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New Hybrid Particle Swarm Optimization Algorithm for Solving Optimal Reactive Power Dispatch Problem

International Journal of Electrical and Electronic Science
Vol.1 , No. 1, Publication Date: Jul. 7, 2014, Page: 6-11

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Authors

[1]	K. Lenin , Jawaharlal Nehru Technological University, Hyderabad, India.
[2]	B. Ravindranath Reddy , Jawaharlal Nehru Technological University, Hyderabad, India.
[3]	M. Surya Kalavathi , Jawaharlal Nehru Technological University, Hyderabad, India.

Abstract

This paper presents a hybrid particle swarm algorithm for solving the multi-objective reactive power dispatch problem. Modal analysis of the system is used for static voltage stability assessment. Loss minimization and maximization of voltage stability margin are taken as the objectives. Generator terminal voltages, reactive power generation of the capacitor banks and tap changing transformer setting are taken as the optimization variables. Evolutionary algorithm and Swarm Intelligence algorithm (EA, SI), a part of Bio inspired optimization algorithm, have been widely used to solve numerous optimization problem in various science and engineering domains. The standard Particle Swarm Optimization (PSO) algorithm is a novel evolutionary algorithm in which each particle studies its own previous best solution and the group’s previous best to optimize problems. One problem exists in PSO is its tendency of trapping into local optima. This paper proposes a hybrid approach by combining a Euclidian distance (EU) based genetic algorithm (GA) and particle swarm optimization (PSO) method - New hybrid particle swarm optimization (NHPSO). The simulation results demonstrate good performance of the NHPSO in solving an optimal reactive power dispatch problem. In order to evaluate the proposed algorithm, it has been tested on IEEE 30 bus system and compared to other algorithms reported those before in literature. Results show that NHPSO is more efficient than others for solution of single-objective optimal reactive power dispatch (ORPD) problem.

Keywords

Particle Swarm Optimization, Genetic Algorithm, Swarm Intelligence, Optimal Reactive Power, Transmission Loss

Reference

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