الفهرس 
LITERA TURE REVIEWOnly 14 pages are availabe for public view
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Abstract
Optimal power (low (Ol’F) is one of the main tools for optimal operation and
planning of modern power systems. As power systems get more complicated,
needs of optimal power flow development rise. One of these main needs is the
power (low flexibility, where the flexible AC transmission systems (FACTS)
devices play the main role in it. With the application of rACTS technology,
power systems become more flexibly controlled. The unified power flow
controller (UPfC) is the most comprehensive multivariable flexible ac
transmission system (FACTS) controller. It can provide simultaneous and
independent control of power system parameters such as line active power (low,
line reactive power (low, line impedances, line voltages, and phase angle.
In this thesis, the steady state model of the unified power now controller
(UPfC) using injection power model is incorporated in a MATLAB optimal
power flow program. Using this model of urrc, the effects of UPfC control
parameters on generation cost, system voltage profile, and system loadability are
studied and illustrated. This method has been investigated on many of test
systems and the results show that the objectives of minimizing any of the
generation cost, enhancing the system voltage profile, and enhancing the system
loadability are satisfied.
Two different optimization techniques have been implemented and compared to
each other. Namely, nonlinear programming (NLP) and Genetic Algorithm
(GA) have been evaluated in this study. The results show that both techniques
can be used to obtain optimum solution, while using rACTS devices, of the
Of’F problem.
Considering the objectives of generation cost, voltage profile, and system
loadability the results obtained demonstrate that GA is more effective and more
robust compared to NLP.
II
Abstract
It has been found that GA has many advantages over NLP technique 111 the
following points:
• GA has given better results in minimizing the problem objective
functions more than that ofNLP.
• To obtain better minimization results, GA has proven better
flexibility over NLP in a way that more optimal operating points are
obtained; therefore, other objectives can be satisfied.
• GA is faster in process than NLP and this has been noticed til
monitoring the convergence time.