Krill herd algorithm for optimal location of distributed generator in radial distribution system
Autor: | Provas Kumar Roy, Sneha Sultana |
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Rok vydání: | 2016 |
Předmět: |
Mathematical optimization
Computer science 020209 energy Evolutionary algorithm Particle swarm optimization 02 engineering and technology AC power Electric power system Bus voltage Search algorithm Simulated annealing Distributed generator Genetic algorithm 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Software |
Zdroj: | Applied Soft Computing. 40:391-404 |
ISSN: | 1568-4946 |
DOI: | 10.1016/j.asoc.2015.11.036 |
Popis: | This paper presents KH algorithm to solve optimal placement of distributed generator (ODG) problem.ODG problem is studied with an objective of reducing power loss and energy cost.Three illustrative examples of radial distribution network are presented.Proposed method shows better results when compared with other techniques in terms of the quality of solution. Distributed generator (DG) is recognized as a viable solution for controlling line losses, bus voltage, voltage stability, etc. and represents a new era for distribution systems. This paper focuses on developing an approach for placement of DG in order to minimize the active power loss and energy loss of distribution lines while maintaining bus voltage and voltage stability index within specified limits of a given power system. The optimization is carried out on the basis of optimal location and optimal size of DG. This paper developed a new, efficient and novel krill herd algorithm (KHA) method for solving the optimal DG allocation problem of distribution networks. To test the feasibility and effectiveness, the proposed KH algorithm is tested on standard 33-bus, 69-bus and 118-bus radial distribution networks. The simulation results indicate that installing DG in the optimal location can significantly reduce the power loss of distributed power system. Moreover, the numerical results, compared with other stochastic search algorithms like genetic algorithm (GA), particle swarm optimization (PSO), combined GA and PSO (GA/PSO) and loss sensitivity factor simulated annealing (LSFSA), show that KHA could find better quality solutions. |
Databáze: | OpenAIRE |
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