東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Nature inspired optimization for ele...

Pandit, Manjaree.

Linked to FindBook

Google Book

Amazon

博客來

Nature inspired optimization for electrical power system

Record Type:	Electronic resources : Monograph/item
Title/Author:	Nature inspired optimization for electrical power system/ edited by Manjaree Pandit, Hari Mohan Dubey, Jagdish Chand Bansal.
other author:	Pandit, Manjaree.
Published:	Singapore :Springer Singapore : : 2020.,
Description:	xiv, 129 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer eBooks
Subject:	Nature-inspired algorithms. -
Online resource:	https://doi.org/10.1007/978-981-15-4004-2
ISBN:	9789811540042

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

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

3451303
Nature-inspired algorithms.

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

Dewey Class. No.: 006.38

Nature inspired optimization for electrical power system
LDR:02897nmm a2200337 a 4500 001 2217794
003 DE-He213
005 20200814140112.0
006 m d
007 cr nn 008maaau
008 201120s2020 si s 0 eng d
020 $a 9789811540042 $q (electronic bk.)
020 $a 9789811540035 $q (paper)
024 7 $a 10.1007/978-981-15-4004-2 $2 doi
035 $a 978-981-15-4004-2
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.N37 $b N388 2020
072 7 $a THR $2 bicssc
072 7 $a TEC007000 $2 bisacsh
072 7 $a THR $2 thema
082 0 4 $a 006.38 $2 23
090 $a QA76.9.N37 $b N285 2020
245 0 0 $a Nature inspired optimization for electrical power system $h [electronic resource] / $c edited by Manjaree Pandit, Hari Mohan Dubey, Jagdish Chand Bansal.
260 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2020.
300 $a xiv, 129 p. : $b ill., digital ; $c 24 cm.
490 1 $a Algorithms for intelligent systems, $x 2524-7565
505 0 $a 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.
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.
650 0 $a Nature-inspired algorithms. $3 3451303
650 0 $a Mathematical optimization. $3 517763
650 1 4 $a Electrical Engineering. $3 1001838
650 2 4 $a Energy Systems. $3 1530445
650 2 4 $a Mathematics, general. $3 895821
650 2 4 $a Optimization. $3 891104
700 1 $a Pandit, Manjaree. $3 3450155
700 1 $a Dubey, Hari Mohan. $3 3451302
700 1 $a Bansal, Jagdish Chand. $3 3378378
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a Algorithms for intelligent systems. $3 3443393
856 4 0 $u https://doi.org/10.1007/978-981-15-4004-2
950 $a Engineering (Springer-11647)