資源:	https://doi.org/10.1007/978-981-15-4004-2
ISBN:	9789811540042$q(electronic bk.)

Nature inspired optimization for electrical power system
Nature inspired optimization for electrical power system[electronic resource] /edited by Manjaree Pandit, Hari Mohan Dubey, Jagdish Chand Bansal. - Singapore :Springer Singapore :2020. - xiv, 129 p. :ill., digital ;24 cm. - Algorithms for intelligent systems,2524-7565. - Algorithms for intelligent systems..

Teaching Learning Based Optimization for Static and Dynamic Load Dispatch -- Application of Elitist Teacher Learner Based Optimization Algorithm for Congestion Management -- PSO Based Optimization of Levelized Cost of Energy for Hybrid Renewable Energy System -- PSO Based PID Controller Designing for LFC of Single Area Electrical Power Network -- Combined Economic Emission Dispatch of Hybrid Thermal-PV System Using Artificial Bee Colony Optimization -- Dynamic Scheduling of Energy Resources in Microgrid Using Grey Wolf Optimization -- Short-Term Hydrothermal Scheduling Using Bio- Inspired Computing: A Review.

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 of nature 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.

ISBN: 9789811540042$q(electronic bk.)

Standard No.: 10.1007/978-981-15-4004-2doiSubjects--Topical Terms:

858794
Nature-inspired algorithms.

LC Class. No.: QA76.9.N37 / N388 2020

Dewey Class. No.: 006.38

Nature inspired optimization for electrical power system
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520 $a 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 of nature 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.
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