Enhancement of Power System Security using Meta-heuristic Optimization Techniques
|International Journal of Electrical and Electronics Engineering|
|© 2017 by SSRG - IJEEE Journal|
|Volume 4 Issue 2|
|Year of Publication : 2017|
|Authors : Amarendra Alluri|
How to Cite?
Amarendra Alluri, "Enhancement of Power System Security using Meta-heuristic Optimization Techniques," SSRG International Journal of Electrical and Electronics Engineering, vol. 4, no. 2, pp. 7-11, 2017. Crossref, https://doi.org/10.14445/23488379/IJEEE-V4I2P102
The genetic algorithm (GA) and particle swarm optimization (PSO) are search heuristic methods that mimics the process of natural evolution. This heuristic is routinely used to generate useful solutions to optimization and search problems. Genetic algorithms belong to the larger class of evolutionary algorithms (EA), which generate solutions to optimization problems using techniques inspired by natural evolution. The project presents a genetic-algorithm (GA) and PSO based OPF algorithm for identifying the optimal values of generator output and enhance power system security. Equality and inequality constraints are considered and severity indices are calculated for over loaded lines. Contingences are ranked based on the severity indices. The proposed method is applied on the standard IEEE 30 bus system. Simulations are obtained and results are presented, with the objectives 1.To calculate the security Index for a given power system network by evaluating various contingencies. 2.Enhancing the Electrical Power System Security levels at the optimum power schedule.
The proposed method is applied on the standard IEEE 30 bus system.
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