Moth Flame Optimization based Reactive Power Planning
Manoj Kumar Kar1, Sanjay Kumar2, Arun Kumar Singh3
1Manoj Kumar Kar*, Electrical Engineering Department, National Institute of Technology Jamshedpur, Jharkhand, India.
2Sanjay Kumar, Electrical Engineering Department, National Institute of Technology Jamshedpur, Jharkhand, India.
3Arun Kumar Singh, Electrical Engineering Department, National Institute of Technology Jamshedpur, Jharkhand, India.
Manuscript received on December 13, 2019. | Revised Manuscript received on December 24, 2019. | Manuscript published on January 10, 2020. | PP: 1528-1533 | Volume-9 Issue-3, January 2020. | Retrieval Number: C8567019320/2020©BEIESP | DOI: 10.35940/ijitee.C8567.019320
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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: Reactive power planning is one of the challenges facing an integrated power network to operate efficiently. It requires optimal coordination of all the reactive power sources in the network. In this work, Moth flame optimization (MFO) based algorithm used for optimal location of flexible alternating current transmission system (FACTS) devices. In standard IEEE 30 and IEEE 57 test systems the proposed approach is examined. The static Var compensator (SVC) and thyristor controlled series capacitor (TCSC) are the two FACTS devices used. The load ability of the power system is enhanced by installing FACTS controllers considering both active and reactive loading. The reactive sources are placed optimally which is chosen by considering position and size of FACTS devices. The proposed method with FACTS devices is compared with other recent techniques like Particle swarm optimization (PSO) and gravitational search algorithm (GSA). It is observed that the MFO based approach is better as compared to other methods in terms of loss and the running cost at various loading conditions.
Keywords: Active Power loss, FACTS Control lerss, Moth Flame Optimization
Scope of the Article: Discrete Optimization