Optimal PMU Placement using Binary Particle Swarm and Artificial Bee Colony with Channel Limitations and Redundancy
K. Saisree Reddy1, V. Vijaya Rama Raju2
1K. Saisree Reddy, Student, Department of EEE, Gokaraju Rangaraju Institute of Engineering and Technology, Bachupally, Hyderabad, India.
2V. Vijaya Rama Raju, Associate Professor, Department of EEE, Gokaraju Rangaraju Institute of Engineering and Technology, Bachupally, Hyderabad, India.
Manuscript received on 02 August 2019. | Revised Manuscript received on 08 August 2019. | Manuscript published on 30 September 2019. | PP: 3881-3886 | Volume-8 Issue-3 September 2019 | Retrieval Number: C5104098319/2019©BEIESP | DOI: 10.35940/ijrte.C5104.098319
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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: Phasor Measurement Unit (PMU) being expensive and to be placed optimally, a meta-heuristic approach of Binary particle swarm optimization (BPSO) and Binary artificial bee colony optimization (BABC) is made for the optimal allocation of PMU in a power system. The PMU locations resulted are served by basic system conditions like network configuration, critical generators, and loads. The pattern of locations on including Zero-Injection Buess (ZIB) is also discussed. The redundancy in case of PMU loss is coined so as to obtain a complete observability of the power system. the channel limitations of device is also taken into consideration for better results in real-time systems. Optimal PMU locations for IEEE 30-bus and 14-bus systems with channel limits are compared with all above considerations. The number of PMU locations is reduced as channel limits increases. The simulated PMU locations are decreased with improved observability by Binary Artificial Bee Colony Optimization as compared to Binary Particle Swarm Optimization.
Index Terms: Observability, Phasor Measurement Unit, Binary Artificial Bee Colony Optimization, Binary Particle Swarm Optimization.
Scope of the Article: Optimal Design of Structures