Implementation of Particle Swarm Optimization (PSO) Algorithm for Tuning of Power System Stabilizers in Multimachine Electric Power Systems

Humberto Verdejo, Victor Pino, Wolfgang Kliemann, Cristhian Becker and José Delpiano
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Humberto Verdejo: Department of Electrical Engineering, Universidad de Santiago de Chile, Santiago 9170124, Chile
Victor Pino: Department of Electrical Engineering, Universidad de Santiago de Chile, Santiago 9170124, Chile
Wolfgang Kliemann: Department of Mathematics, Iowa State University, Ames, IA 50011, USA
Cristhian Becker: Department of Electrical Engineering, Universidad de Santiago de Chile, Santiago 9170124, Chile
José Delpiano: School of Engineering and Applied Sciences, Universidad de los Andes, Santiago 7620001, Chile

Energies, 2020, vol. 13, issue 8, 1-29

Abstract: The application of artificial intelligence-based techniques has covered a wide range of applications related to electric power systems (EPS). Particularly, a metaheuristic technique known as Particle Swarm Optimization (PSO) has been chosen for the tuning of parameters for Power System Stabilizers (PSS) with success for relatively small systems. This article proposes a tuning methodology for PSSs based on the use of PSO that works for systems with ten or even more machines. Our new methodology was implemented using the source language of the commercial simulation software DigSilent PowerFactory. Therefore, it can be translated into current practice directly. Our methodology was applied to different test systems showing the effectiveness and potential of the proposed technique.

Keywords: power system; power system stabilizer; particle swarm optimization; multimachine system (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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