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_a9789811540042 _9978-981-15-4004-2 |
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_a10.1007/978-981-15-4004-2 _2doi |
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_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. |
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300 |
_aXIV, 129 p. 49 illus., 35 illus. in color. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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_aonline resource _bcr _2rdacarrier |
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_atext file _bPDF _2rda |
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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 |
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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 |
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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 |
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