Optimal Placement of Distributed Generation Units in Radial Distribution System using Hybrid Techniques
Banka Jyothsna Rani1, Ankireddipalli Srinivasula Reddy2

1Banka Jyothsna Rani*, Department of Technical Education, Government Model Residential Polytechnic, Madanapalle (Andhra Pradesh) India.
2Mr. Ankireddipalli Srinivasula Reddy, CMR Engineering College, Hyderabad (Telangana) India.
Manuscript received on November 25, 2019. | Revised Manuscript received on December 15, 2019. | Manuscript published on December 30, 2019. | PP: 1682-1688 | Volume-9 Issue-2, December, 2019. | Retrieval Number:  B3297129219/2019©BEIESP | DOI: 10.35940/ijeat.B3297.129219
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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: Reconfiguration is a process that supports to eliminate the power loss from a distribution network and this process have the capability to reduce the losses up to a specific point. Additionally, loss minimization may be calculated through the presentation of Distributed Generation (DG) units. Conversely, the incorporation of DG into the distribution network at an improper position may cause higher in losses and fluctuations in voltage. In the meantime, the uncertainty in voltage may produce partial power failure in the system. For that reason, it is essential to deliberate the stability boundaries in DGs position and sizing in the Radial Distribution System (RDS). In this research paper, hybrid Binary Particle Swarm Optimization (BPSO) with Flower Pollination Algorithm (FPA) is proposed for the ideal reconfiguration process and placing the DG in the 69-bus RDS. BPSO is applied to identify the best DG reconfiguration and FPA is proposed to determine the optimal DG size. This technique narrowly changes the DG location in every load bus of the network that delivers the minimum value of the objective function, which is considered as the finest candidate for DG connection. The simulation outcomes indicate the proposed method is more effective in reducing the power loss from 224.9804 to 27.2183 KW with the reduction of 88.8972% when compared to existing algorithm.
Keywords: Binary Particle Swarm Optimization (BPSO), Distributed Generation (DG), Flower Pollination Algorithm (FPA), Radial Distribution System (RDS), Reconfiguration.