Optimal Load Distribution of Thermal Generating Units using Particle Swarm Optimization (PSO)
Abhinav Saxena1, G.M Patil2, Prashant3, Parveen Poon Terang4, Nirmal Kumar Agarwal5, Arun Rawat6

1Abhinav Saxena, Ph.D Scholar, Department of Electrical Engineering, Jamia Millia Islamia, (New Delhi), India.
2G.M Patil, JSS Science and Technology University, Mysuru (Karnataka), India.
3Prashant, Ph.D Scholar, Department of Electrical Engineering, Jamia Millia Islamia, (New Delhi), India
4Parveen Poon Terang, Ph.D, Department of Electric Power Systems Management, Jamia Millia Islamia, (New Delhi), India.
5Nirmal Kumar Agarwal, Assistant Professor, Department of Electrical Engineering, JSSATE Noida (U.P), India.
6Arun Rawat, JSS Science and Technology University, Mysuru (Karnataka), India.
Manuscript received on 05 August 2019 | Revised Manuscript received on 28 August 2019 | Manuscript Published on 05 September 2019 | PP: 440-444 | Volume-8 Issue-2S7 July 2019 | Retrieval Number: B10810782S719/2019©BEIESP | DOI: 10.35940/ijrte.B1081.0782S719
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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 shows load planning of two thermal generating units feeding a load of 200 MW using Particle swarm optimization (PSO). The PSO involves selection of population size or number of particle, fitness function. The advantages of PSO over conventional method are better and reliable solution, better convergence rate. Initially fitness function, population size corresponding to each variable are decided, thereafter PSO is used for finding most optimal solution of the generating units under different set of iteration. The performance of system using PSO is compared with conventional method in terms of the tolerance band.
Keywords: PSO, Fitness, Population Size, Thermal.
Scope of the Article: Swarm Intelligence