Optimal Location of multi-type FACTS for Power System Security Enhancement
N. Srilatha1, G. Yesuratnam2
1N. Srilatha, Dept. of Electrical Engineering, Univ. College of Engineering, Osmania University, Hyderabad, Telangana, India.
2G. Yesuratnam, Dept. of Electrical Engineering, Univ. College of Engineering, Osmania University, Hyderabad, Telangana, India.
Manuscript received on November 22, 2019. | Revised Manuscript received on December 30, 2019. | Manuscript published on December 30, 2019. | PP: 5345-5349 | Volume-9 Issue-2, December, 2019. | Retrieval Number: B5144129219/2019©BEIESP | DOI: 10.35940/ijeat.B5144.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Transmission congestion results from the contingencies in the power system and increasing load demand that has to be supplied through predetermined corridors in case of restructured environment. The Flexible AC Transmission Systems (FACTS) devices when deployed in a power system can result in improving the system performance in terms increased loading capability of transmission lines, reduction in losses, improved stability and security of the system by relieving stress on congested lines. This work deals with congestion management of the power transmission network by employing FACTS devices, with the help of Genetic Algorithm (GA) based optimization algorithm. Optimal location of FACTS placement and optimal parameter settings of these devices are the objectives for the optimization problem. The optimization process aims at maximizing the loading capability by the network by transferring power from overloaded lines to adjacent lightly loaded lines. FACTS devices considered are TCSC, SVC and UPFC for the alleviation of the overload on transmission lines and to reduce overall transmission loss of the system. An IEEE 30-bus system is used to illustrate the effectiveness of the proposed method.
Keywords: Congestion management, FACTS, Optimal Location, Genetic Algorithm.