Uma Nangia

GA based multiobjective economic load dispatch by Maximization Of Minimum Relative Attainments

In this paper, two important objectives of power systems - cost of generation and system transmission losses have been considered. The multiobjective economic load dispatch (MELD) problem is formulated using constraint method. Genetic Algorithm has been used to generate the noninferior set for IEEE 5, 14 and 30 bus systems in 2D space. Target Point (TP) or the best compromise solution has been achieved by Maximization of Minimum Relative Attainments method. This method assumes that all the objectives are being minimized and seeks the solution which maximizes the minimum relative attainment by any objective of its ideal reference value relative to its worst feasible value. The TP obtained by this method has been compared with that obtained by Ideal Distance Minimization method and Surrogate Worth Tradeoff technique. It is observed that the proposed method gives encouraging results for IEEE 5 and 14 bus systems whereas Ideal Distance Minimization method is the best for IEEE 30 bus system.

An improved PSO based on Initial selection of Particles (ISBPSO) for Economic load Dispatch

In this paper, an attempt has been made to develop an improved PSO based on Initial selection of Particles (ISBPSO) by selecting a better population of particles from the initially generated particles and this population has been generated based on function value. ISBPSO has been implemented to perform Economic load Dispatch on IEEE 5,14, and 30 bus systems and its performance has been compared to Basic Particle Swarm Optimization( BPSO) resulted in faster convergence and more accurate results. It was observed that ISBPSO resultant in faster convergence and better accuracy.

Load flow studies based on a new Particle Swarm Optimization

In this paper, an attempt has been made to develop a new variant of Particle Swarm Optimization (PSO) algorithm and perform load flow on IEEE 5 and 14 bus systems using this new algorithm. In this new PSO, a better population of particles is selected by applying reduction factor (r) after suitable number of iterations called sorting frequency (fs). This better population is based on the objective function value and is chosen after suitable sorting frequency. The results of load flow using the new PSO are found to be as accurate as that obtained by Newton Raphson method and are also found to converge faster than the conventional PSO.