Determination of FACTS placement using differential evolution technique

Sookananta, Bongkoj

doi:10.1109/iceei.2009.5254730

Cited by 5 publications

(2 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Compensating reactive power; UPFC and SVC are regarded as a source in operating range of ±100MVAr. Also, TCSC is modeled as the variable impedance with working range (-0.7-+0.2) X L. Simulation results for simultaneous and non-simultaneous placement are as the Tables (1) to (3). The results show that by non-simultaneous placement of a UPFC, a TCSC and SVC, vulnerability of a system decreases from 3.4529 to 2.2255 (without FACTS devices installations).…”

Section: Numerical Studiesmentioning

confidence: 99%

The Optimization on FACTS Devices for Network Security

Yang¹,

Yang²

2013

Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)

View full text Add to dashboard Cite

One of the most important indices which are used in FACTS devices placement are indices related to network security and its vulnerability. On this basis, objective function consisting of vulnerability indices of generators, lines and bus bars presented and placement is done simultaneously and nonsimultaneously. For case studies, placement of a UPFC, a TCSC and a SVC in IEEE 30 bus test system are performed simultaneously and non-simultaneously. The results have shown simultaneous placement of these devices improve vulnerability indices more than non-simultaneous placement.

show abstract

Section: Numerical Studiesmentioning

confidence: 99%

The Optimization on FACTS Devices for Network Security

Yang¹,

Yang²

2013

Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)

View full text Add to dashboard Cite

show abstract

“…Many researchers have tested different optimization algorithms in the recent past. The best suited approach to such type of problems is to use Metaheuristics optimization algorithms such as Differential Evolution (DE) [15,16], Particle Swarm Optimization (PSO) [17,18], Genetic Algorithm (GA) [19][20][21][22][23][24], Evolutionary programming [25,26], Simulated Annealing (SA) and Tabu Search [27].…”

Section: Introductionmentioning

confidence: 99%

Performance Comparison of GA, DE, PSO and SA Approaches in Enhancement of Total Transfer Capability using FACTS Devices

Chandrasekar¹,

Ramana²

2012

Journal of Electrical Engineering and Technology

View full text Add to dashboard Cite

-In this paper the performance of meta-heuristics algorithms such as GA (Genetic Algorithm), DE (Differential Evolution), PSO (Particle Swarm Optimization) and SA (Simulated Annealing) for the problem of TTC enhancement using FACTS devices are compared. In addition to that in the assessment procedure of TTC two novel techniques are proposed. First the optimization algorithm which is used for TTC enhancement is simultaneously used for assessment of TTC. Second the power flow is done using Broyden -Shamanski method with Sherman -Morrison formula (BSS). The proposed approach is tested on WSCC 9 bus, IEEE 118 bus test systems and the results are compared with the conventional Repeated Power Flow (RPF) using Newton Raphson (NR) method which indicates that the proposed method provides better TTC enhancement and computational efficacy than the conventional procedure.

show abstract