Optimization methods for optimal power quality monitor placement in power systems: A performance comparison

Ahmad Asrul Ibrahim, Azah Mohamed, Hussain Shareef, Sakti Prasad Ghoshal

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

This paper presents a performance comparison between three optimization techniques, namely, quantum-inspired binary particle swarm optimization, binary particle swarm optimization and genetic algorithm in application to optimal power quality monitor (PQM) placement method for voltage sag assessment. The optimization handles the observability constraints based on the topological monitor reach area concept and solves a multi-objective function in obtaining the optimal number and placement of PQMs in power systems. The objective function consists of two functions which are based on monitor overlapping index and sag severity index. All the optimization algorithms have been implemented and tested on the IEEE 34-node, the 69-bus and the IEEE 118-bus test systems to evaluate the effectiveness of the aforementioned techniques. The results show that QBPSO provide a better optimal solution than the standard binary particle swarm optimization and the existing genetic algorithm by 56% and 31%, respectively. The validation test illustrated that the optimal PQM placements can detect and record the voltage sag events due to any fault occurrence in the systems.

Original languageEnglish
Pages (from-to)78-91
Number of pages14
JournalInternational Journal on Electrical Engineering and Informatics
Volume4
Issue number1
Publication statusPublished - 2012

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Power quality
Particle swarm optimization (PSO)
Genetic algorithms
Observability
Electric potential

Keywords

  • Binary particle swarm optimization
  • Genetic algorithm
  • Multi-objective function
  • Quantum-inspired binary particle swarm optimization
  • Topological monitor reach area

ASJC Scopus subject areas

  • Engineering(all)

Cite this

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title = "Optimization methods for optimal power quality monitor placement in power systems: A performance comparison",
abstract = "This paper presents a performance comparison between three optimization techniques, namely, quantum-inspired binary particle swarm optimization, binary particle swarm optimization and genetic algorithm in application to optimal power quality monitor (PQM) placement method for voltage sag assessment. The optimization handles the observability constraints based on the topological monitor reach area concept and solves a multi-objective function in obtaining the optimal number and placement of PQMs in power systems. The objective function consists of two functions which are based on monitor overlapping index and sag severity index. All the optimization algorithms have been implemented and tested on the IEEE 34-node, the 69-bus and the IEEE 118-bus test systems to evaluate the effectiveness of the aforementioned techniques. The results show that QBPSO provide a better optimal solution than the standard binary particle swarm optimization and the existing genetic algorithm by 56{\%} and 31{\%}, respectively. The validation test illustrated that the optimal PQM placements can detect and record the voltage sag events due to any fault occurrence in the systems.",
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