Sneha Sultana

Krill herd algorithm for optimal location of distributed generator in radial distribution system

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.

Oppositional cuckoo optimization algorithm to solve DG allocation problem of radial distribution system

Optimal sizing and placement of distributed generator (DG) units in radial distribution system are becoming very attractive to researchers these days. In this paper, cuckoo optimization algorithm (COA) is applied in order to find the optimal location of DG to optimize the power loss in radial distribution system. Furthermore, an oppositional based learning (OBL) is introduced with COA for improving the convergence speed of COA. The proposed oppositional COA (OCOA) methodology is successfully applied to the 33 bus and 69 bus radial distribution systems in order to show the effectiveness and efficiency of this optimization. The simulation result of proposed methods are compared with the other population based optimization like genetic algorithm (GA), particle swarm optimization (PSO), GA/PSO, bacteria foraging optimization algorithm (BFOA) in order to show the usefulness of this proposed approach.

Automatic Generation Control of Interconnected Power System using Cuckoo Optimization Algorithm

Automatic generation control (AGC) is added in power system to ensure constancy in frequency and tie-line power of an interconnected multi-area power system. In this article, proportional integral (PI) controlled based AGC of two-area hydrothermal system is solved by cuckoo optimization algorithm (COA). It is one of the most powerful stochastic real parameter optimization in current use. The design objective is to improve the dynamic performance of the interconnected system following a disturbance. System performance is examined considering 1% step load perturbation in thermal area with generation rate constraints. The results are compared with BBO, GA and DE to show the effectiveness of the proposed method. Computed results shows that the proposed method effectively improve the performance of the objective function with corresponding minimization of the overshoot, undershoot and settling time to reach steady state.

Optimal Allocation of Distributed Generator Using Chemical Reaction Optimization

In this paper, an effort is taken to attain optimal DG placement using chemical reaction optimization (CRO) algorithm to minimize the power loss in radial distribution system. The suggested methodology is fruitfully applied on 33-bus and 69-bus radial distribution systems. Simulation results have been compared with other population-based optimization technique like genetic algorithm (GA), particle swarm optimization (PSO), hybrid GA and PSO (GA/PSO), oppositional cuckoo optimization algorithm (OCOA), and bacteria foraging optimization algorithm (BFOA). The simulation results imply that the suggested methodology offers reasonable efficiency and it outperforms the other artificial optimization techniques that are available in the recent literature.