Performance Enhancement in Transmission Lines by Locating the Distributed Generators using Improved Particle Swarm Optimization Algorithm
S. Syed Musthafa Gani1, S. Boobalan2, S. Arun Prakash3, S. Faizal Mukthar Hussain4, A. MohamedIfthikar Alii5
1S. Syed Musthafa Gani*, Assistant Professor, Department of EEE, Mohamed Sathak Engineering College, Kilakarai
2S. Boobalan, Professor and Head, Department of EEE, Mohamed Sathak Engineering College, Kilakarai,.
3S. Arun Prakash, Assistant Professor, Department of EEE, University College of Engineering, Ramanathapuram.
4S. Faizal Mukthar Hussain, Assistant Professor, Department of CSE, Mohamed Sathak Engineering College, Kilakarai
5A. Mohamed Ifthikar Ali, Associate Professor, Department of ECE, Mohamed Sathak Engineering College, Kilakarai.
Manuscript received on March 15, 2020. | Revised Manuscript received on March 25, 2020. | Manuscript published on April 10, 2020. | PP: 1624-1628 | Volume-9 Issue-6, April 2020. | Retrieval Number: F4090049620/2020©BEIESP | DOI: 10.35940/ijitee.F4090.049620
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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: This paper indicates the nonlinear congestion mitigation in the transmission lines. The generator active power is rescheduled to achieve the congestion- transmission line network in electrical power system. The transmission congestion in the buses can be reconfigured by using the Improved Particle Swarm Optimization Algorithm. By locating the Distributed Generators in the identified weak buses the voltage profile and the power loss in the transmission system can be improved. The proposed solution’s achievability is tested by estimating the cost of congestion on different standard IEEE transmission line networks. The algorithm have the recompense such as the capability of local search and the capability of global search in the algorithms Gravitational Search Algorithm (GSA) and Particle Swarm Optimization (PSO) algorithm respectively. Consistence with the calculation is tried by considering different contextual investigations including clog on the separate test transport arrange because of two-sided, polygonal exchanges and line blackouts.
Keywords: Artificial Intelligence, Improved Particle Swarm Optimization (IPSO), Available Transfer Capability (ATC), Power System Planning, Open Access Same-Time Information System (OASTIS), Economic Dispatch (ED), Distributed Generators (DG).
Scope of the Article: Artificial Intelligence and Machine Learning