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|a com.springer.onix.9789811540042
|b Springer Nature
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|a QA76.9.N37
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|a HCDD
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|a Nature inspired optimization for electrical power system /
|c Manjaree Pandit, Hari Mohan Dubey, Jagdish Chand Bansal, editors.
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| 264 |
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|a Singapore :
|b Springer,
|c 2020.
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| 300 |
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|a 1 online resource
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| 336 |
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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|a online resource
|b cr
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| 490 |
1 |
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|a Algorithms for intelligent systems
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0 |
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|a Intro -- Preface -- Synopsis -- Contents -- About the Editors -- 1 Teaching-Learning-Based Optimization for Static and Dynamic Load Dispatch -- 1 Introduction -- 2 Problem Statement -- 3 Teaching-Learning-Based Optimization -- 4 Description of Problems and Simulation Results -- 5 Conclusion -- References -- 2 Application of Elitist Teacher-Learner-Based Optimization Algorithm for Congestion Management -- 1 Introduction -- 2 Problem Formulation -- 2.1 Equality Constraints -- 2.2 Inequality Constraints -- 2.3 Fitness Function -- 3 Frame of Elitist Teacher-Learner-Based Optimization (ETLBO)
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|a 3.1 Teacher Phase -- 3.2 Learner Phase -- 3.3 Elitism -- 4 Elitist TLBO for Congestion Management -- 4.1 About Test Systems -- 4.2 Line Outage Contingency: Case I -- 4.3 Sudden Increment in Demand with Single Line Outage: Case II -- 4.4 Abrupt Line Power Limits Variation: Case III and IV -- 4.5 Generation Rescheduling for CM -- 4.6 ETLBO for Solution of CM Problem: Mathematical Procedure -- 5 Numerical Results and Analysis -- 5.1 Convergence Analysis of ETLBO -- 6 Conclusions -- References -- 3 PSO-Based Optimization of Levelized Cost of Energy for Hybrid Renewable Energy System -- 1 Introduction
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|a 2 Problem Formulation -- 3 Optimization of LCOE -- 3.1 Power Generation Equality/Inequality Constraint -- 4 Results and Discussion -- 4.1 Test Case Description -- 4.2 Optimization of LCOE -- 4.3 Effect of Capacity Factor on Optimal Value of LCOE -- 4.4 Convergence Characteristics of the Solver -- 4.5 Validation of Results Using Particle Swarm Optimization -- 5 Conclusion -- References -- 4 PSO-Based PID Controller Designing for LFC of Single Area Electrical Power Network -- 1 Introduction -- 2 Problem Formulation -- 2.1 System Description -- 2.2 A Brief Introduction of PID Controller
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|a 2.3 Objective Function Formulation -- 3 Employed Optimization Techniques -- 3.1 GA -- 3.2 PSO -- 4 Results and Discussions -- 4.1 Case 1: Objective Function-IAE -- 4.2 Case 2: Objective Function-ISE -- 4.3 Case 3: Objective Function-ITAE -- 4.4 Case 4: Objective Function-ITSE -- 5 Conclusion -- References -- 5 Combined Economic Emission Dispatch of Hybrid Thermal PV System Using Artificial Bee Colony Optimization -- 1 Introduction -- 2 Problem Formulation -- 2.1 Objective Function -- 2.2 Equality Constraint -- 2.3 Inequality Constraint -- 3 Artificial Bee Colony Optimization
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|a 4 Results and Discussion -- 4.1 Description of Test Cases -- 4.2 Simulation Results -- 5 Conclusion -- References -- 6 Dynamic Scheduling of Energy Resources in Microgrid Using Grey Wolf Optimization -- 1 Introduction -- 2 Problem Formulation -- 2.1 Inequality Constraints -- 2.2 Equality Constraints -- 3 Grey Wolf Optimization -- 4 Results and Discussion -- 4.1 Description of Test Cases -- 4.2 Simulation Results -- 5 Conclusion -- References -- 7 Mixed-Integer Differential Evolution Algorithm for Optimal Static/Dynamic Scheduling of a Microgrid with Mixed Generation -- 1 Introduction
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|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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| 650 |
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|a Nature-inspired algorithms.
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| 650 |
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|a Mathematical optimization.
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| 650 |
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|a Electrical engineering.
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| 650 |
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|a Mathematics.
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| 650 |
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7 |
|a electrical engineering.
|2 aat
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| 650 |
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|a mathematics.
|2 aat
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| 650 |
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|a applied mathematics.
|2 aat
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| 650 |
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|a Mathematical optimization
|2 fast
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| 650 |
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|a Nature-inspired algorithms
|2 fast
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| 700 |
1 |
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|a Pandit, Manjaree,
|e editor.
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| 700 |
1 |
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|a Dubey, Hari Mohan,
|e editor.
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| 700 |
1 |
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|a Bansal, Jagdish Chand,
|e editor.
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| 758 |
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|i has work:
|a Nature Inspired Optimization for Electrical Power System (Text)
|1 https://id.oclc.org/worldcat/entity/E39PD3dyk7qXdPPMk4M4DKfHyb
|4 https://id.oclc.org/worldcat/ontology/hasWork
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| 776 |
0 |
8 |
|i Print version:
|t Nature inspired optimization for electrical power system.
|d Singapore : Springer, 2020
|z 9811540039
|z 9789811540035
|w (OCoLC)1142510603
|
| 830 |
|
0 |
|a Algorithms for intelligent systems.
|
| 856 |
4 |
0 |
|u https://holycross.idm.oclc.org/login?auth=cas&url=https://link.springer.com/10.1007/978-981-15-4004-2
|y Click for online access
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| 903 |
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|a SPRING-ENGINE2020
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| 994 |
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|a 92
|b HCD
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