TITLE:
Firefly Algorithm in Determining Maximum Load Utilization Point and Its Enhancement through Optimal Placement of FACTS Device
AUTHORS:
S. Rajasekaran, Dr. S. Muralidharan
KEYWORDS:
FACTS, Maximum Loadability, Firefly Algorithm (FFA)
JOURNAL NAME:
Circuits and Systems,
Vol.7 No.10,
August
19,
2016
ABSTRACT: In a Power System, load is
the most uncertain and extremely time varying unit. Hence it is important to
determine the system’s supreme acceptable loadability limit called maximum
loadabilitypoint to accommodate
the sudden variation of load demand. Nowadays the enhancement of themaximum loadability point is essential
to meet the rapid growth of load demand by improvising the system’s load
utilization capacity. Flexible AC Transmission system devices (FACTS) with
their speed and flexibility will play a key role in enhancing the controllability
and power transfer capability of the system. Considering the theme of FACTS
devices in the loadability limit enhancement,in
this paper maximum loadability limit determination and itsenhancement are prepared with the help
of swarm intelligence based meta-heuristic Firefly Algorithm(FFA) by finding
the optimal loading factor for each load and optimally placing the SVC (Shunt
Compensation) and TCSC (SeriesCompensation)
FACTS devices in the system. To illuminate the effectiveness of FACTS devices
inthe loadability enhancement,
the line contingency scenario is also concernedin the study. The study of FACTS based
maximum system load utilization acceptability point determination is
demonstrated with the help of modified IEEE 30 bus, IEEE 57 Bus and IEEE 118
Bus test systems. The results of FACTS devices involvement in determining the
maximum loading point enhance the loadutilization
point in normal stateand also
help to overcome the system violation in transmissionline contingency state.
Also the firefly algorithm in determiningthe
maximum loadability point provides better search capability with faster
convergence rate compared to that of Particle swarm optimization (PSO) and
Differential evolution algorithm.