Reactive power and voltage control based on general quantum genetic algorithms

Ioannis (John) Vlachogiannis, Jacob Østergaard

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

This paper presents an improved evolutionary algorithm based on quantum computing for optima l steady-state performance of power systems. However, the proposed general quantum genetic algorithm (GQ-GA) can be applied in various combinatorial optimization problems. In this study the GQ-GA determines the optimal settings of control variables, such as generator voltages, transformer taps and shunt VAR compensation devices for optimal reactive power and voltage control of IEEE 30-bus and 118-bus systems. The results of GQ-GA are compared with those given by the state-of-the-art evolutionary computational techniques such as enhanced GA, multi-objective evolutionary algorithm and particle swarm optimization algorithms, as well as the classical primal-dual interior-point optimal power flow algorithm. The comparison demonstrates the ability of the GQ-GA in reaching more optimal solutions.
Original languageEnglish
JournalExpert Systems with Applications
Volume36
Issue number3
Pages (from-to)6118-6126
ISSN0957-4174
DOIs
Publication statusPublished - 2009

Keywords

  • Genetic algorithm
  • Reactive power control
  • Quantum mechanics computation
  • Steady-state performance
  • Meta-heuristic techniques

Cite this

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title = "Reactive power and voltage control based on general quantum genetic algorithms",
abstract = "This paper presents an improved evolutionary algorithm based on quantum computing for optima l steady-state performance of power systems. However, the proposed general quantum genetic algorithm (GQ-GA) can be applied in various combinatorial optimization problems. In this study the GQ-GA determines the optimal settings of control variables, such as generator voltages, transformer taps and shunt VAR compensation devices for optimal reactive power and voltage control of IEEE 30-bus and 118-bus systems. The results of GQ-GA are compared with those given by the state-of-the-art evolutionary computational techniques such as enhanced GA, multi-objective evolutionary algorithm and particle swarm optimization algorithms, as well as the classical primal-dual interior-point optimal power flow algorithm. The comparison demonstrates the ability of the GQ-GA in reaching more optimal solutions.",
keywords = "Genetic algorithm, Reactive power control, Quantum mechanics computation, Steady-state performance, Meta-heuristic techniques",
author = "Vlachogiannis, {Ioannis (John)} and Jacob {\O}stergaard",
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Reactive power and voltage control based on general quantum genetic algorithms. / Vlachogiannis, Ioannis (John); Østergaard, Jacob.

In: Expert Systems with Applications, Vol. 36, No. 3, 2009, p. 6118-6126.

Research output: Contribution to journalJournal articleResearchpeer-review

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AU - Østergaard, Jacob

PY - 2009

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N2 - This paper presents an improved evolutionary algorithm based on quantum computing for optima l steady-state performance of power systems. However, the proposed general quantum genetic algorithm (GQ-GA) can be applied in various combinatorial optimization problems. In this study the GQ-GA determines the optimal settings of control variables, such as generator voltages, transformer taps and shunt VAR compensation devices for optimal reactive power and voltage control of IEEE 30-bus and 118-bus systems. The results of GQ-GA are compared with those given by the state-of-the-art evolutionary computational techniques such as enhanced GA, multi-objective evolutionary algorithm and particle swarm optimization algorithms, as well as the classical primal-dual interior-point optimal power flow algorithm. The comparison demonstrates the ability of the GQ-GA in reaching more optimal solutions.

AB - This paper presents an improved evolutionary algorithm based on quantum computing for optima l steady-state performance of power systems. However, the proposed general quantum genetic algorithm (GQ-GA) can be applied in various combinatorial optimization problems. In this study the GQ-GA determines the optimal settings of control variables, such as generator voltages, transformer taps and shunt VAR compensation devices for optimal reactive power and voltage control of IEEE 30-bus and 118-bus systems. The results of GQ-GA are compared with those given by the state-of-the-art evolutionary computational techniques such as enhanced GA, multi-objective evolutionary algorithm and particle swarm optimization algorithms, as well as the classical primal-dual interior-point optimal power flow algorithm. The comparison demonstrates the ability of the GQ-GA in reaching more optimal solutions.

KW - Genetic algorithm

KW - Reactive power control

KW - Quantum mechanics computation

KW - Steady-state performance

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