Abstract
In this paper Better-Quality Particle Swarm Optimization (BPSO) algorithm is proposed to solve the optimal reactive power Problem. Proposed algorithm is obtained by combining particle swarm optimization (PSO), Cauchy mutation and an evolutionary selection strategy. The idea is to introduce the Cauchy mutation into PSO in the hope of preventing PSO from trapping into a local optimum through long jumps made by the Cauchy mutation. In order to evaluate the efficiency of the proposed Better-Quality Particle Swarm Optimization (BPSO) algorithm, it has been tested on IEEE 57 bus system. Simulation Results show’s that BPSO is more efficient than other reported algorithms in reducing the real power loss.
Highlights
Different numerical methods have been implemented to solve this optimal reactive power dispatch problem
Preventing particle swarm optimization (PSO) from trapping into a local optimum through long jumps made by the Cauchy mutation
In order to evaluate the efficiency of the proposed Better-Quality Particle Swarm Optimization (BPSO) algorithm, it has been tested on IEEE 57 bus system
Summary
Different numerical methods have been implemented to solve this optimal reactive power dispatch problem. These consist of the gradient method [1, 2], Newton method [3] and linear programming [4,5,6,7].The gradient and Newton methods suffer from the difficulty in handling inequality constraints. In this paper Better-Quality Particle Swarm Optimization (BPSO) algorithm is proposed to solve the optimal reactive power Problem. Proposed algorithm is obtained by combining particle swarm optimization (PSO), Cauchy mutation and an evolutionary selection strategy. In order to evaluate the efficiency of the proposed Better-Quality Particle Swarm Optimization (BPSO) algorithm, it has been tested on IEEE 57 bus system. Simulation Results show’s that BPSO is more efficient than other reported algorithms in reducing the real power loss
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