000			03946nam a22004695i 4500
999			_c120327 _d120327
001			120327
003			ES-MaUEC
005			20230102114036.0
006			a||||fo|||| 00| 0
007			cr nn nnnaamaa
008			200407s2020 si a o |||| 0|eng d
020			_a9789811540042
024	7		_a10.1007/978-981-15-4004-2 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.9.N37 _b2020 EB
245	0	0	_aNature Inspired Optimization for Electrical Power System / _cedited by Manjaree Pandit, Hari Mohan Dubey, Jagdish Chand Bansal
250			_aFirst edition
264		1	_aSingapore _bSpringer Singapore : _bImprint: Springer _c2020
300			_a1 recurso en línea (XIV, 129 páginas) _b49 ilustraciones, 35 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aArchivo de texto _bPDF
490	0		_aAlgorithms for Intelligent Systems _x2524-7565
490	0		_aEngineering (Springer-11647)
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	3		_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. .
988			_aSpringer_Engineering_23062020
650		7	_aProceso en lenguaje natural (Informática) _2embne _9158738
650		7	_aProceso de imágenes _2embne _9669495
650		7	_aOptimización matemática _2embne _9145705
700	1		_aPandit, Manjaree _eeditor _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aDubey, Hari Mohan _eeditor _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aBansal, Jagdish Chand _eeditor _4edt _4http://id.loc.gov/vocabulary/relators/edt _1http://viaf.org/viaf/27154921364063592940
710	2		_aSpringerLink (Online service) _0http://id.loc.gov/authorities/names/no2005046756 _1http://viaf.org/viaf/148105729
776	0	8	_iPrinted edition: _z9789811540035
776	0	8	_iPrinted edition: _z9789811540059
776	0	8	_iPrinted edition: _z9789811540066
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-15-4004-2 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b07/2020 _dz _eo _zSI