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001 978-981-15-4004-2
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007 cr nn 008mamaa
008 200407s2020 si | s |||| 0|eng d
020 _a9789811540042
_9978-981-15-4004-2
024 7 _a10.1007/978-981-15-4004-2
_2doi
050 4 _aTK1-9971
072 7 _aTHR
_2bicssc
072 7 _aTEC007000
_2bisacsh
072 7 _aTHR
_2thema
082 0 4 _a621.3
_223
245 1 0 _aNature Inspired Optimization for Electrical Power System
_h[electronic resource] /
_cedited by Manjaree Pandit, Hari Mohan Dubey, Jagdish Chand Bansal.
250 _a1st ed. 2020.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2020.
300 _aXIV, 129 p. 49 illus., 35 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aAlgorithms for Intelligent Systems,
_x2524-7573
505 0 _aTeaching 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 _aThis 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 _aElectrical engineering.
_934140
650 0 _aElectric power production.
_927574
650 0 _aMathematics.
_911584
650 0 _aMathematical optimization.
_94112
650 1 4 _aElectrical and Electronic Engineering.
_934141
650 2 4 _aElectrical Power Engineering.
_931821
650 2 4 _aMechanical Power Engineering.
_932122
650 2 4 _aMathematics.
_911584
650 2 4 _aOptimization.
_934142
700 1 _aPandit, Manjaree.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_934143
700 1 _aDubey, Hari Mohan.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_934144
700 1 _aBansal, Jagdish Chand.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_934145
710 2 _aSpringerLink (Online service)
_934146
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789811540035
776 0 8 _iPrinted edition:
_z9789811540059
776 0 8 _iPrinted edition:
_z9789811540066
830 0 _aAlgorithms for Intelligent Systems,
_x2524-7573
_934147
856 4 0 _uhttps://doi.org/10.1007/978-981-15-4004-2
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
999 _c75560
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