Investigations on solving the load flow problem by genetic algorithms

Many engineering optimization tasks involve finding more than one optimum solution. The present study provides a comprehensive review of the existing work done in the field of multi-modal function optimization and provides a critical analysis of the existing methods. Existing niching methods are analyzed and an improved niching method is proposed. To achieve this purpose, we first give an introduction to niching and diversity preservation, followed by discussion of a number of algorithms. Thereafter, a comparison of clearing, clustering, deterministic crowding, probabilistic crowding, restricted tournament selection, sharing, species conserving genetic algorithms is made. A modified niching-based technique -modified clearing approach -is introduced and also compared with existing methods. For comparison, a versatile hump test function is also proposed and used together with two other functions. The ability of the algorithms in finding, locating, and maintaining multiple optima is judged using two performance measures: (i) number of peaks maintained, and (ii) computational time. Based on the results, we conclude that the restricted tournament selection and the proposed modified clearing approaches are better in terms of finding and maintaining the multiple optima.

show abstract

“…Yin [19], [20] proposed a niching scheme using a clustering methodology. A clustering algorithm is used to divide the population into niches.…”

Section: Clusteringmentioning

confidence: 99%

Comparison of multi-modal optimization algorithms based on evolutionary algorithms

Singh

Deb

2006

Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation

140

111

View full text Add to dashboard Cite

show abstract

“…This may be installed at midpoint of the transmission line. The reactive power output of an SVC can be expressed as follows: (6) Where, is the equivalent slope reactance in p.u equal to the slope of voltage control characteristic, and are reference voltage magnitude. The exact loss formula of a system having N number of buses is [12].…”

Section: Mathematical Model Of Facts Devicesmentioning

confidence: 99%

“…Recently, the evolutionary computation techniques have found many applications in power systems, especially in the economic operation area and optimal power flow. [6] PSO is one of the stochastic search techniques developed by Kennedy and Eberhart [7]. It was developed through simulation of a simplified social system, and has been found to be robust in solving continuous nonlinear optimization problems.…”

Section: Introductionmentioning

confidence: 99%

Optimal location of FACTS devices in a power system using modified particle swarm optimization

Parastar

Pirayesh

Nikoukar

2007

2007 42nd International Universities Power Engineering Conference

View full text Add to dashboard Cite

The introduction of flexible AC transmission system (FACTS) in a power system improves the stability, reduces the losses, reduces the cost of generation and also improves the loadability of the system. In the proposed work, a non-traditional optimization technique, modified particle swarm optimization (MPSO) is used to optimize the various process parameters involved of FACTS devices in a power system. The various parameters taken into consideration were the location of the device, their type, and their rated value of the devices. The simulation was performed on a modified IEEE 30-bus power system with two types of FACTS controllers (SVC, TCSC), modeled for steady state studies. The optimization results clearly indicate that introduction of FACTS devices in a right location increases the loadability of the system and algorithm can be effectively used for this kind of optimization.Index Terms-FACTS, modified particle swam optimization optimal power flow, SVC, TCSC.

show abstract

“…This permits the formation of stable subpopulations around the different optima in the multimodal space. In [14], an attempt to search for all load flow solutions of the Klos-Kemer 3-bus system was tried by using the genetic algorithm with sharing. It was shown that the algorithm is able to simultaneously find all 4 solutions.…”

Section: Genetic Algorithm With Sharingmentioning

confidence: 99%

Application of genetic algorithms to multiple load flow solution problem in electrical power systems

Yin

Proceedings of 32nd IEEE Conference on Decision and Control

View full text Add to dashboard Cite

A new approach based on genetic algorithms to find multiple load flow solutions is presented in this paper. Genetic algorithms(GAs) are search procedures based on the mechanics of natural genetics and natural selection. A genetic algorithm with sharing scheme is proposed and applied to simultaneously find multiple load flow solutions. Numerical experiments on two example systems show that the proposed algorithm can be used as an efficient guide for simultaneously and globally searching multiple solution regions.

show abstract

Investigations on solving the load flow problem by genetic algorithms

Cited by 59 publications

References 1 publication

Comparison of multi-modal optimization algorithms based on evolutionary algorithms

Comparison of multi-modal optimization algorithms based on evolutionary algorithms

Optimal location of FACTS devices in a power system using modified particle swarm optimization

Application of genetic algorithms to multiple load flow solution problem in electrical power systems

Contact Info

Product

Resources

About