Available transfer capability enhancement with FACTS using Cat Swarm Optimization

Nireekshana, T.; Rao, G. Kesava; Raju, S. Sivanaga

doi:10.1016/j.asej.2015.11.011

Cited by 50 publications

(33 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ref. CSO applied on electrical payment system in order to minimize electricity cost for customers CSO outperformed PSO [46] CSO applied on economic load dispatch (ELD) of wind and thermal generator CSO outperformed PSO [47] BCSO applied on unit commitment (UC) CSO outperformed LR, ICGA, BF, MILP, ICA, and SFLA [48] Applied CSO algorithm on UPFC to increase the stability of the system IEEE 6-bus and 14-bus networks were used in the simulation experiments and desirable results were achieved [49] Applied ADCSO on reactive power dispatch problem to minimize active power loss IEEE 57-bus system was used in the simulation experiments, in which ADCSO outperformed 16 other optimization algorithms [50] Applied CSO algorithm to regulate the position and control parameters of SVC and TCSC to improve available transfer capability (ATC) IEEE 14-bus and IEEE 24-bus systems were used in the simulation experiments, in which the system provided better results after adopting CSO [51] Building a classification model based on BCSO and SVM to classify the transformers according to their reliability status.…”

Section: Purpose Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Cat Swarm Optimization Algorithm: A Survey and Performance Evaluation

Ahmed

Rashid

Saeed

2020

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

This paper presents an in-depth survey and performance evaluation of cat swarm optimization (CSO) algorithm. CSO is a robust and powerful metaheuristic swarm-based optimization approach that has received very positive feedback since its emergence. It has been tackling many optimization problems, and many variants of it have been introduced. However, the literature lacks a detailed survey or a performance evaluation in this regard. Therefore, this paper is an attempt to review all these works, including its developments and applications, and group them accordingly. In addition, CSO is tested on 23 classical benchmark functions and 10 modern benchmark functions (CEC 2019). The results are then compared against three novel and powerful optimization algorithms, namely, dragonfly algorithm (DA), butterfly optimization algorithm (BOA), and fitness dependent optimizer (FDO). These algorithms are then ranked according to Friedman test, and the results show that CSO ranks first on the whole. Finally, statistical approaches are employed to further confirm the outperformance of CSO algorithm.

show abstract

Section: Purpose Resultsmentioning

confidence: 99%

“…Improving available transfer capability (ATC) is very significant in electrical engineering. Nireekshana et al used CSO algorithm to regulate the position and control parameters of SVC and TCSC with the aim of maximizing power transfer transactions during normal and contingency cases [51].…”

Section: Electricalmentioning

confidence: 99%

Cat Swarm Optimization Algorithm: A Survey and Performance Evaluation

Ahmed

Rashid

Saeed

2020

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…Several methods based on RPF and CPF have been employed in literature for the determination of ATC . Ejebe et al formulated ATC problem based on CPF with adaptive localization which improved the speed in solving a large number of contingencies, giving a considerable reduction in computation time.…”

Section: Available Transfer Capability Determination Methodsmentioning

confidence: 99%

“…However, the techniques involve complex computations. Nireekshana et al proposed CPF and FACTS devices for the ATC determination and enhancement, respectively. The FACTS devices are used to maximize the power transfer during normal and contingency conditions.…”

Section: Available Transfer Capability Determination Methodsmentioning

confidence: 99%

Available transfer capability calculation methods: A comprehensive review

Mohammed

Mustafa

Mohammed

et al. 2019

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

Summary The ceaseless efforts by power system industries to promote sustainable and competitive electric power market structure in the deregulated environment have given rise to enormous research in the area of transfer capability of transmission networks. Due to high demand for electricity, transmission components are stressed to operate close to their operating limits, and this leads to a decrease in transmission efficiency. To address this issue, efficient evaluation of available transfer capability (ATC) is crucial for system planning, operation, and control. Several approaches have been proposed for ATC computation. Surprisingly, a comprehensive literature review on ATC computation is yet to be efficiently presented. Researchers have been able to come up with fast algorithms, but most of these algorithms are not accurate, and the presented accurate techniques are not fast enough for online applications. This paper presents a comprehensive review of the different approaches for ATC determination. It provides the concepts, methods, and the features of the ATC. For each technique, the state of the art of the several contributions made by researchers has been highlighted. This review reveals that there are issues regarding ATC calculation methods that need attention: the development of fast and accurate algorithm incorporating system dynamics and system uncertainties in ATC determination. Additionally, efforts on the incorporation of renewable energy generation in the ATC evaluation need to be intensified. This review will serve as one in all for researchers as well as a guide for the entrants in this field.

show abstract

“…Several authors have used Genetic algorithm and its variants for obtaining the optimal location of FACTS devices and have applied OPF technique for various objective functions [3][4][5]. Ya-Chin et al [6] implemented Particle Swarm optimization method for a multi-objective function using SVC to improve transmission system loading margin (LM) to a certain degree and reduce network expansion cost.…”

Section: Introductionmentioning

confidence: 99%

A Combinatory Index based Optimal Reallocation of Generators in the presence of SVC using Krill Herd Algorithm

2017

View full text Add to dashboard Cite

Abstract:In the new competitive electric world, it is compulsory for the electrical industry to make effective utilization of the available resources. Optimal tuning of generators and implementation of FACTS devices has been found to be very effective in this regard. In this paper, a combination strategy of optimal tuning of generators using Krill herd (KH) algorithm in the presence of Static VAR Compensator (SVC) has been proposed.A combinatory index (CI), which is a combination of Vi/Vo index and L-index, has been formulated and verified for obtaining the optimal location of SVC. A multi objective function has been formulated for tuning the generators. The results obtained after performing Optimal Power Flow on an IEEE 30 bus system for normal loading and for severe system conditions due to line outage in the presence of SVC using KH has been verified with that of GA, to prove the effectiveness of the chosen methodology.

show abstract

Available transfer capability enhancement with FACTS using Cat Swarm Optimization

Cited by 50 publications

References 12 publications

Cat Swarm Optimization Algorithm: A Survey and Performance Evaluation

Cat Swarm Optimization Algorithm: A Survey and Performance Evaluation

Available transfer capability calculation methods: A comprehensive review

A Combinatory Index based Optimal Reallocation of Generators in the presence of SVC using Krill Herd Algorithm

Contact Info

Product

Resources

About