Optimal power flow by a fuzzy based hybrid particle swarm optimization approach

Liang, Rung-Huei; Tsai, Sheng-Ren; Chen, Yie‐Tone; Tseng, Wan-Tsun

doi:10.1016/j.epsr.2011.02.011

Cited by 118 publications

(57 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[16]. The results reveal that the method has good performance and is very effective in reaching an optimal setting for real power generation levels, voltage magnitudes and LTC (Load Tap Changer) tap positions, when uncertainties in load demand and wind speed are considered.…”

Section: Particle Swarm Optimization Methodsmentioning

confidence: 92%

Particle Swarm Optimization method for estimation of Weibull parameters: A case study for the Brazilian northeast region

et al. 2016

View full text Add to dashboard Cite

a b s t r a c tIn this paper the application of the Particle Swarm Optimization (PSO) method to estimate the Weibull parameters for wind resources in the Brazilian Northeast Region (BRNER) is reported. For the present research, wind speed data from three 80 m towers installed at different sites in the region were collected. The measuring periods for each tower site are: Petrolina. Aiming to compare with the PSO performance, five numerical methods are applied to calculate the Weibull distribution parameters. Best performance for all analyzed sites is achieved by the PSO method, with a correlation higher than 99% and an error close to zero. PSO proves to be a valuable technique for characterization of the particular wind conditions found in the BRNER.

show abstract

Section: Particle Swarm Optimization Methodsmentioning

confidence: 92%

Particle Swarm Optimization method for estimation of Weibull parameters: A case study for the Brazilian northeast region

et al. 2016

View full text Add to dashboard Cite

show abstract

“…In view of the drawbacks of the classical methods, the research has focused on the application of evolutionary programming (EP) [8], genetic algorithm (GA) [9,10], particle swarm optimization (PSO) [11], differential evolution (DE) [12] and many other meta-heuristic algorithms to solve the OPF problem. From these literatures, it can be the observed that the heuristic search algorithms are well-suited to solve the OPF problem.…”

Section: Introductionmentioning

confidence: 99%

An enhanced cuckoo search algorithm based contingency constrained economic load dispatch for security enhancement

Sekhar

Mohanty

2016

International Journal of Electrical Power & Energy Systems

View full text Add to dashboard Cite

“…Genetic algorithm (GA) was used to solve OPF problem [2]. In [3], particle swarm optimization (PSO) has been successfully implemented for solution of OPF problem. Cai [4] has adopted the differential evolution algorithm (DE) to solve a OPF problem with a constrained objective function.…”

Section: Introductionmentioning

confidence: 99%

Learning-Based Evolutionary Optimization for Optimal Power Flow

Niu

Peng

Zhang

2015

Intelligent Computing Theories and Methodologies

View full text Add to dashboard Cite

Abstract. This paper proposes a learning-based evolutionary optimization (LBEO) for solving optimal power flow (OPF) problem. The LBEO is a simple and effective algorithm, which simplifies the structure of teaching-learning-based optimization (TLBO) and enhances the convergence speed. The performance of this method is implemented on IEEE 30-bus test system with the minimized fuel cost objective function, and the results show that LBEO is practicable for OPF problem compared with other methods in the literature.

show abstract

Optimal power flow by a fuzzy based hybrid particle swarm optimization approach

Cited by 118 publications

References 16 publications

Particle Swarm Optimization method for estimation of Weibull parameters: A case study for the Brazilian northeast region

Particle Swarm Optimization method for estimation of Weibull parameters: A case study for the Brazilian northeast region

An enhanced cuckoo search algorithm based contingency constrained economic load dispatch for security enhancement

Learning-Based Evolutionary Optimization for Optimal Power Flow

Contact Info

Product

Resources

About